article writing on technology in education

How technology is reinventing education

Stanford Graduate School of Education Dean Dan Schwartz and other education scholars weigh in on what's next for some of the technology trends taking center stage in the classroom.

Image credit: Claire Scully

New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of Stanford Graduate School of Education (GSE), who is also a professor of educational technology at the GSE and faculty director of the Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately worried that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or coach students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the AI + Education initiative at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of CRAFT (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the Digital Learning initiative at the Stanford Accelerator for Learning, which runs a program exploring the use of virtual field trips to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Review Article
• Open access
• Published: 12 February 2024

Education reform and change driven by digital technology: a bibliometric study from a global perspective

• Chengliang Wang 1 ,
• Xiaojiao Chen 1 ,
• Teng Yu   ORCID: orcid.org/0000-0001-5198-7261 2 , 3 ,
• Yidan Liu 1 , 4 &
• Yuhui Jing 1  

Humanities and Social Sciences Communications volume  11 , Article number:  256 ( 2024 ) Cite this article

5247 Accesses

2 Citations

1 Altmetric

Metrics details

• Development studies
• Science, technology and society

Amidst the global digital transformation of educational institutions, digital technology has emerged as a significant area of interest among scholars. Such technologies have played an instrumental role in enhancing learner performance and improving the effectiveness of teaching and learning. These digital technologies also ensure the sustainability and stability of education during the epidemic. Despite this, a dearth of systematic reviews exists regarding the current state of digital technology application in education. To address this gap, this study utilized the Web of Science Core Collection as a data source (specifically selecting the high-quality SSCI and SCIE) and implemented a topic search by setting keywords, yielding 1849 initial publications. Furthermore, following the PRISMA guidelines, we refined the selection to 588 high-quality articles. Using software tools such as CiteSpace, VOSviewer, and Charticulator, we reviewed these 588 publications to identify core authors (such as Selwyn, Henderson, Edwards), highly productive countries/regions (England, Australia, USA), key institutions (Monash University, Australian Catholic University), and crucial journals in the field ( Education and Information Technologies , Computers & Education , British Journal of Educational Technology ). Evolutionary analysis reveals four developmental periods in the research field of digital technology education application: the embryonic period, the preliminary development period, the key exploration, and the acceleration period of change. The study highlights the dual influence of technological factors and historical context on the research topic. Technology is a key factor in enabling education to transform and upgrade, and the context of the times is an important driving force in promoting the adoption of new technologies in the education system and the transformation and upgrading of education. Additionally, the study identifies three frontier hotspots in the field: physical education, digital transformation, and professional development under the promotion of digital technology. This study presents a clear framework for digital technology application in education, which can serve as a valuable reference for researchers and educational practitioners concerned with digital technology education application in theory and practice.

Similar content being viewed by others

A bibliometric analysis of knowledge mapping in Chinese education digitalization research from 2012 to 2022

Digital transformation and digital literacy in the context of complexity within higher education institutions: a systematic literature review

Education big data and learning analytics: a bibliometric analysis

Introduction.

Digital technology has become an essential component of modern education, facilitating the extension of temporal and spatial boundaries and enriching the pedagogical contexts (Selwyn and Facer, 2014 ). The advent of mobile communication technology has enabled learning through social media platforms (Szeto et al. 2015 ; Pires et al. 2022 ), while the advancement of augmented reality technology has disrupted traditional conceptions of learning environments and spaces (Perez-Sanagustin et al., 2014 ; Kyza and Georgiou, 2018 ). A wide range of digital technologies has enabled learning to become a norm in various settings, including the workplace (Sjöberg and Holmgren, 2021 ), home (Nazare et al. 2022 ), and online communities (Tang and Lam, 2014 ). Education is no longer limited to fixed locations and schedules, but has permeated all aspects of life, allowing learning to continue at any time and any place (Camilleri and Camilleri, 2016 ; Selwyn and Facer, 2014 ).

The advent of digital technology has led to the creation of several informal learning environments (Greenhow and Lewin, 2015 ) that exhibit divergent form, function, features, and patterns in comparison to conventional learning environments (Nygren et al. 2019 ). Consequently, the associated teaching and learning processes, as well as the strategies for the creation, dissemination, and acquisition of learning resources, have undergone a complete overhaul. The ensuing transformations have posed a myriad of novel issues, such as the optimal structuring of teaching methods by instructors and the adoption of appropriate learning strategies by students in the new digital technology environment. Consequently, an examination of the principles that underpin effective teaching and learning in this environment is a topic of significant interest to numerous scholars engaged in digital technology education research.

Over the course of the last two decades, digital technology has made significant strides in the field of education, notably in extending education time and space and creating novel educational contexts with sustainability. Despite research attempts to consolidate the application of digital technology in education, previous studies have only focused on specific aspects of digital technology, such as Pinto and Leite’s ( 2020 ) investigation into digital technology in higher education and Mustapha et al.’s ( 2021 ) examination of the role and value of digital technology in education during the pandemic. While these studies have provided valuable insights into the practical applications of digital technology in particular educational domains, they have not comprehensively explored the macro-mechanisms and internal logic of digital technology implementation in education. Additionally, these studies were conducted over a relatively brief period, making it challenging to gain a comprehensive understanding of the macro-dynamics and evolutionary process of digital technology in education. Some studies have provided an overview of digital education from an educational perspective but lack a precise understanding of technological advancement and change (Yang et al. 2022 ). Therefore, this study seeks to employ a systematic scientific approach to collate relevant research from 2000 to 2022, comprehend the internal logic and development trends of digital technology in education, and grasp the outstanding contribution of digital technology in promoting the sustainability of education in time and space. In summary, this study aims to address the following questions:

RQ1: Since the turn of the century, what is the productivity distribution of the field of digital technology education application research in terms of authorship, country/region, institutional and journal level?

RQ2: What is the development trend of research on the application of digital technology in education in the past two decades?

RQ3: What are the current frontiers of research on the application of digital technology in education?

Literature review

Although the term “digital technology” has become ubiquitous, a unified definition has yet to be agreed upon by scholars. Because the meaning of the word digital technology is closely related to the specific context. Within the educational research domain, Selwyn’s ( 2016 ) definition is widely favored by scholars (Pinto and Leite, 2020 ). Selwyn ( 2016 ) provides a comprehensive view of various concrete digital technologies and their applications in education through ten specific cases, such as immediate feedback in classes, orchestrating teaching, and community learning. Through these specific application scenarios, Selwyn ( 2016 ) argues that digital technology encompasses technologies associated with digital devices, including but not limited to tablets, smartphones, computers, and social media platforms (such as Facebook and YouTube). Furthermore, Further, the behavior of accessing the internet at any location through portable devices can be taken as an extension of the behavior of applying digital technology.

The evolving nature of digital technology has significant implications in the field of education. In the 1890s, the focus of digital technology in education was on comprehending the nuances of digital space, digital culture, and educational methodologies, with its connotations aligned more towards the idea of e-learning. The advent and subsequent widespread usage of mobile devices since the dawn of the new millennium have been instrumental in the rapid expansion of the concept of digital technology. Notably, mobile learning devices such as smartphones and tablets, along with social media platforms, have become integral components of digital technology (Conole and Alevizou, 2010 ; Batista et al. 2016 ). In recent times, the burgeoning application of AI technology in the education sector has played a vital role in enriching the digital technology lexicon (Banerjee et al. 2021 ). ChatGPT, for instance, is identified as a novel educational technology that has immense potential to revolutionize future education (Rospigliosi, 2023 ; Arif, Munaf and Ul-Haque, 2023 ).

Pinto and Leite ( 2020 ) conducted a comprehensive macroscopic survey of the use of digital technologies in the education sector and identified three distinct categories, namely technologies for assessment and feedback, mobile technologies, and Information Communication Technologies (ICT). This classification criterion is both macroscopic and highly condensed. In light of the established concept definitions of digital technology in the educational research literature, this study has adopted the characterizations of digital technology proposed by Selwyn ( 2016 ) and Pinto and Leite ( 2020 ) as crucial criteria for analysis and research inclusion. Specifically, this criterion encompasses several distinct types of digital technologies, including Information and Communication Technologies (ICT), Mobile tools, eXtended Reality (XR) Technologies, Assessment and Feedback systems, Learning Management Systems (LMS), Publish and Share tools, Collaborative systems, Social media, Interpersonal Communication tools, and Content Aggregation tools.

Methodology and materials

Research method: bibliometric.

The research on econometric properties has been present in various aspects of human production and life, yet systematic scientific theoretical guidance has been lacking, resulting in disorganization. In 1969, British scholar Pritchard ( 1969 ) proposed “bibliometrics,” which subsequently emerged as an independent discipline in scientific quantification research. Initially, Pritchard defined bibliometrics as “the application of mathematical and statistical methods to books and other media of communication,” however, the definition was not entirely rigorous. To remedy this, Hawkins ( 2001 ) expanded Pritchard’s definition to “the quantitative analysis of the bibliographic features of a body of literature.” De Bellis further clarified the objectives of bibliometrics, stating that it aims to analyze and identify patterns in literature, such as the most productive authors, institutions, countries, and journals in scientific disciplines, trends in literary production over time, and collaboration networks (De Bellis, 2009 ). According to Garfield ( 2006 ), bibliometric research enables the examination of the history and structure of a field, the flow of information within the field, the impact of journals, and the citation status of publications over a longer time scale. All of these definitions illustrate the unique role of bibliometrics as a research method for evaluating specific research fields.

This study uses CiteSpace, VOSviewer, and Charticulator to analyze data and create visualizations. Each of these three tools has its own strengths and can complement each other. CiteSpace and VOSviewer use set theory and probability theory to provide various visualization views in fields such as keywords, co-occurrence, and co-authors. They are easy to use and produce visually appealing graphics (Chen, 2006 ; van Eck and Waltman, 2009 ) and are currently the two most widely used bibliometric tools in the field of visualization (Pan et al. 2018 ). In this study, VOSviewer provided the data necessary for the Performance Analysis; Charticulator was then used to redraw using the tabular data exported from VOSviewer (for creating the chord diagram of country collaboration); this was to complement the mapping process, while CiteSpace was primarily utilized to generate keyword maps and conduct burst word analysis.

Data retrieval

This study selected documents from the Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) in the Web of Science Core Collection as the data source, for the following reasons:

(1) The Web of Science Core Collection, as a high-quality digital literature resource database, has been widely accepted by many researchers and is currently considered the most suitable database for bibliometric analysis (Jing et al. 2023a ). Compared to other databases, Web of Science provides more comprehensive data information (Chen et al. 2022a ), and also provides data formats suitable for analysis using VOSviewer and CiteSpace (Gaviria-Marin et al. 2019 ).

(2) The application of digital technology in the field of education is an interdisciplinary research topic, involving technical knowledge literature belonging to the natural sciences and education-related literature belonging to the social sciences. Therefore, it is necessary to select Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) as the sources of research data, ensuring the comprehensiveness of data while ensuring the reliability and persuasiveness of bibliometric research (Hwang and Tsai, 2011 ; Wang et al. 2022 ).

After establishing the source of research data, it is necessary to determine a retrieval strategy (Jing et al. 2023b ). The choice of a retrieval strategy should consider a balance between the breadth and precision of the search formula. That is to say, it should encompass all the literature pertaining to the research topic while excluding irrelevant documents as much as possible. In light of this, this study has set a retrieval strategy informed by multiple related papers (Mustapha et al. 2021 ; Luo et al. 2021 ). The research by Mustapha et al. ( 2021 ) guided us in selecting keywords (“digital” AND “technolog*”) to target digital technology, while Luo et al. ( 2021 ) informed the selection of terms (such as “instruct*,” “teach*,” and “education”) to establish links with the field of education. Then, based on the current application of digital technology in the educational domain and the scope of selection criteria, we constructed the final retrieval strategy. Following the general patterns of past research (Jing et al. 2023a , 2023b ), we conducted a specific screening using the topic search (Topics, TS) function in Web of Science. For the specific criteria used in the screening for this study, please refer to Table 1 .

Literature screening

Literature acquired through keyword searches may contain ostensibly related yet actually unrelated works. Therefore, to ensure the close relevance of literature included in the analysis to the research topic, it is often necessary to perform a manual screening process to identify the final literature to be analyzed, subsequent to completing the initial literature search.

The manual screening process consists of two steps. Initially, irrelevant literature is weeded out based on the title and abstract, with two members of the research team involved in this phase. This stage lasted about one week, resulting in 1106 articles being retained. Subsequently, a comprehensive review of the full text is conducted to accurately identify the literature required for the study. To carry out the second phase of manual screening effectively and scientifically, and to minimize the potential for researcher bias, the research team established the inclusion criteria presented in Table 2 . Three members were engaged in this phase, which took approximately 2 weeks, culminating in the retention of 588 articles after meticulous screening. The entire screening process is depicted in Fig. 1 , adhering to the PRISMA guidelines (Page et al. 2021 ).

The process of obtaining and filtering the necessary literature data for research.

Data standardization

Nguyen and Hallinger ( 2020 ) pointed out that raw data extracted from scientific databases often contains multiple expressions of the same term, and not addressing these synonymous expressions could affect research results in bibliometric analysis. For instance, in the original data, the author list may include “Tsai, C. C.” and “Tsai, C.-C.”, while the keyword list may include “professional-development” and “professional development,” which often require merging. Therefore, before analyzing the selected literature, a data disambiguation process is necessary to standardize the data (Strotmann and Zhao, 2012 ; Van Eck and Waltman, 2019 ). This study adopted the data standardization process proposed by Taskin and Al ( 2019 ), mainly including the following standardization operations:

Firstly, the author and source fields in the data are corrected and standardized to differentiate authors with similar names.

Secondly, the study checks whether the journals to which the literature belongs have been renamed in the past over 20 years, so as to avoid the influence of periodical name change on the analysis results.

Finally, the keyword field is standardized by unifying parts of speech and singular/plural forms of keywords, which can help eliminate redundant entries in the knowledge graph.

Performance analysis (RQ1)

This section offers a thorough and detailed analysis of the state of research in the field of digital technology education. By utilizing descriptive statistics and visual maps, it provides a comprehensive overview of the development trends, authors, countries, institutions, and journal distribution within the field. The insights presented in this section are of great significance in advancing our understanding of the current state of research in this field and identifying areas for further investigation. The use of visual aids to display inter-country cooperation and the evolution of the field adds to the clarity and coherence of the analysis.

Time trend of the publications

To understand a research field, it is first necessary to understand the most basic quantitative information, among which the change in the number of publications per year best reflects the development trend of a research field. Figure 2 shows the distribution of publication dates.

Time trend of the publications on application of digital technology in education.

From the Fig. 2 , it can be seen that the development of this field over the past over 20 years can be roughly divided into three stages. The first stage was from 2000 to 2007, during which the number of publications was relatively low. Due to various factors such as technological maturity, the academic community did not pay widespread attention to the role of digital technology in expanding the scope of teaching and learning. The second stage was from 2008 to 2019, during which the overall number of publications showed an upward trend, and the development of the field entered an accelerated period, attracting more and more scholars’ attention. The third stage was from 2020 to 2022, during which the number of publications stabilized at around 100. During this period, the impact of the pandemic led to a large number of scholars focusing on the role of digital technology in education during the pandemic, and research on the application of digital technology in education became a core topic in social science research.

Analysis of authors

An analysis of the author’s publication volume provides information about the representative scholars and core research strengths of a research area. Table 3 presents information on the core authors in adaptive learning research, including name, publication number, and average number of citations per article (based on the analysis and statistics from VOSviewer).

Variations in research foci among scholars abound. Within the field of digital technology education application research over the past two decades, Neil Selwyn stands as the most productive author, having published 15 papers garnering a total of 1027 citations, resulting in an average of 68.47 citations per paper. As a Professor at the Faculty of Education at Monash University, Selwyn concentrates on exploring the application of digital technology in higher education contexts (Selwyn et al. 2021 ), as well as related products in higher education such as Coursera, edX, and Udacity MOOC platforms (Bulfin et al. 2014 ). Selwyn’s contributions to the educational sociology perspective include extensive research on the impact of digital technology on education, highlighting the spatiotemporal extension of educational processes and practices through technological means as the greatest value of educational technology (Selwyn, 2012 ; Selwyn and Facer, 2014 ). In addition, he provides a blueprint for the development of future schools in 2030 based on the present impact of digital technology on education (Selwyn et al. 2019 ). The second most productive author in this field, Henderson, also offers significant contributions to the understanding of the important value of digital technology in education, specifically in the higher education setting, with a focus on the impact of the pandemic (Henderson et al. 2015 ; Cohen et al. 2022 ). In contrast, Edwards’ research interests focus on early childhood education, particularly the application of digital technology in this context (Edwards, 2013 ; Bird and Edwards, 2015 ). Additionally, on the technical level, Edwards also mainly prefers digital game technology, because it is a digital technology that children are relatively easy to accept (Edwards, 2015 ).

Analysis of countries/regions and organization

The present study aimed to ascertain the leading countries in digital technology education application research by analyzing 75 countries related to 558 works of literature. Table 4 depicts the top ten countries that have contributed significantly to this field in terms of publication count (based on the analysis and statistics from VOSviewer). Our analysis of Table 4 data shows that England emerged as the most influential country/region, with 92 published papers and 2401 citations. Australia and the United States secured the second and third ranks, respectively, with 90 papers (2187 citations) and 70 papers (1331 citations) published. Geographically, most of the countries featured in the top ten publication volumes are situated in Australia, North America, and Europe, with China being the only exception. Notably, all these countries, except China, belong to the group of developed nations, suggesting that economic strength is a prerequisite for fostering research in the digital technology education application field.

This study presents a visual representation of the publication output and cooperation relationships among different countries in the field of digital technology education application research. Specifically, a chord diagram is employed to display the top 30 countries in terms of publication output, as depicted in Fig. 3 . The chord diagram is composed of nodes and chords, where the nodes are positioned as scattered points along the circumference, and the length of each node corresponds to the publication output, with longer lengths indicating higher publication output. The chords, on the other hand, represent the cooperation relationships between any two countries, and are weighted based on the degree of closeness of the cooperation, with wider chords indicating closer cooperation. Through the analysis of the cooperation relationships, the findings suggest that the main publishing countries in this field are engaged in cooperative relationships with each other, indicating a relatively high level of international academic exchange and research internationalization.

In the diagram, nodes are scattered along the circumference of a circle, with the length of each node representing the volume of publications. The weighted arcs connecting any two points on the circle are known as chords, representing the collaborative relationship between the two, with the width of the arc indicating the closeness of the collaboration.

Further analyzing Fig. 3 , we can extract more valuable information, enabling a deeper understanding of the connections between countries in the research field of digital technology in educational applications. It is evident that certain countries, such as the United States, China, and England, display thicker connections, indicating robust collaborative relationships in terms of productivity. These thicker lines signify substantial mutual contributions and shared objectives in certain sectors or fields, highlighting the interconnectedness and global integration in these areas. By delving deeper, we can also explore potential future collaboration opportunities through the chord diagram, identifying possible partners to propel research and development in this field. In essence, the chord diagram successfully encapsulates and conveys the multi-dimensionality of global productivity and cooperation, allowing for a comprehensive understanding of the intricate inter-country relationships and networks in a global context, providing valuable guidance and insights for future research and collaborations.

An in-depth examination of the publishing institutions is provided in Table 5 , showcasing the foremost 10 institutions ranked by their publication volume. Notably, Monash University and Australian Catholic University, situated in Australia, have recorded the most prolific publications within the digital technology education application realm, with 22 and 10 publications respectively. Moreover, the University of Oslo from Norway is featured among the top 10 publishing institutions, with an impressive average citation count of 64 per publication. It is worth highlighting that six institutions based in the United Kingdom were also ranked within the top 10 publishing institutions, signifying their leading position in this area of research.

Analysis of journals

Journals are the main carriers for publishing high-quality papers. Some scholars point out that the two key factors to measure the influence of journals in the specified field are the number of articles published and the number of citations. The more papers published in a magazine and the more citations, the greater its influence (Dzikowski, 2018 ). Therefore, this study utilized VOSviewer to statistically analyze the top 10 journals with the most publications in the field of digital technology in education and calculated the average citations per article (see Table 6 ).

Based on Table 6 , it is apparent that the highest number of articles in the domain of digital technology in education research were published in Education and Information Technologies (47 articles), Computers & Education (34 articles), and British Journal of Educational Technology (32 articles), indicating a higher article output compared to other journals. This underscores the fact that these three journals concentrate more on the application of digital technology in education. Furthermore, several other journals, such as Technology Pedagogy and Education and Sustainability, have published more than 15 articles in this domain. Sustainability represents the open access movement, which has notably facilitated research progress in this field, indicating that the development of open access journals in recent years has had a significant impact. Although there is still considerable disagreement among scholars on the optimal approach to achieve open access, the notion that research outcomes should be accessible to all is widely recognized (Huang et al. 2020 ). On further analysis of the research fields to which these journals belong, except for Sustainability, it is evident that they all pertain to educational technology, thus providing a qualitative definition of the research area of digital technology education from the perspective of journals.

Temporal keyword analysis: thematic evolution (RQ2)

The evolution of research themes is a dynamic process, and previous studies have attempted to present the developmental trajectory of fields by drawing keyword networks in phases (Kumar et al. 2021 ; Chen et al. 2022b ). To understand the shifts in research topics across different periods, this study follows past research and, based on the significant changes in the research field and corresponding technological advancements during the outlined periods, divides the timeline into four stages (the first stage from January 2000 to December 2005, the second stage from January 2006 to December 2011, the third stage from January 2012 to December 2017; and the fourth stage from January 2018 to December 2022). The division into these four stages was determined through a combination of bibliometric analysis and literature review, which presented a clear trajectory of the field’s development. The research analyzes the keyword networks for each time period (as there are only three articles in the first stage, it was not possible to generate an appropriate keyword co-occurrence map, hence only the keyword co-occurrence maps from the second to the fourth stages are provided), to understand the evolutionary track of the digital technology education application research field over time.

2000.1–2005.12: germination period

From January 2000 to December 2005, digital technology education application research was in its infancy. Only three studies focused on digital technology, all of which were related to computers. Due to the popularity of computers, the home became a new learning environment, highlighting the important role of digital technology in expanding the scope of learning spaces (Sutherland et al. 2000 ). In specific disciplines and contexts, digital technology was first favored in medical clinical practice, becoming an important tool for supporting the learning of clinical knowledge and practice (Tegtmeyer et al. 2001 ; Durfee et al. 2003 ).

2006.1–2011.12: initial development period

Between January 2006 and December 2011, it was the initial development period of digital technology education research. Significant growth was observed in research related to digital technology, and discussions and theoretical analyses about “digital natives” emerged. During this phase, scholars focused on the debate about “how to use digital technology reasonably” and “whether current educational models and school curriculum design need to be adjusted on a large scale” (Bennett and Maton, 2010 ; Selwyn, 2009 ; Margaryan et al. 2011 ). These theoretical and speculative arguments provided a unique perspective on the impact of cognitive digital technology on education and teaching. As can be seen from the vocabulary such as “rethinking”, “disruptive pedagogy”, and “attitude” in Fig. 4 , many scholars joined the calm reflection and analysis under the trend of digital technology (Laurillard, 2008 ; Vratulis et al. 2011 ). During this phase, technology was still undergoing dramatic changes. The development of mobile technology had already caught the attention of many scholars (Wong et al. 2011 ), but digital technology represented by computers was still very active (Selwyn et al. 2011 ). The change in technological form would inevitably lead to educational transformation. Collins and Halverson ( 2010 ) summarized the prospects and challenges of using digital technology for learning and educational practices, believing that digital technology would bring a disruptive revolution to the education field and bring about a new educational system. In addition, the term “teacher education” in Fig. 4 reflects the impact of digital technology development on teachers. The rapid development of technology has widened the generation gap between teachers and students. To ensure smooth communication between teachers and students, teachers must keep up with the trend of technological development and establish a lifelong learning concept (Donnison, 2009 ).

In the diagram, each node represents a keyword, with the size of the node indicating the frequency of occurrence of the keyword. The connections represent the co-occurrence relationships between keywords, with a higher frequency of co-occurrence resulting in tighter connections.

2012.1–2017.12: critical exploration period

During the period spanning January 2012 to December 2017, the application of digital technology in education research underwent a significant exploration phase. As can be seen from Fig. 5 , different from the previous stage, the specific elements of specific digital technology have started to increase significantly, including the enrichment of technological contexts, the greater variety of research methods, and the diversification of learning modes. Moreover, the temporal and spatial dimensions of the learning environment were further de-emphasized, as noted in previous literature (Za et al. 2014 ). Given the rapidly accelerating pace of technological development, the education system in the digital era is in urgent need of collaborative evolution and reconstruction, as argued by Davis, Eickelmann, and Zaka ( 2013 ).

In the domain of digital technology, social media has garnered substantial scholarly attention as a promising avenue for learning, as noted by Pasquini and Evangelopoulos ( 2016 ). The implementation of social media in education presents several benefits, including the liberation of education from the restrictions of physical distance and time, as well as the erasure of conventional educational boundaries. The user-generated content (UGC) model in social media has emerged as a crucial source for knowledge creation and distribution, with the widespread adoption of mobile devices. Moreover, social networks have become an integral component of ubiquitous learning environments (Hwang et al. 2013 ). The utilization of social media allows individuals to function as both knowledge producers and recipients, which leads to a blurring of the conventional roles of learners and teachers. On mobile platforms, the roles of learners and teachers are not fixed, but instead interchangeable.

In terms of research methodology, the prevalence of empirical studies with survey designs in the field of educational technology during this period is evident from the vocabulary used, such as “achievement,” “acceptance,” “attitude,” and “ict.” in Fig. 5 . These studies aim to understand learners’ willingness to adopt and attitudes towards new technologies, and some seek to investigate the impact of digital technologies on learning outcomes through quasi-experimental designs (Domínguez et al. 2013 ). Among these empirical studies, mobile learning emerged as a hot topic, and this is not surprising. First, the advantages of mobile learning environments over traditional ones have been empirically demonstrated (Hwang et al. 2013 ). Second, learners born around the turn of the century have been heavily influenced by digital technologies and have developed their own learning styles that are more open to mobile devices as a means of learning. Consequently, analyzing mobile learning as a relatively novel mode of learning has become an important issue for scholars in the field of educational technology.

The intervention of technology has led to the emergence of several novel learning modes, with the blended learning model being the most representative one in the current phase. Blended learning, a novel concept introduced in the information age, emphasizes the integration of the benefits of traditional learning methods and online learning. This learning mode not only highlights the prominent role of teachers in guiding, inspiring, and monitoring the learning process but also underlines the importance of learners’ initiative, enthusiasm, and creativity in the learning process. Despite being an early conceptualization, blended learning’s meaning has been expanded by the widespread use of mobile technology and social media in education. The implementation of new technologies, particularly mobile devices, has resulted in the transformation of curriculum design and increased flexibility and autonomy in students’ learning processes (Trujillo Maza et al. 2016 ), rekindling scholarly attention to this learning mode. However, some scholars have raised concerns about the potential drawbacks of the blended learning model, such as its significant impact on the traditional teaching system, the lack of systematic coping strategies and relevant policies in several schools and regions (Moskal et al. 2013 ).

2018.1–2022.12: accelerated transformation period

The period spanning from January 2018 to December 2022 witnessed a rapid transformation in the application of digital technology in education research. The field of digital technology education research reached a peak period of publication, largely influenced by factors such as the COVID-19 pandemic (Yu et al. 2023 ). Research during this period was built upon the achievements, attitudes, and social media of the previous phase, and included more elements that reflect the characteristics of this research field, such as digital literacy, digital competence, and professional development, as depicted in Fig. 6 . Alongside this, scholars’ expectations for the value of digital technology have expanded, and the pursuit of improving learning efficiency and performance is no longer the sole focus. Some research now aims to cultivate learners’ motivation and enhance their self-efficacy by applying digital technology in a reasonable manner, as demonstrated by recent studies (Beardsley et al. 2021 ; Creely et al. 2021 ).

The COVID-19 pandemic has emerged as a crucial backdrop for the digital technology’s role in sustaining global education, as highlighted by recent scholarly research (Zhou et al. 2022 ; Pan and Zhang, 2020 ; Mo et al. 2022 ). The online learning environment, which is supported by digital technology, has become the primary battleground for global education (Yu, 2022 ). This social context has led to various studies being conducted, with some scholars positing that the pandemic has impacted the traditional teaching order while also expanding learning possibilities in terms of patterns and forms (Alabdulaziz, 2021 ). Furthermore, the pandemic has acted as a catalyst for teacher teaching and technological innovation, and this viewpoint has been empirically substantiated (Moorhouse and Wong, 2021 ). Additionally, some scholars believe that the pandemic’s push is a crucial driving force for the digital transformation of the education system, serving as an essential mechanism for overcoming the system’s inertia (Romero et al. 2021 ).

The rapid outbreak of the pandemic posed a challenge to the large-scale implementation of digital technologies, which was influenced by a complex interplay of subjective and objective factors. Objective constraints included the lack of infrastructure in some regions to support digital technologies, while subjective obstacles included psychological resistance among certain students and teachers (Moorhouse, 2021 ). These factors greatly impacted the progress of online learning during the pandemic. Additionally, Timotheou et al. ( 2023 ) conducted a comprehensive systematic review of existing research on digital technology use during the pandemic, highlighting the critical role played by various factors such as learners’ and teachers’ digital skills, teachers’ personal attributes and professional development, school leadership and management, and administration in facilitating the digitalization and transformation of schools.

The current stage of research is characterized by the pivotal term “digital literacy,” denoting a growing interest in learners’ attitudes and adoption of emerging technologies. Initially, the term “literacy” was restricted to fundamental abilities and knowledge associated with books and print materials (McMillan, 1996 ). However, with the swift advancement of computers and digital technology, there have been various attempts to broaden the scope of literacy beyond its traditional meaning, including game literacy (Buckingham and Burn, 2007 ), information literacy (Eisenberg, 2008 ), and media literacy (Turin and Friesem, 2020 ). Similarly, digital literacy has emerged as a crucial concept, and Gilster and Glister ( 1997 ) were the first to introduce this concept, referring to the proficiency in utilizing technology and processing digital information in academic, professional, and daily life settings. In practical educational settings, learners who possess higher digital literacy often exhibit an aptitude for quickly mastering digital devices and applying them intelligently to education and teaching (Yu, 2022 ).

The utilization of digital technology in education has undergone significant changes over the past two decades, and has been a crucial driver of educational reform with each new technological revolution. The impact of these changes on the underlying logic of digital technology education applications has been noticeable. From computer technology to more recent developments such as virtual reality (VR), augmented reality (AR), and artificial intelligence (AI), the acceleration in digital technology development has been ongoing. Educational reforms spurred by digital technology development continue to be dynamic, as each new digital innovation presents new possibilities and models for teaching practice. This is especially relevant in the post-pandemic era, where the importance of technological progress in supporting teaching cannot be overstated (Mughal et al. 2022 ). Existing digital technologies have already greatly expanded the dimensions of education in both time and space, while future digital technologies aim to expand learners’ perceptions. Researchers have highlighted the potential of integrated technology and immersive technology in the development of the educational metaverse, which is highly anticipated to create a new dimension for the teaching and learning environment, foster a new value system for the discipline of educational technology, and more effectively and efficiently achieve the grand educational blueprint of the United Nations’ Sustainable Development Goals (Zhang et al. 2022 ; Li and Yu, 2023 ).

Hotspot evolution analysis (RQ3)

The examination of keyword evolution reveals a consistent trend in the advancement of digital technology education application research. The emergence and transformation of keywords serve as indicators of the varying research interests in this field. Thus, the utilization of the burst detection function available in CiteSpace allowed for the identification of the top 10 burst words that exhibited a high level of burst strength. This outcome is illustrated in Table 7 .

According to the results presented in Table 7 , the explosive terminology within the realm of digital technology education research has exhibited a concentration mainly between the years 2018 and 2022. Prior to this time frame, the emerging keywords were limited to “information technology” and “computer”. Notably, among them, computer, as an emergent keyword, has always had a high explosive intensity from 2008 to 2018, which reflects the important position of computer in digital technology and is the main carrier of many digital technologies such as Learning Management Systems (LMS) and Assessment and Feedback systems (Barlovits et al. 2022 ).

Since 2018, an increasing number of research studies have focused on evaluating the capabilities of learners to accept, apply, and comprehend digital technologies. As indicated by the use of terms such as “digital literacy” and “digital skill,” the assessment of learners’ digital literacy has become a critical task. Scholarly efforts have been directed towards the development of literacy assessment tools and the implementation of empirical assessments. Furthermore, enhancing the digital literacy of both learners and educators has garnered significant attention. (Nagle, 2018 ; Yu, 2022 ). Simultaneously, given the widespread use of various digital technologies in different formal and informal learning settings, promoting learners’ digital skills has become a crucial objective for contemporary schools (Nygren et al. 2019 ; Forde and OBrien, 2022 ).

Since 2020, the field of applied research on digital technology education has witnessed the emergence of three new hotspots, all of which have been affected to some extent by the pandemic. Firstly, digital technology has been widely applied in physical education, which is one of the subjects that has been severely affected by the pandemic (Parris et al. 2022 ; Jiang and Ning, 2022 ). Secondly, digital transformation has become an important measure for most schools, especially higher education institutions, to cope with the impact of the pandemic globally (García-Morales et al. 2021 ). Although the concept of digital transformation was proposed earlier, the COVID-19 pandemic has greatly accelerated this transformation process. Educational institutions must carefully redesign their educational products to face this new situation, providing timely digital learning methods, environments, tools, and support systems that have far-reaching impacts on modern society (Krishnamurthy, 2020 ; Salas-Pilco et al. 2022 ). Moreover, the professional development of teachers has become a key mission of educational institutions in the post-pandemic era. Teachers need to have a certain level of digital literacy and be familiar with the tools and online teaching resources used in online teaching, which has become a research hotspot today. Organizing digital skills training for teachers to cope with the application of emerging technologies in education is an important issue for teacher professional development and lifelong learning (Garzón-Artacho et al. 2021 ). As the main organizers and practitioners of emergency remote teaching (ERT) during the pandemic, teachers must put cognitive effort into their professional development to ensure effective implementation of ERT (Romero-Hall and Jaramillo Cherrez, 2022 ).

The burst word “digital transformation” reveals that we are in the midst of an ongoing digital technology revolution. With the emergence of innovative digital technologies such as ChatGPT and Microsoft 365 Copilot, technology trends will continue to evolve, albeit unpredictably. While the impact of these advancements on school education remains uncertain, it is anticipated that the widespread integration of technology will significantly affect the current education system. Rejecting emerging technologies without careful consideration is unwise. Like any revolution, the technological revolution in the education field has both positive and negative aspects. Detractors argue that digital technology disrupts learning and memory (Baron, 2021 ) or causes learners to become addicted and distracted from learning (Selwyn and Aagaard, 2020 ). On the other hand, the prudent use of digital technology in education offers a glimpse of a golden age of open learning. Educational leaders and practitioners have the opportunity to leverage cutting-edge digital technologies to address current educational challenges and develop a rational path for the sustainable and healthy growth of education.

Discussion on performance analysis (RQ1)

The field of digital technology education application research has experienced substantial growth since the turn of the century, a phenomenon that is quantifiably apparent through an analysis of authorship, country/region contributions, and institutional engagement. This expansion reflects the increased integration of digital technologies in educational settings and the heightened scholarly interest in understanding and optimizing their use.

Discussion on authorship productivity in digital technology education research

The authorship distribution within digital technology education research is indicative of the field’s intellectual structure and depth. A primary figure in this domain is Neil Selwyn, whose substantial citation rate underscores the profound impact of his work. His focus on the implications of digital technology in higher education and educational sociology has proven to be seminal. Selwyn’s research trajectory, especially the exploration of spatiotemporal extensions of education through technology, provides valuable insights into the multifaceted role of digital tools in learning processes (Selwyn et al. 2019 ).

Other notable contributors, like Henderson and Edwards, present diversified research interests, such as the impact of digital technologies during the pandemic and their application in early childhood education, respectively. Their varied focuses highlight the breadth of digital technology education research, encompassing pedagogical innovation, technological adaptation, and policy development.

Discussion on country/region-level productivity and collaboration

At the country/region level, the United Kingdom, specifically England, emerges as a leading contributor with 92 published papers and a significant citation count. This is closely followed by Australia and the United States, indicating a strong English-speaking research axis. Such geographical concentration of scholarly output often correlates with investment in research and development, technological infrastructure, and the prevalence of higher education institutions engaging in cutting-edge research.

China’s notable inclusion as the only non-Western country among the top contributors to the field suggests a growing research capacity and interest in digital technology in education. However, the lower average citation per paper for China could reflect emerging engagement or different research focuses that may not yet have achieved the same international recognition as Western counterparts.

The chord diagram analysis furthers this understanding, revealing dense interconnections between countries like the United States, China, and England, which indicates robust collaborations. Such collaborations are fundamental in addressing global educational challenges and shaping international research agendas.

Discussion on institutional-level contributions to digital technology education

Institutional productivity in digital technology education research reveals a constellation of universities driving the field forward. Monash University and the Australian Catholic University have the highest publication output, signaling Australia’s significant role in advancing digital education research. The University of Oslo’s remarkable average citation count per publication indicates influential research contributions, potentially reflecting high-quality studies that resonate with the broader academic community.

The strong showing of UK institutions, including the University of London, The Open University, and the University of Cambridge, reinforces the UK’s prominence in this research field. Such institutions are often at the forefront of pedagogical innovation, benefiting from established research cultures and funding mechanisms that support sustained inquiry into digital education.

Discussion on journal publication analysis

An examination of journal outputs offers a lens into the communicative channels of the field’s knowledge base. Journals such as Education and Information Technologies , Computers & Education , and the British Journal of Educational Technology not only serve as the primary disseminators of research findings but also as indicators of research quality and relevance. The impact factor (IF) serves as a proxy for the quality and influence of these journals within the academic community.

The high citation counts for articles published in Computers & Education suggest that research disseminated through this medium has a wide-reaching impact and is of particular interest to the field. This is further evidenced by its significant IF of 11.182, indicating that the journal is a pivotal platform for seminal work in the application of digital technology in education.

The authorship, regional, and institutional productivity in the field of digital technology education application research collectively narrate the evolution of this domain since the turn of the century. The prominence of certain authors and countries underscores the importance of socioeconomic factors and existing academic infrastructure in fostering research productivity. Meanwhile, the centrality of specific journals as outlets for high-impact research emphasizes the role of academic publishing in shaping the research landscape.

As the field continues to grow, future research may benefit from leveraging the collaborative networks that have been elucidated through this analysis, perhaps focusing on underrepresented regions to broaden the scope and diversity of research. Furthermore, the stabilization of publication numbers in recent years invites a deeper exploration into potential plateaus in research trends or saturation in certain sub-fields, signaling an opportunity for novel inquiries and methodological innovations.

Discussion on the evolutionary trends (RQ2)

The evolution of the research field concerning the application of digital technology in education over the past two decades is a story of convergence, diversification, and transformation, shaped by rapid technological advancements and shifting educational paradigms.

At the turn of the century, the inception of digital technology in education was largely exploratory, with a focus on how emerging computer technologies could be harnessed to enhance traditional learning environments. Research from this early period was primarily descriptive, reflecting on the potential and challenges of incorporating digital tools into the educational setting. This phase was critical in establishing the fundamental discourse that would guide subsequent research, as it set the stage for understanding the scope and impact of digital technology in learning spaces (Wang et al. 2023 ).

As the first decade progressed, the narrative expanded to encompass the pedagogical implications of digital technologies. This was a period of conceptual debates, where terms like “digital natives” and “disruptive pedagogy” entered the academic lexicon, underscoring the growing acknowledgment of digital technology as a transformative force within education (Bennett and Maton, 2010 ). During this time, the research began to reflect a more nuanced understanding of the integration of technology, considering not only its potential to change where and how learning occurred but also its implications for educational equity and access.

In the second decade, with the maturation of internet connectivity and mobile technology, the focus of research shifted from theoretical speculations to empirical investigations. The proliferation of digital devices and the ubiquity of social media influenced how learners interacted with information and each other, prompting a surge in studies that sought to measure the impact of these tools on learning outcomes. The digital divide and issues related to digital literacy became central concerns, as scholars explored the varying capacities of students and educators to engage with technology effectively.

Throughout this period, there was an increasing emphasis on the individualization of learning experiences, facilitated by adaptive technologies that could cater to the unique needs and pacing of learners (Jing et al. 2023a ). This individualization was coupled with a growing recognition of the importance of collaborative learning, both online and offline, and the role of digital tools in supporting these processes. Blended learning models, which combined face-to-face instruction with online resources, emerged as a significant trend, advocating for a balance between traditional pedagogies and innovative digital strategies.

The later years, particularly marked by the COVID-19 pandemic, accelerated the necessity for digital technology in education, transforming it from a supplementary tool to an essential platform for delivering education globally (Mo et al. 2022 ; Mustapha et al. 2021 ). This era brought about an unprecedented focus on online learning environments, distance education, and virtual classrooms. Research became more granular, examining not just the pedagogical effectiveness of digital tools, but also their role in maintaining continuity of education during crises, their impact on teacher and student well-being, and their implications for the future of educational policy and infrastructure.

Across these two decades, the research field has seen a shift from examining digital technology as an external addition to the educational process, to viewing it as an integral component of curriculum design, instructional strategies, and even assessment methods. The emergent themes have broadened from a narrow focus on specific tools or platforms to include wider considerations such as data privacy, ethical use of technology, and the environmental impact of digital tools.

Moreover, the field has moved from considering the application of digital technology in education as a primarily cognitive endeavor to recognizing its role in facilitating socio-emotional learning, digital citizenship, and global competencies. Researchers have increasingly turned their attention to the ways in which technology can support collaborative skills, cultural understanding, and ethical reasoning within diverse student populations.

In summary, the past over twenty years in the research field of digital technology applications in education have been characterized by a progression from foundational inquiries to complex analyses of digital integration. This evolution has mirrored the trajectory of technology itself, from a facilitative tool to a pervasive ecosystem defining contemporary educational experiences. As we look to the future, the field is poised to delve into the implications of emerging technologies like AI, AR, and VR, and their potential to redefine the educational landscape even further. This ongoing metamorphosis suggests that the application of digital technology in education will continue to be a rich area of inquiry, demanding continual adaptation and forward-thinking from educators and researchers alike.

Discussion on the study of research hotspots (RQ3)

The analysis of keyword evolution in digital technology education application research elucidates the current frontiers in the field, reflecting a trajectory that is in tandem with the rapidly advancing digital age. This landscape is sculpted by emergent technological innovations and shaped by the demands of an increasingly digital society.

Interdisciplinary integration and pedagogical transformation

One of the frontiers identified from recent keyword bursts includes the integration of digital technology into diverse educational contexts, particularly noted with the keyword “physical education.” The digitalization of disciplines traditionally characterized by physical presence illustrates the pervasive reach of technology and signifies a push towards interdisciplinary integration where technology is not only a facilitator but also a transformative agent. This integration challenges educators to reconceptualize curriculum delivery to accommodate digital tools that can enhance or simulate the physical aspects of learning.

Digital literacy and skills acquisition

Another pivotal frontier is the focus on “digital literacy” and “digital skill”, which has intensified in recent years. This suggests a shift from mere access to technology towards a comprehensive understanding and utilization of digital tools. In this realm, the emphasis is not only on the ability to use technology but also on critical thinking, problem-solving, and the ethical use of digital resources (Yu, 2022 ). The acquisition of digital literacy is no longer an additive skill but a fundamental aspect of modern education, essential for navigating and contributing to the digital world.

Educational digital transformation

The keyword “digital transformation” marks a significant research frontier, emphasizing the systemic changes that education institutions must undergo to align with the digital era (Romero et al. 2021 ). This transformation includes the redesigning of learning environments, pedagogical strategies, and assessment methods to harness digital technology’s full potential. Research in this area explores the complexity of institutional change, addressing the infrastructural, cultural, and policy adjustments needed for a seamless digital transition.

Engagement and participation

Further exploration into “engagement” and “participation” underscores the importance of student-centered learning environments that are mediated by technology. The current frontiers examine how digital platforms can foster collaboration, inclusivity, and active learning, potentially leading to more meaningful and personalized educational experiences. Here, the use of technology seeks to support the emotional and cognitive aspects of learning, moving beyond the transactional view of education to one that is relational and interactive.

Professional development and teacher readiness

As the field evolves, “professional development” emerges as a crucial area, particularly in light of the pandemic which necessitated emergency remote teaching. The need for teacher readiness in a digital age is a pressing frontier, with research focusing on the competencies required for educators to effectively integrate technology into their teaching practices. This includes familiarity with digital tools, pedagogical innovation, and an ongoing commitment to personal and professional growth in the digital domain.

Pandemic as a catalyst

The recent pandemic has acted as a catalyst for accelerated research and application in this field, particularly in the domains of “digital transformation,” “professional development,” and “physical education.” This period has been a litmus test for the resilience and adaptability of educational systems to continue their operations in an emergency. Research has thus been directed at understanding how digital technologies can support not only continuity but also enhance the quality and reach of education in such contexts.

Ethical and societal considerations

The frontier of digital technology in education is also expanding to consider broader ethical and societal implications. This includes issues of digital equity, data privacy, and the sociocultural impact of technology on learning communities. The research explores how educational technology can be leveraged to address inequities and create more equitable learning opportunities for all students, regardless of their socioeconomic background.

Innovation and emerging technologies

Looking forward, the frontiers are set to be influenced by ongoing and future technological innovations, such as artificial intelligence (AI) (Wu and Yu, 2023 ; Chen et al. 2022a ). The exploration into how these technologies can be integrated into educational practices to create immersive and adaptive learning experiences represents a bold new chapter for the field.

In conclusion, the current frontiers of research on the application of digital technology in education are multifaceted and dynamic. They reflect an overarching movement towards deeper integration of technology in educational systems and pedagogical practices, where the goals are not only to facilitate learning but to redefine it. As these frontiers continue to expand and evolve, they will shape the educational landscape, requiring a concerted effort from researchers, educators, policymakers, and technologists to navigate the challenges and harness the opportunities presented by the digital revolution in education.

Conclusions and future research

Conclusions.

The utilization of digital technology in education is a research area that cuts across multiple technical and educational domains and continues to experience dynamic growth due to the continuous progress of technology. In this study, a systematic review of this field was conducted through bibliometric techniques to examine its development trajectory. The primary focus of the review was to investigate the leading contributors, productive national institutions, significant publications, and evolving development patterns. The study’s quantitative analysis resulted in several key conclusions that shed light on this research field’s current state and future prospects.

(1) The research field of digital technology education applications has entered a stage of rapid development, particularly in recent years due to the impact of the pandemic, resulting in a peak of publications. Within this field, several key authors (Selwyn, Henderson, Edwards, etc.) and countries/regions (England, Australia, USA, etc.) have emerged, who have made significant contributions. International exchanges in this field have become frequent, with a high degree of internationalization in academic research. Higher education institutions in the UK and Australia are the core productive forces in this field at the institutional level.

(2) Education and Information Technologies , Computers & Education , and the British Journal of Educational Technology are notable journals that publish research related to digital technology education applications. These journals are affiliated with the research field of educational technology and provide effective communication platforms for sharing digital technology education applications.

(3) Over the past two decades, research on digital technology education applications has progressed from its early stages of budding, initial development, and critical exploration to accelerated transformation, and it is currently approaching maturity. Technological progress and changes in the times have been key driving forces for educational transformation and innovation, and both have played important roles in promoting the continuous development of education.

(4) Influenced by the pandemic, three emerging frontiers have emerged in current research on digital technology education applications, which are physical education, digital transformation, and professional development under the promotion of digital technology. These frontier research hotspots reflect the core issues that the education system faces when encountering new technologies. The evolution of research hotspots shows that technology breakthroughs in education’s original boundaries of time and space create new challenges. The continuous self-renewal of education is achieved by solving one hotspot problem after another.

The present study offers significant practical implications for scholars and practitioners in the field of digital technology education applications. Firstly, it presents a well-defined framework of the existing research in this area, serving as a comprehensive guide for new entrants to the field and shedding light on the developmental trajectory of this research domain. Secondly, the study identifies several contemporary research hotspots, thus offering a valuable decision-making resource for scholars aiming to explore potential research directions. Thirdly, the study undertakes an exhaustive analysis of published literature to identify core journals in the field of digital technology education applications, with Sustainability being identified as a promising open access journal that publishes extensively on this topic. This finding can potentially facilitate scholars in selecting appropriate journals for their research outputs.

Limitation and future research

Influenced by some objective factors, this study also has some limitations. First of all, the bibliometrics analysis software has high standards for data. In order to ensure the quality and integrity of the collected data, the research only selects the periodical papers in SCIE and SSCI indexes, which are the core collection of Web of Science database, and excludes other databases, conference papers, editorials and other publications, which may ignore some scientific research and original opinions in the field of digital technology education and application research. In addition, although this study used professional software to carry out bibliometric analysis and obtained more objective quantitative data, the analysis and interpretation of data will inevitably have a certain subjective color, and the influence of subjectivity on data analysis cannot be completely avoided. As such, future research endeavors will broaden the scope of literature screening and proactively engage scholars in the field to gain objective and state-of-the-art insights, while minimizing the adverse impact of personal subjectivity on research analysis.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/F9QMHY

Alabdulaziz MS (2021) COVID-19 and the use of digital technology in mathematics education. Educ Inf Technol 26(6):7609–7633. https://doi.org/10.1007/s10639-021-10602-3

Arif TB, Munaf U, Ul-Haque I (2023) The future of medical education and research: is ChatGPT a blessing or blight in disguise? Med Educ Online 28. https://doi.org/10.1080/10872981.2023.2181052

Banerjee M, Chiew D, Patel KT, Johns I, Chappell D, Linton N, Cole GD, Francis DP, Szram J, Ross J, Zaman S (2021) The impact of artificial intelligence on clinical education: perceptions of postgraduate trainee doctors in London (UK) and recommendations for trainers. BMC Med Educ 21. https://doi.org/10.1186/s12909-021-02870-x

Barlovits S, Caldeira A, Fesakis G, Jablonski S, Koutsomanoli Filippaki D, Lázaro C, Ludwig M, Mammana MF, Moura A, Oehler DXK, Recio T, Taranto E, Volika S(2022) Adaptive, synchronous, and mobile online education: developing the ASYMPTOTE learning environment. Mathematics 10:1628. https://doi.org/10.3390/math10101628

Article   Google Scholar  

Baron NS(2021) Know what? How digital technologies undermine learning and remembering J Pragmat 175:27–37. https://doi.org/10.1016/j.pragma.2021.01.011

Batista J, Morais NS, Ramos F (2016) Researching the use of communication technologies in higher education institutions in Portugal. https://doi.org/10.4018/978-1-5225-0571-6.ch057

Beardsley M, Albó L, Aragón P, Hernández-Leo D (2021) Emergency education effects on teacher abilities and motivation to use digital technologies. Br J Educ Technol 52. https://doi.org/10.1111/bjet.13101

Bennett S, Maton K(2010) Beyond the “digital natives” debate: towards a more nuanced understanding of students’ technology experiences J Comput Assist Learn 26:321–331. https://doi.org/10.1111/j.1365-2729.2010.00360.x

Buckingham D, Burn A (2007) Game literacy in theory and practice 16:323–349

Google Scholar  

Bulfin S, Pangrazio L, Selwyn N (2014) Making “MOOCs”: the construction of a new digital higher education within news media discourse. In: The International Review of Research in Open and Distributed Learning 15. https://doi.org/10.19173/irrodl.v15i5.1856

Camilleri MA, Camilleri AC(2016) Digital learning resources and ubiquitous technologies in education Technol Knowl Learn 22:65–82. https://doi.org/10.1007/s10758-016-9287-7

Chen C(2006) CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature J Am Soc Inf Sci Technol 57:359–377. https://doi.org/10.1002/asi.20317

Chen J, Dai J, Zhu K, Xu L(2022) Effects of extended reality on language learning: a meta-analysis Front Psychol 13:1016519. https://doi.org/10.3389/fpsyg.2022.1016519

Article   PubMed   PubMed Central   Google Scholar  

Chen J, Wang CL, Tang Y (2022b) Knowledge mapping of volunteer motivation: a bibliometric analysis and cross-cultural comparative study. Front Psychol 13. https://doi.org/10.3389/fpsyg.2022.883150

Cohen A, Soffer T, Henderson M(2022) Students’ use of technology and their perceptions of its usefulness in higher education: International comparison J Comput Assist Learn 38(5):1321–1331. https://doi.org/10.1111/jcal.12678

Collins A, Halverson R(2010) The second educational revolution: rethinking education in the age of technology J Comput Assist Learn 26:18–27. https://doi.org/10.1111/j.1365-2729.2009.00339.x

Conole G, Alevizou P (2010) A literature review of the use of Web 2.0 tools in higher education. Walton Hall, Milton Keynes, UK: the Open University, retrieved 17 February

Creely E, Henriksen D, Crawford R, Henderson M(2021) Exploring creative risk-taking and productive failure in classroom practice. A case study of the perceived self-efficacy and agency of teachers at one school Think Ski Creat 42:100951. https://doi.org/10.1016/j.tsc.2021.100951

Davis N, Eickelmann B, Zaka P(2013) Restructuring of educational systems in the digital age from a co-evolutionary perspective J Comput Assist Learn 29:438–450. https://doi.org/10.1111/jcal.12032

De Belli N (2009) Bibliometrics and citation analysis: from the science citation index to cybermetrics, Scarecrow Press. https://doi.org/10.1111/jcal.12032

Domínguez A, Saenz-de-Navarrete J, de-Marcos L, Fernández-Sanz L, Pagés C, Martínez-Herráiz JJ(2013) Gamifying learning experiences: practical implications and outcomes Comput Educ 63:380–392. https://doi.org/10.1016/j.compedu.2012.12.020

Donnison S (2009) Discourses in conflict: the relationship between Gen Y pre-service teachers, digital technologies and lifelong learning. Australasian J Educ Technol 25. https://doi.org/10.14742/ajet.1138

Durfee SM, Jain S, Shaffer K (2003) Incorporating electronic media into medical student education. Acad Radiol 10:205–210. https://doi.org/10.1016/s1076-6332(03)80046-6

Dzikowski P(2018) A bibliometric analysis of born global firms J Bus Res 85:281–294. https://doi.org/10.1016/j.jbusres.2017.12.054

van Eck NJ, Waltman L(2009) Software survey: VOSviewer, a computer program for bibliometric mapping Scientometrics 84:523–538 https://doi.org/10.1007/s11192-009-0146-3

Edwards S(2013) Digital play in the early years: a contextual response to the problem of integrating technologies and play-based pedagogies in the early childhood curriculum Eur Early Child Educ Res J 21:199–212. https://doi.org/10.1080/1350293x.2013.789190

Edwards S(2015) New concepts of play and the problem of technology, digital media and popular-culture integration with play-based learning in early childhood education Technol Pedagogy Educ 25:513–532 https://doi.org/10.1080/1475939x.2015.1108929

Article   MathSciNet   Google Scholar  

Eisenberg MB(2008) Information literacy: essential skills for the information age DESIDOC J Libr Inf Technol 28:39–47. https://doi.org/10.14429/djlit.28.2.166

Forde C, OBrien A (2022) A literature review of barriers and opportunities presented by digitally enhanced practical skill teaching and learning in health science education. Med Educ Online 27. https://doi.org/10.1080/10872981.2022.2068210

García-Morales VJ, Garrido-Moreno A, Martín-Rojas R (2021) The transformation of higher education after the COVID disruption: emerging challenges in an online learning scenario. Front Psychol 12. https://doi.org/10.3389/fpsyg.2021.616059

Garfield E(2006) The history and meaning of the journal impact factor JAMA 295:90. https://doi.org/10.1001/jama.295.1.90

Article   PubMed   Google Scholar  

Garzón-Artacho E, Sola-Martínez T, Romero-Rodríguez JM, Gómez-García G(2021) Teachers’ perceptions of digital competence at the lifelong learning stage Heliyon 7:e07513. https://doi.org/10.1016/j.heliyon.2021.e07513

Gaviria-Marin M, Merigó JM, Baier-Fuentes H(2019) Knowledge management: a global examination based on bibliometric analysis Technol Forecast Soc Change 140:194–220. https://doi.org/10.1016/j.techfore.2018.07.006

Gilster P, Glister P (1997) Digital literacy. Wiley Computer Pub, New York

Greenhow C, Lewin C(2015) Social media and education: reconceptualizing the boundaries of formal and informal learning Learn Media Technol 41:6–30. https://doi.org/10.1080/17439884.2015.1064954

Hawkins DT(2001) Bibliometrics of electronic journals in information science Infor Res 7(1):7–1. http://informationr.net/ir/7-1/paper120.html

Henderson M, Selwyn N, Finger G, Aston R(2015) Students’ everyday engagement with digital technology in university: exploring patterns of use and “usefulness J High Educ Policy Manag 37:308–319 https://doi.org/10.1080/1360080x.2015.1034424

Huang CK, Neylon C, Hosking R, Montgomery L, Wilson KS, Ozaygen A, Brookes-Kenworthy C (2020) Evaluating the impact of open access policies on research institutions. eLife 9. https://doi.org/10.7554/elife.57067

Hwang GJ, Tsai CC(2011) Research trends in mobile and ubiquitous learning: a review of publications in selected journals from 2001 to 2010 Br J Educ Technol 42:E65–E70. https://doi.org/10.1111/j.1467-8535.2011.01183.x

Hwang GJ, Wu PH, Zhuang YY, Huang YM(2013) Effects of the inquiry-based mobile learning model on the cognitive load and learning achievement of students Interact Learn Environ 21:338–354. https://doi.org/10.1080/10494820.2011.575789

Jiang S, Ning CF (2022) Interactive communication in the process of physical education: are social media contributing to the improvement of physical training performance. Universal Access Inf Soc, 1–10. https://doi.org/10.1007/s10209-022-00911-w

Jing Y, Zhao L, Zhu KK, Wang H, Wang CL, Xia Q(2023) Research landscape of adaptive learning in education: a bibliometric study on research publications from 2000 to 2022 Sustainability 15:3115–3115. https://doi.org/10.3390/su15043115

Jing Y, Wang CL, Chen Y, Wang H, Yu T, Shadiev R (2023b) Bibliometric mapping techniques in educational technology research: a systematic literature review. Educ Inf Technol 1–29. https://doi.org/10.1007/s10639-023-12178-6

Krishnamurthy S (2020) The future of business education: a commentary in the shadow of the Covid-19 pandemic. J Bus Res. https://doi.org/10.1016/j.jbusres.2020.05.034

Kumar S, Lim WM, Pandey N, Christopher Westland J (2021) 20 years of electronic commerce research. Electron Commer Res 21:1–40

Kyza EA, Georgiou Y(2018) Scaffolding augmented reality inquiry learning: the design and investigation of the TraceReaders location-based, augmented reality platform Interact Learn Environ 27:211–225. https://doi.org/10.1080/10494820.2018.1458039

Laurillard D(2008) Technology enhanced learning as a tool for pedagogical innovation J Philos Educ 42:521–533. https://doi.org/10.1111/j.1467-9752.2008.00658.x

Li M, Yu Z (2023) A systematic review on the metaverse-based blended English learning. Front Psychol 13. https://doi.org/10.3389/fpsyg.2022.1087508

Luo H, Li G, Feng Q, Yang Y, Zuo M (2021) Virtual reality in K-12 and higher education: a systematic review of the literature from 2000 to 2019. J Comput Assist Learn. https://doi.org/10.1111/jcal.12538

Margaryan A, Littlejohn A, Vojt G(2011) Are digital natives a myth or reality? University students’ use of digital technologies Comput Educ 56:429–440. https://doi.org/10.1016/j.compedu.2010.09.004

McMillan S(1996) Literacy and computer literacy: definitions and comparisons Comput Educ 27:161–170. https://doi.org/10.1016/s0360-1315(96)00026-7

Mo CY, Wang CL, Dai J, Jin P (2022) Video playback speed influence on learning effect from the perspective of personalized adaptive learning: a study based on cognitive load theory. Front Psychology 13. https://doi.org/10.3389/fpsyg.2022.839982

Moorhouse BL (2021) Beginning teaching during COVID-19: newly qualified Hong Kong teachers’ preparedness for online teaching. Educ Stud 1–17. https://doi.org/10.1080/03055698.2021.1964939

Moorhouse BL, Wong KM (2021) The COVID-19 Pandemic as a catalyst for teacher pedagogical and technological innovation and development: teachers’ perspectives. Asia Pac J Educ 1–16. https://doi.org/10.1080/02188791.2021.1988511

Moskal P, Dziuban C, Hartman J (2013) Blended learning: a dangerous idea? Internet High Educ 18:15–23

Mughal MY, Andleeb N, Khurram AFA, Ali MY, Aslam MS, Saleem MN (2022) Perceptions of teaching-learning force about Metaverse for education: a qualitative study. J. Positive School Psychol 6:1738–1745

Mustapha I, Thuy Van N, Shahverdi M, Qureshi MI, Khan N (2021) Effectiveness of digital technology in education during COVID-19 pandemic. a bibliometric analysis. Int J Interact Mob Technol 15:136

Nagle J (2018) Twitter, cyber-violence, and the need for a critical social media literacy in teacher education: a review of the literature. Teach Teach Education 76:86–94

Nazare J, Woolf A, Sysoev I, Ballinger S, Saveski M, Walker M, Roy D (2022) Technology-assisted coaching can increase engagement with learning technology at home and caregivers’ awareness of it. Comput Educ 188:104565

Nguyen UP, Hallinger P (2020) Assessing the distinctive contributions of simulation & gaming to the literature, 1970-2019: a bibliometric review. Simul Gaming 104687812094156. https://doi.org/10.1177/1046878120941569

Nygren H, Nissinen K, Hämäläinen R, Wever B(2019) Lifelong learning: formal, non-formal and informal learning in the context of the use of problem-solving skills in technology-rich environments Br J Educ Technol 50:1759–1770. https://doi.org/10.1111/bjet.12807

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg 88:105906

Pan SL, Zhang S(2020) From fighting COVID-19 pandemic to tackling sustainable development goals: an opportunity for responsible information systems research Int J Inf Manage 55:102196. https://doi.org/10.1016/j.ijinfomgt.2020.102196

Pan X, Yan E, Cui M, Hua W(2018) Examining the usage, citation, and diffusion patterns of bibliometric mapping software: a comparative study of three tools J Informetr 12:481–493. https://doi.org/10.1016/j.joi.2018.03.005

Parris Z, Cale L, Harris J, Casey A (2022) Physical activity for health, covid-19 and social media: what, where and why?. Movimento, 28. https://doi.org/10.22456/1982-8918.122533

Pasquini LA, Evangelopoulos N (2016) Sociotechnical stewardship in higher education: a field study of social media policy documents. J Comput High Educ 29:218–239

Pérez-Sanagustín M, Hernández-Leo D, Santos P, Delgado Kloos C, Blat J(2014) Augmenting reality and formality of informal and non-formal settings to enhance blended learning IEEE Trans Learn Technol 7:118–131. https://doi.org/10.1109/TLT.2014.2312719

Pinto M, Leite C (2020) Digital technologies in support of students learning in Higher Education: literature review. Digital Education Review 343–360. https://doi.org/10.1344/der.2020.37.343-360

Pires F, Masanet MJ, Tomasena JM, Scolari CA(2022) Learning with YouTube: beyond formal and informal through new actors, strategies and affordances Convergence 28(3):838–853. https://doi.org/10.1177/1354856521102054

Pritchard A (1969) Statistical bibliography or bibliometrics 25:348

Romero M, Romeu T, Guitert M, Baztán P (2021) Digital transformation in higher education: the UOC case. In ICERI2021 Proceedings (pp. 6695–6703). IATED https://doi.org/10.21125/iceri.2021.1512

Romero-Hall E, Jaramillo Cherrez N (2022) Teaching in times of disruption: faculty digital literacy in higher education during the COVID-19 pandemic. Innovations in Education and Teaching International 1–11. https://doi.org/10.1080/14703297.2022.2030782

Rospigliosi PA(2023) Artificial intelligence in teaching and learning: what questions should we ask of ChatGPT? Interactive Learning Environments 31:1–3. https://doi.org/10.1080/10494820.2023.2180191

Salas-Pilco SZ, Yang Y, Zhang Z(2022) Student engagement in online learning in Latin American higher education during the COVID-19 pandemic: a systematic review. Br J Educ Technol 53(3):593–619. https://doi.org/10.1111/bjet.13190

Selwyn N(2009) The digital native-myth and reality In Aslib proceedings 61(4):364–379. https://doi.org/10.1108/00012530910973776

Selwyn N(2012) Making sense of young people, education and digital technology: the role of sociological theory Oxford Review of Education 38:81–96. https://doi.org/10.1080/03054985.2011.577949

Selwyn N, Facer K(2014) The sociology of education and digital technology: past, present and future Oxford Rev Educ 40:482–496. https://doi.org/10.1080/03054985.2014.933005

Selwyn N, Banaji S, Hadjithoma-Garstka C, Clark W(2011) Providing a platform for parents? Exploring the nature of parental engagement with school Learning Platforms J Comput Assist Learn 27:314–323. https://doi.org/10.1111/j.1365-2729.2011.00428.x

Selwyn N, Aagaard J (2020) Banning mobile phones from classrooms-an opportunity to advance understandings of technology addiction, distraction and cyberbullying. Br J Educ Technol 52. https://doi.org/10.1111/bjet.12943

Selwyn N, O’Neill C, Smith G, Andrejevic M, Gu X (2021) A necessary evil? The rise of online exam proctoring in Australian universities. Media Int Austr 1329878X2110058. https://doi.org/10.1177/1329878x211005862

Selwyn N, Pangrazio L, Nemorin S, Perrotta C (2019) What might the school of 2030 be like? An exercise in social science fiction. Learn, Media Technol 1–17. https://doi.org/10.1080/17439884.2020.1694944

Selwyn, N (2016) What works and why?* Understanding successful technology enabled learning within institutional contexts 2016 Final report Appendices (Part B). Monash University Griffith University

Sjöberg D, Holmgren R (2021) Informal workplace learning in swedish police education-a teacher perspective. Vocations and Learning. https://doi.org/10.1007/s12186-021-09267-3

Strotmann A, Zhao D (2012) Author name disambiguation: what difference does it make in author-based citation analysis? J Am Soc Inf Sci Technol 63:1820–1833

Article   CAS   Google Scholar  

Sutherland R, Facer K, Furlong R, Furlong J(2000) A new environment for education? The computer in the home. Comput Educ 34:195–212. https://doi.org/10.1016/s0360-1315(99)00045-7

Szeto E, Cheng AY-N, Hong J-C(2015) Learning with social media: how do preservice teachers integrate YouTube and Social Media in teaching? Asia-Pac Educ Res 25:35–44. https://doi.org/10.1007/s40299-015-0230-9

Tang E, Lam C(2014) Building an effective online learning community (OLC) in blog-based teaching portfolios Int High Educ 20:79–85. https://doi.org/10.1016/j.iheduc.2012.12.002

Taskin Z, Al U(2019) Natural language processing applications in library and information science Online Inf Rev 43:676–690. https://doi.org/10.1108/oir-07-2018-0217

Tegtmeyer K, Ibsen L, Goldstein B(2001) Computer-assisted learning in critical care: from ENIAC to HAL Crit Care Med 29:N177–N182. https://doi.org/10.1097/00003246-200108001-00006

Article   CAS   PubMed   Google Scholar  

Timotheou S, Miliou O, Dimitriadis Y, Sobrino SV, Giannoutsou N, Cachia R, Moné AM, Ioannou A(2023) Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: a literature review. Educ Inf Technol 28(6):6695–6726. https://doi.org/10.1007/s10639-022-11431-8

Trujillo Maza EM, Gómez Lozano MT, Cardozo Alarcón AC, Moreno Zuluaga L, Gamba Fadul M (2016) Blended learning supported by digital technology and competency-based medical education: a case study of the social medicine course at the Universidad de los Andes, Colombia. Int J Educ Technol High Educ 13. https://doi.org/10.1186/s41239-016-0027-9

Turin O, Friesem Y(2020) Is that media literacy?: Israeli and US media scholars’ perceptions of the field J Media Lit Educ 12:132–144. https://doi.org/10.1007/s11192-009-0146-3

Van Eck NJ, Waltman L (2019) VOSviewer manual. Universiteit Leiden

Vratulis V, Clarke T, Hoban G, Erickson G(2011) Additive and disruptive pedagogies: the use of slowmation as an example of digital technology implementation Teach Teach Educ 27:1179–1188. https://doi.org/10.1016/j.tate.2011.06.004

Wang CL, Dai J, Xu LJ (2022) Big data and data mining in education: a bibliometrics study from 2010 to 2022. In 2022 7th International Conference on Cloud Computing and Big Data Analytics ( ICCCBDA ) (pp. 507-512). IEEE. https://doi.org/10.1109/icccbda55098.2022.9778874

Wang CL, Dai J, Zhu KK, Yu T, Gu XQ (2023) Understanding the continuance intention of college students toward new E-learning spaces based on an integrated model of the TAM and TTF. Int J Hum-Comput Int 1–14. https://doi.org/10.1080/10447318.2023.2291609

Wong L-H, Boticki I, Sun J, Looi C-K(2011) Improving the scaffolds of a mobile-assisted Chinese character forming game via a design-based research cycle Comput Hum Behav 27:1783–1793. https://doi.org/10.1016/j.chb.2011.03.005

Wu R, Yu Z (2023) Do AI chatbots improve students learning outcomes? Evidence from a meta-analysis. Br J Educ Technol. https://doi.org/10.1111/bjet.13334

Yang D, Zhou J, Shi D, Pan Q, Wang D, Chen X, Liu J (2022) Research status, hotspots, and evolutionary trends of global digital education via knowledge graph analysis. Sustainability 14:15157–15157. https://doi.org/10.3390/su142215157

Yu T, Dai J, Wang CL (2023) Adoption of blended learning: Chinese university students’ perspectives. Humanit Soc Sci Commun 10:390. https://doi.org/10.3390/su142215157

Yu Z (2022) Sustaining student roles, digital literacy, learning achievements, and motivation in online learning environments during the COVID-19 pandemic. Sustainability 14:4388. https://doi.org/10.3390/su14084388

Za S, Spagnoletti P, North-Samardzic A(2014) Organisational learning as an emerging process: the generative role of digital tools in informal learning practices Br J Educ Technol 45:1023–1035. https://doi.org/10.1111/bjet.12211

Zhang X, Chen Y, Hu L, Wang Y (2022) The metaverse in education: definition, framework, features, potential applications, challenges, and future research topics. Front Psychol 13:1016300. https://doi.org/10.3389/fpsyg.2022.1016300

Zhou M, Dzingirai C, Hove K, Chitata T, Mugandani R (2022) Adoption, use and enhancement of virtual learning during COVID-19. Education and Information Technologies. https://doi.org/10.1007/s10639-022-10985-x

Download references

Acknowledgements

This research was supported by the Zhejiang Provincial Social Science Planning Project, “Mechanisms and Pathways for Empowering Classroom Teaching through Learning Spaces under the Strategy of High-Quality Education Development”, the 2022 National Social Science Foundation Education Youth Project “Research on the Strategy of Creating Learning Space Value and Empowering Classroom Teaching under the background of ‘Double Reduction’” (Grant No. CCA220319) and the National College Student Innovation and Entrepreneurship Training Program of China (Grant No. 202310337023).

Author information

Authors and affiliations.

College of Educational Science and Technology, Zhejiang University of Technology, Zhejiang, China

Chengliang Wang, Xiaojiao Chen, Yidan Liu & Yuhui Jing

Graduate School of Business, Universiti Sains Malaysia, Minden, Malaysia

Department of Management, The Chinese University of Hong Kong, Hong Kong, China

College of Humanities and Social Sciences, Beihang University, Beijing, China

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization: Y.J., C.W.; methodology, C.W.; software, C.W., Y.L.; writing-original draft preparation, C.W., Y.L.; writing-review and editing, T.Y., Y.L., C.W.; supervision, X.C., T.Y.; project administration, Y.J.; funding acquisition, X.C., Y.L. All authors read and approved the final manuscript. All authors have read and approved the re-submission of the manuscript.

Corresponding author

Correspondence to Yuhui Jing .

Ethics declarations

Ethical approval.

Ethical approval was not required as the study did not involve human participants.

Informed consent

Informed consent was not required as the study did not involve human participants.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Wang, C., Chen, X., Yu, T. et al. Education reform and change driven by digital technology: a bibliometric study from a global perspective. Humanit Soc Sci Commun 11 , 256 (2024). https://doi.org/10.1057/s41599-024-02717-y

Download citation

Received : 11 July 2023

Accepted : 17 January 2024

Published : 12 February 2024

DOI : https://doi.org/10.1057/s41599-024-02717-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

A meta-analysis of learners’ continuance intention toward online education platforms.

• Chengliang Wang

Education and Information Technologies (2024)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Become an Insider

Sign up today to receive premium content.

The Evolution of Technology in K–12 Classrooms: 1659 to Today

Alexander Huls is a Toronto-based writer whose work has appeared in  The New York Times ,  Popular Mechanics ,  Esquire ,  The Atlantic  and elsewhere.

In the 21st century, it can feel like advanced technology is changing the K–12 classroom in ways we’ve never seen before. But the truth is, technology and education have a long history of evolving together to dramatically change how students learn.

With more innovations surely headed our way, why not look back at how we got to where we are today, while looking forward to how educators can continue to integrate new technologies into their learning?

DISCOVER:  Special education departments explore advanced tech in their classrooms.

Using Technology in the K–12 Classroom: A History

1659: magic lantern.

• Inventor:  Christiaan Huygens
• A Brief History:  An ancestor of the slide projector, the magic lantern projected glass slides with light from oil lamps or candles. In the 1680s, the technology was brought to the education space to show detailed anatomical illustrations, which were difficult to sketch on a chalkboard.
• Interesting Fact:  Huygens initially regretted his creation, thinking it was too frivolous.

1795: Pencil

• Inventor:  Nicolas-Jacques Conté
• A Brief History : Versions of the pencil can be traced back hundreds of years, but what’s considered the modern pencil is credited to Conté, a scientist in Napoleon Bonaparte’s army. It made its impact on the classroom, however, when it began to be mass produced in the 1900s.
• Interesting Fact:  The Aztecs used a form of graphite pencil in the 13th century.

1801: Chalkboard

• Inventor:  James Pillans
• A Brief History:  Pillans — a headmaster at a high school in Edinburgh, Scotland — created the first front-of-class chalkboard, or “blackboard,” to better teach his students geography with large maps. Prior to his creation, educators worked with students on smaller, individual pieces of wood or slate. In the 1960s, the creation was upgraded to a green board, which became a familiar fixture in every classroom.
• Interesting Fact:  Before chalkboards were commercially manufactured, some were made do-it-yourself-style with ingredients like pine board, egg whites and charred potatoes.

1888: Ballpoint Pen

• Inventory:  John L. Loud
• A Brief History:  John L. Loud invented and patented the first ballpoint pen after seeking to create a tool that could write on leather. It was not a commercial success. Fifty years later, following the lapse of Loud’s patent, Hungarian journalist László Bíró invented a pen with a quick-drying special ink that wouldn’t smear thanks to a rolling ball in its nib.
• Interesting Fact:  When ballpoint pens debuted in the U.S., they were so popular that Gimbels, the department store selling them, made $81 million in today’s money within six months.

LEARN MORE:  Logitech Pen works with Chromebooks to combine digital and physical learning.

1950s: Overhead Projector

• Inventor:  Roger Appeldorn
• A Brief History:  Overhead projects were used during World War II for mission briefings. However, 3M employee Appeldorn is credited with creating not only a projectable transparent film, but also the overhead projectors that would find a home in classrooms for decades.
• Interesting Fact:  Appeldorn’s creation is the predecessor to today’s  bright and efficient laser projectors .

1959: Photocopier

• Inventor:  Chester Carlson
• A Brief History:  Because of his arthritis, patent attorney and inventor Carlson wanted to create a less painful alternative to making carbon copies. Between 1938 and 1947, working with The Haloid Photographic Company, Carlson perfected the process of electrophotography, which led to development of the first photocopy machines.
• Interesting Fact:  Haloid and Carlson named their photocopying process xerography, which means “dry writing” in Greek. Eventually, Haloid renamed its company (and its flagship product line) Xerox .

1967: Handheld Calculator

• Inventor:   Texas Instruments
• A Brief History:  As recounted in our  history of the calculator , Texas Instruments made calculators portable with a device that weighed 45 ounces and featured a small keyboard with 18 keys and a visual display of 12 decimal digits.
• Interesting Fact:  The original 1967 prototype of the device can be found in the Smithsonian Institution’s  National Museum of American History .

1981: The Osborne 1 Laptop

• Inventor:  Adam Osborne, Lee Felsenstein
• A Brief History:  Osborne, a computer book author, teamed up with computer engineer Felsenstein to create a portable computer that would appeal to general consumers. In the process, they provided the technological foundation that made modern one-to-one devices — like Chromebooks — a classroom staple.
• Interesting Fact:  At 24.5 pounds, the Osborne 1 was about as big and heavy as a sewing machine, earning it the current classification of a “luggable” computer, rather than a laptop.

1990: World Wide Web

• Inventor:  Tim Berners-Lee
• A Brief History:  In the late 1980s, British scientist Berners-Lee created the World Wide Web to enable information sharing between scientists and academics. It wasn’t long before the Web could connect anyone, anywhere to a wealth of information, and it was soon on its way to powering the modern classroom.
• Interesting Fact:  The first web server Berners-Lee created was so new, he had to put a sign on the computer that read, “This machine is a server. DO NOT POWER IT DOWN!”

Click the banner  to access customized K–12 technology content when you sign up as an Insider.

What Technology Is Used in Today’s K–12 Classrooms?

Technology has come so far that modern classrooms are more technologically advanced than many science labs were two decades ago. Students have access to digital textbooks,  personal devices , collaborative  cloud-based tools , and  interactive whiteboards . Emerging technologies now being introduced to K–12 classrooms include voice assistants, virtual reality devices and 3D printers.

Perhaps the most important thing about ed tech in K–12 isn’t what the technology is, but how it’s used.

How to Integrate Technology into K–12 Classrooms

The first step to integrating technology into the K–12 classroom is  figuring out which solution to integrate , given the large variety of tools available to educators. That variety comes with benefits — like the ability to align tech with district objectives and grade level — but also brings challenges.

“It’s difficult to know how to choose the appropriate digital tool or resource,” says Judi Harris, professor and Pavey Family Chair in Educational Technology at the William & Mary School of Education. “Teachers need some familiarity with the tools so that they understand the potential advantages and disadvantages.”

Judi Harris Professor and Pavey Family Chair in Educational Technology, William and Mary School of Education

K–12 IT leaders should also be careful not to focus too much on technology implementation at the expense of curriculum-based learning needs. “What districts need to ask themselves is not only whether they’re going to adopt a technology, but how they’re going to adopt it,” says Royce Kimmons, associate professor of instructional psychology and technology at Brigham Young University.

In other words, while emerging technologies may be exciting, acquiring them without proper consideration of their role in improving classroom learning will likely result in mixed student outcomes. For effective integration, educators should ask themselves, in what ways would the tech increase or support a student’s productivity and learning outcomes? How will it improve engagement?

Integrating ed tech also requires some practical know-how. “Teachers need to be comfortable and confident with the tools they ask students to use,” says Harris.

Professional development for new technologies is crucial, as are supportive IT teams, tech providers with generous onboarding programs and technology integration specialists. Harris also points to initiatives like YES: Youth and Educators Succeeding, a nonprofit organization that prepares students to act as resident experts and classroom IT support.

KEEP READING:  What is the continued importance of professional development in K–12 education?

But as educational technology is rolled out and integrated, it’s important to keep academic goals in sight. “We should never stop focusing on how to best understand and help the learner to achieve those learning objectives,” says Harris.

That should continue to be the case as the technology timeline unfolds, something Harris has witnessed firsthand during her four decades in the field. “It’s been an incredible thing to watch and to participate in,” she notes. “The great majority of teachers are extremely eager to learn and to do anything that will help their students learn better.”

• Professional Development

Related Articles

Unlock white papers, personalized recommendations and other premium content for an in-depth look at evolving IT

Copyright © 2024 CDW LLC 200 N. Milwaukee Avenue , Vernon Hills, IL 60061 Do Not Sell My Personal Information

Global Education Monitoring Report

Technology in education

As recognised in the Incheon Declaration, the achievement of SDG 4 is dependent on opportunities and challenges posed by technology, a relationship that was strengthened by the onset of the COVID-19 pandemic. Technology appears in six out of the ten targets in the fourth Sustainable Development goal on education. These references recognize that technology affects education through five distinct channels, as input, means of delivery, skill, tool for planning, and providing a social and cultural context.

There are often bitter divisions in how the role of technology is viewed, however. These divisions are widening as the technology is evolving at breakneck speed.  The 2023 GEM Report on technology and education explores these debates, examining education challenges to which appropriate use of technology can offer solutions (access, equity and inclusion; quality; technology advancement; system management), while recognizing that many solutions proposed may also be detrimental.

The report also explores three system-wide conditions (access to technology, governance regulation, and teacher preparation) that need to be met for any technology in education to reach its full potential. It provides the mid-term assessment of progress towards SDG 4 , which was summarized in a brochure and promoted at the 2023 SDG Summit.

The 2023 GEM Report and 200 PEER country profiles on technology and education were launched on 26 July. A recording of the global launch event can be watched  here  and a south-south dialogue between Ministers of education in Latin America and Africa here .

Background material

Watch the launch event

Consultations

The GEM Report is partnering with Restless Development  to mobilize youth globally to inform the development of the 2023 Youth Report, exploring how technology can address various education challenges.

The GEM Report ran a consultation process to collect feedback and evidence on the proposed lines of research of the 2023 concept note.

Related resources

on technology and education

in quality and school infrastructure

Related content

Monitoring SDG 4: Quality

Is technology good or bad for learning?

Subscribe to the brown center on education policy newsletter, saro mohammed, ph.d. smp saro mohammed, ph.d. partner - the learning accelerator @edresearchworks.

May 8, 2019

I’ll bet you’ve read something about technology and learning recently. You may have read that device use enhances learning outcomes . Or perhaps you’ve read that screen time is not good for kids . Maybe you’ve read that there’s no link between adolescents’ screen time and their well-being . Or that college students’ learning declines the more devices are present in their classrooms .

If ever there were a case to be made that more research can cloud rather than clarify an issue, technology use and learning seems to fit the bill. This piece covers what the research actually says, some outstanding questions, and how to approach the use of technology in learning environments to maximize opportunities for learning and minimize the risk of doing harm to students.

In my recent posts , I have frequently cited the mixed evidence about blended learning, which strategically integrates in-person learning with technology to enable real-time data use, personalized instruction, and mastery-based progression. One thing that this nascent evidence base does show is that technology can be linked to improved learning . When technology is integrated into lessons in ways that are aligned with good in-person teaching pedagogy, learning can be better than without technology.

A 2018 meta-analysis of dozens of rigorous studies of ed tech , along with the executive summary of a forthcoming update (126 rigorous experiments), indicated that when education technology is used to individualize students’ pace of learning, the results overall show “ enormous promise .” In other words, ed tech can improve learning when used to personalize instruction to each student’s pace.

Further, this same meta-analysis, along with other large but correlational studies (e.g., OECD 2015 ), also found that increased access to technology in school was associated with improved proficiency with, and increased use of, technology overall. This is important in light of the fact that access to technology outside of learning environments is still very unevenly distributed across ethnic, socio-economic, and geographic lines. Technology for learning, when deployed to all students, ensures that no student experiences a “21st-century skills and opportunity” gap.

More practically, technology has been shown to scale and sustain instructional practices that would be too resource-intensive to work in exclusively in-person learning environments, especially those with the highest needs. In multiple , large-scale studies where technology has been incorporated into the learning experiences of hundreds of students across multiple schools and school systems, they have been associated with better academic outcomes than comparable classrooms that did not include technology. Added to these larger bodies of research are dozens, if not hundreds, of smaller , more localized examples of technology being used successfully to improve students’ learning experiences. Further, meta-analyses and syntheses of the research show that blended learning can produce greater learning than exclusively in-person learning.

All of the above suggest that technology, used well, can drive equity in learning opportunities. We are seeing that students and families from privileged backgrounds are able to make choices about technology use that maximize its benefits and minimize its risks , while students and families from marginalized backgrounds do not have opportunities to make the same informed choices. Intentional, thoughtful inclusion of technology in public learning environments can ensure that all students, regardless of their ethnicity, socioeconomic status, language status, special education status, or other characteristics, have the opportunity to experience learning and develop skills that allow them to fully realize their potential.

On the other hand, the evidence is decidedly mixed on the neurological impact of technology use. In November 2016, the American Association of Pediatrics updated their screen time guidelines for parents, generally relaxing restrictions and increasing the recommended maximum amount of time that children in different age groups spend interacting with screens. These guidelines were revised not because of any new research, but for two far more practical reasons. First, the nuance of the existing evidence–especially the ways in which recommendations change as children get older–was not adequately captured in the previous guidelines. Second, the proliferation of technology in our lives had made the previous guidelines almost impossible to follow.

The truth is that infants, in particular, learn by interacting with our physical world and with other humans, and it is likely that very early (passive) interactions with devices–rather than humans–can disrupt or misinform neural development . As we grow older, time spent on devices often replaces time spent engaging in physical activity or socially with other people, and it can even become a substitute for emotional regulation, which is detrimental to physical, social, and emotional development.

In adolescence and young adulthood, the presence of technology in learning environments has also been associated with (but has not been shown to be the cause of) negative variables such as attention deficits or hyperactivity , feeling lonely , and lower grades . Multitasking is not something our brains can do while learning , and technology often represents not just one more “task” to have to attend to in a learning environment, but multiple additional tasks due to the variety of apps and programs installed on and producing notifications through a single device.

The pragmatic

The current takeaway from the research is that there are potential benefits and risks to deploying technology in learning environments. While we can’t wrap this topic up with a bow just yet–there are still more questions than answers–there is evidence that technology can amplify effective teaching and learning when in the hands of good teachers. The best we can do today is understand how technology can be a valuable tool for educators to do the complex, human work that is teaching by capitalizing on the benefits while remaining fully mindful of the risks as we currently understand them.

We must continue to build our understanding of both the risks and benefits as we proceed. With that in mind, here are some “Dos” and “Don’ts” for using technology in learning environments:

Related Content

Saro Mohammed, Ph.D.

November 3, 2017

June 16, 2017

September 27, 2017

Education Technology K-12 Education

Governance Studies

Brown Center on Education Policy

Katharine Meyer

May 7, 2024

Jamie Klinenberg, Jon Valant, Nicolas Zerbino

Thinley Choden

May 3, 2024

Writing is a technology that restructures thought — and in an AI age, universities need to teach it more

Senior Lecturer, Faculty of Education, Simon Fraser University

Instructor, English, Kwantlen Polytechnic University

Disclosure statement

Joel Heng Hartse receives funding from the Social Sciences and Humanities Research Council of Canada. He is also president of the Canadian Association for the Study of Discourse and Writing/Association Canadienne de Rédactologie.

Taylor Morphett does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Simon Fraser University provides funding as a member of The Conversation CA.

Simon Fraser University provides funding as a member of The Conversation CA-FR.

View all partners

In an age of AI-assisted writing , is it important for university students to learn how to write?

We believe it is now more than ever.

In the writing classroom, students get the time and help they need to understand writing as not only a skill, but what the language scholar Walter J. Ong called a “ technology that restructures thought .”

“Technology” is not simply iPhones or spreadsheets — it is about mediating our relationship with the world through the creation of tools , and writing itself is arguably the most important tool for thinking that university students need to master.

Perhaps not surprisingly, not everyone agrees.

Role of university writing courses

“Eliminate the Required First-Year Writing Course” was the headline of a provocative article published in Inside Higher Ed in November.

In this article, a professor of writing studies, Melissa Nicolas of Washington State University, writes that while she has seen reason to question how efficient first-year composition courses are before now, “the advent of generative artificial intelligence is the final nail in the coffin.”

In her estimation, “learning to write and writing to learn are two distinct things.” First-year writing courses are “largely about learning to write, but AI can now do this for us. Writing to learn is much more complicated and is something that can only be done by the human mind.”

We take issue with this distinction. From the perspective of human learning and development, the grammatically correct prose produced by generative AI like ChatGPT is not “good writing” — even if it is or seems factually correct — if it does not reflect intellectual engagement with its subject matter. This is not to mention serious questions about the meaning of gaining insight from digital data, issues surrounding data biases, and so on.

First-year composition and other writing courses are a crucial part of the way university students are socialized into ways of communicating that will benefit them far beyond their undergraduate years.

Canadian versus American universities

We propose another solution to the problem Nicolas raises of first-year composition courses being formulaic and outdated. Universities need to devote resources to expanding and improving writing programs, including first-year composition.

We especially need this in Canada, where, as doctoral research carried out by one of the authors of this piece (Taylor Morphett) has shown, first-year composition has traditionally been under-emphasized, and writing has only been taught in a piecemeal way.

When first-year composition courses began to develop at the end of the 19th century in the United States, in Canada the focus was on the fine-tuning of literary taste and the reading of canonical British literature .

The philosophies of education and approaches to teaching that developed from this early time are still present today in Canada. Writing education is often seen by universities as a remedial skill, something students should already know how to do.

In reality, much more writing instruction is needed. Today’s undergraduates are plunged into a sea of texts, information and technology they have immense difficulty navigating , and ChatGPT has made it harder, not easier, for students to discern the credibility of sources.

Writing programs in Canada

In writing courses, students can begin to see the critical variety and power of one of our best technologies: the human act of writing, a system of finite resources but infinite combinations. They learn to think, synthesize, judge the credibility of sources and information and interact with an audience — none of which can be done by AI.

Thankfully, some universities have taken the lead in making writing a cornerstone of undergraduate education. For example, the University of Victoria has a robust academic writing requirement for all students, regardless of their field of study. At the University of Toronto Mississauga, first-year students take an innovative for-credit writing course that takes a “ writing-about-writing ” approach. In this program, undergraduates study writing as an academic subject itself, not just a skill. They learn about the importance, complexity and socially situated nature of academic writing.

Needed at all universities

All Canadian universities should make a beginning academic writing or communication course required for all undergraduates, along with discipline-specific upper-division writing courses focused on scholarly and professional genres in their fields.

Academic and professional writing is a second language for everyone: no one is born knowing how to properly cite sources or craft airtight business proposals.

We need dedicated writing programs to help students understand and communicate complex concepts to a specific audience for a specific purpose in rhetorically flexible ways, with an awareness of their responsibilities to a human community of readers.

Skills and knowledge to make a difference

Generative AI like ChatGPT cannot do this, because it cannot know or “understand” anything . Its raison d'être is to produce plausible strings of symbols in response to human prompts, based on data it has been trained upon.

We have knowledgeable and talented PhDs graduating in communication, applied linguistics, English, rhetoric and related fields whose expertise in these areas is sorely needed at institutions across the country.

If Canada wants to graduate domestic and international students with the skills and knowledge to make a difference in the world, we need to be training them in writing.

• Universities
• undergraduate education

Events and Communications Coordinator

Assistant Editor - 1 year cadetship

Executive Dean, Faculty of Health

Lecturer/Senior Lecturer, Earth System Science (School of Science)

Sydney Horizon Educators (Identified)

This site belongs to UNESCO's International Institute for Educational Planning

IIEP Learning Portal

Search form

• issue briefs
• Improve learning

Information and communication technology (ICT) in education

Information and communications technology (ict) can impact student learning when teachers are digitally literate and understand how to integrate it into curriculum..

Schools use a diverse set of ICT tools to communicate, create, disseminate, store, and manage information.(6) In some contexts, ICT has also become integral to the teaching-learning interaction, through such approaches as replacing chalkboards with interactive digital whiteboards, using students’ own smartphones or other devices for learning during class time, and the “flipped classroom” model where students watch lectures at home on the computer and use classroom time for more interactive exercises.

When teachers are digitally literate and trained to use ICT, these approaches can lead to higher order thinking skills, provide creative and individualized options for students to express their understandings, and leave students better prepared to deal with ongoing technological change in society and the workplace.(18)

ICT issues planners must consider include: considering the total cost-benefit equation, supplying and maintaining the requisite infrastructure, and ensuring investments are matched with teacher support and other policies aimed at effective ICT use.(16)

Issues and Discussion

Digital culture and digital literacy: Computer technologies and other aspects of digital culture have changed the ways people live, work, play, and learn, impacting the construction and distribution of knowledge and power around the world.(14) Graduates who are less familiar with digital culture are increasingly at a disadvantage in the national and global economy. Digital literacy—the skills of searching for, discerning, and producing information, as well as the critical use of new media for full participation in society—has thus become an important consideration for curriculum frameworks.(8)

In many countries, digital literacy is being built through the incorporation of information and communication technology (ICT) into schools. Some common educational applications of ICT include:

• One laptop per child: Less expensive laptops have been designed for use in school on a 1:1 basis with features like lower power consumption, a low cost operating system, and special re-programming and mesh network functions.(42) Despite efforts to reduce costs, however, providing one laptop per child may be too costly for some developing countries.(41)
• Tablets: Tablets are small personal computers with a touch screen, allowing input without a keyboard or mouse. Inexpensive learning software (“apps”) can be downloaded onto tablets, making them a versatile tool for learning.(7)(25) The most effective apps develop higher order thinking skills and provide creative and individualized options for students to express their understandings.(18)
• Interactive White Boards or Smart Boards : Interactive white boards allow projected computer images to be displayed, manipulated, dragged, clicked, or copied.(3) Simultaneously, handwritten notes can be taken on the board and saved for later use. Interactive white boards are associated with whole-class instruction rather than student-centred activities.(38) Student engagement is generally higher when ICT is available for student use throughout the classroom.(4)
• E-readers : E-readers are electronic devices that can hold hundreds of books in digital form, and they are increasingly utilized in the delivery of reading material.(19) Students—both skilled readers and reluctant readers—have had positive responses to the use of e-readers for independent reading.(22) Features of e-readers that can contribute to positive use include their portability and long battery life, response to text, and the ability to define unknown words.(22) Additionally, many classic book titles are available for free in e-book form.
• Flipped Classrooms: The flipped classroom model, involving lecture and practice at home via computer-guided instruction and interactive learning activities in class, can allow for an expanded curriculum. There is little investigation on the student learning outcomes of flipped classrooms.(5) Student perceptions about flipped classrooms are mixed, but generally positive, as they prefer the cooperative learning activities in class over lecture.(5)(35)

ICT and Teacher Professional Development: Teachers need specific professional development opportunities in order to increase their ability to use ICT for formative learning assessments, individualized instruction, accessing online resources, and for fostering student interaction and collaboration.(15) Such training in ICT should positively impact teachers’ general attitudes towards ICT in the classroom, but it should also provide specific guidance on ICT teaching and learning within each discipline. Without this support, teachers tend to use ICT for skill-based applications, limiting student academic thinking.(32) To sup­port teachers as they change their teaching, it is also essential for education managers, supervisors, teacher educators, and decision makers to be trained in ICT use.(11)

Ensuring benefits of ICT investments: To ensure the investments made in ICT benefit students, additional conditions must be met. School policies need to provide schools with the minimum acceptable infrastructure for ICT, including stable and affordable internet connectivity and security measures such as filters and site blockers. Teacher policies need to target basic ICT literacy skills, ICT use in pedagogical settings, and discipline-specific uses. (21) Successful imple­mentation of ICT requires integration of ICT in the curriculum. Finally, digital content needs to be developed in local languages and reflect local culture. (40) Ongoing technical, human, and organizational supports on all of these issues are needed to ensure access and effective use of ICT. (21)

Resource Constrained Contexts: The total cost of ICT ownership is considerable: training of teachers and administrators, connectivity, technical support, and software, amongst others. (42) When bringing ICT into classrooms, policies should use an incremental pathway, establishing infrastructure and bringing in sustainable and easily upgradable ICT. (16) Schools in some countries have begun allowing students to bring their own mobile technology (such as laptop, tablet, or smartphone) into class rather than providing such tools to all students—an approach called Bring Your Own Device. (1)(27)(34) However, not all families can afford devices or service plans for their children. (30) Schools must ensure all students have equitable access to ICT devices for learning.

Inclusiveness Considerations

Digital Divide: The digital divide refers to disparities of digital media and internet access both within and across countries, as well as the gap between people with and without the digital literacy and skills to utilize media and internet.(23)(26)(31) The digital divide both creates and reinforces socio-economic inequalities of the world’s poorest people. Policies need to intentionally bridge this divide to bring media, internet, and digital literacy to all students, not just those who are easiest to reach.

Minority language groups: Students whose mother tongue is different from the official language of instruction are less likely to have computers and internet connections at home than students from the majority. There is also less material available to them online in their own language, putting them at a disadvantage in comparison to their majority peers who gather information, prepare talks and papers, and communicate more using ICT. (39) Yet ICT tools can also help improve the skills of minority language students—especially in learning the official language of instruction—through features such as automatic speech recognition, the availability of authentic audio-visual materials, and chat functions. (2)(17)

Students with different styles of learning: ICT can provide diverse options for taking in and processing information, making sense of ideas, and expressing learning. Over 87% of students learn best through visual and tactile modalities, and ICT can help these students ‘experience’ the information instead of just reading and hearing it. (20)(37) Mobile devices can also offer programmes (“apps”) that provide extra support to students with special needs, with features such as simplified screens and instructions, consistent placement of menus and control features, graphics combined with text, audio feedback, ability to set pace and level of difficulty, appropriate and unambiguous feedback, and easy error correction. (24)(29)

Plans and policies

• India [ PDF ]
• Detroit, USA [ PDF ]
• Finland [ PDF ]
• Alberta Education. 2012. Bring your own device: A guide for schools . Retrieved from http://education.alberta.ca/admin/technology/research.aspx
• Alsied, S.M. and Pathan, M.M. 2015. ‘The use of computer technology in EFL classroom: Advantages and implications.’ International Journal of English Language and Translation Studies . 1 (1).
• BBC. N.D. ‘What is an interactive whiteboard?’ Retrieved from http://www.bbcactive.com/BBCActiveIdeasandResources/Whatisaninteractivewhiteboard.aspx
• Beilefeldt, T. 2012. ‘Guidance for technology decisions from classroom observation.’ Journal of Research on Technology in Education . 44 (3).
• Bishop, J.L. and Verleger, M.A. 2013. ‘The flipped classroom: A survey of the research.’ Presented at the 120th ASEE Annual Conference and Exposition. Atlanta, Georgia.
• Blurton, C. 2000. New Directions of ICT-Use in Education . United National Education Science and Culture Organization (UNESCO).
• Bryant, B.R., Ok, M., Kang, E.Y., Kim, M.K., Lang, R., Bryant, D.P. and Pfannestiel, K. 2015. ‘Performance of fourth-grade students with learning disabilities on multiplication facts comparing teacher-mediated and technology-mediated interventions: A preliminary investigation. Journal of Behavioral Education. 24.
• Buckingham, D. 2005. Educación en medios. Alfabetización, aprendizaje y cultura contemporánea, Barcelona, Paidós.
• Buckingham, D., Sefton-Green, J., and Scanlon, M. 2001. 'Selling the Digital Dream: Marketing Education Technologies to Teachers and Parents.'  ICT, Pedagogy, and the Curriculum: Subject to Change . London: Routledge.
• "Burk, R. 2001. 'E-book devices and the marketplace: In search of customers.' Library Hi Tech 19 (4)."
• Chapman, D., and Mählck, L. (Eds). 2004. Adapting technology for school improvement: a global perspective. Paris: International Institute for Educational Planning.
• Cheung, A.C.K and Slavin, R.E. 2012. ‘How features of educational technology applications affect student reading outcomes: A meta-analysis.’ Educational Research Review . 7.
• Cheung, A.C.K and Slavin, R.E. 2013. ‘The effectiveness of educational technology applications for enhancing mathematics achievement in K-12 classrooms: A meta-analysis.’ Educational Research Review . 9.
• Deuze, M. 2006. 'Participation Remediation Bricolage - Considering Principal Components of a Digital Culture.' The Information Society . 22 .
• Dunleavy, M., Dextert, S. and Heinecke, W.F. 2007. ‘What added value does a 1:1 student to laptop ratio bring to technology-supported teaching and learning?’ Journal of Computer Assisted Learning . 23.
• Enyedy, N. 2014. Personalized Instruction: New Interest, Old Rhetoric, Limited Results, and the Need for a New Direction for Computer-Mediated Learning . Boulder, CO: National Education Policy Center.
• Golonka, E.M., Bowles, A.R., Frank, V.M., Richardson, D.L. and Freynik, S. 2014. ‘Technologies for foreign language learning: A review of technology types and their effectiveness.’ Computer Assisted Language Learning . 27 (1).
• Goodwin, K. 2012. Use of Tablet Technology in the Classroom . Strathfield, New South Wales: NSW Curriculum and Learning Innovation Centre.
• Jung, J., Chan-Olmsted, S., Park, B., and Kim, Y. 2011. 'Factors affecting e-book reader awareness, interest, and intention to use.' New Media & Society . 14 (2)
• Kenney, L. 2011. ‘Elementary education, there’s an app for that. Communication technology in the elementary school classroom.’ The Elon Journal of Undergraduate Research in Communications . 2 (1).
• Kopcha, T.J. 2012. ‘Teachers’ perceptions of the barriers to technology integration and practices with technology under situated professional development.’ Computers and Education . 59.
• Miranda, T., Williams-Rossi, D., Johnson, K., and McKenzie, N. 2011. "Reluctant readers in middle school: Successful engagement with text using the e-reader.' International journal of applied science and technology . 1 (6).
• Moyo, L. 2009. 'The digital divide: scarcity, inequality and conflict.' Digital Cultures . New York: Open University Press.
• Newton, D.A. and Dell, A.G. 2011. ‘Mobile devices and students with disabilities: What do best practices tell us?’ Journal of Special Education Technology . 26 (3).
• Nirvi, S. (2011). ‘Special education pupils find learning tool in iPad applications.’ Education Week . 30 .
• Norris, P. 2001. Digital Divide: Civic Engagement, Information Poverty, and the Internet Worldwide . Cambridge, USA: Cambridge University Press.
• Project Tomorrow. 2012. Learning in the 21st century: Mobile devices + social media = personalized learning . Washington, D.C.: Blackboard K-12.
• Riasati, M.J., Allahyar, N. and Tan, K.E. 2012. ‘Technology in language education: Benefits and barriers.’ Journal of Education and Practice . 3 (5).
• Rodriquez, C.D., Strnadova, I. and Cumming, T. 2013. ‘Using iPads with students with disabilities: Lessons learned from students, teachers, and parents.’ Intervention in School and Clinic . 49 (4).
• Sangani, K. 2013. 'BYOD to the classroom.' Engineering & Technology . 3 (8).
• Servon, L. 2002. Redefining the Digital Divide: Technology, Community and Public Policy . Malden, MA: Blackwell Publishers.
• Smeets, E. 2005. ‘Does ICT contribute to powerful learning environments in primary education?’ Computers and Education. 44 .
• Smith, G.E. and Thorne, S. 2007. Differentiating Instruction with Technology in K-5 Classrooms . Eugene, OR: International Society for Technology in Education.
• Song, Y. 2014. '"Bring your own device (BYOD)" for seamless science inquiry in a primary school.' Computers & Education. 74 .
• Strayer, J.F. 2012. ‘How learning in an inverted classroom influences cooperation, innovation and task orientation.’ Learning Environment Research. 15.
• Tamim, R.M., Bernard, R.M., Borokhovski, E., Abrami, P.C. and Schmid, R.F. 2011. ‘What forty years of research says about the impact of technology on learning: A second-order meta-analysis and validation study. Review of Educational Research. 81 (1).
• Tileston, D.W. 2003. What Every Teacher Should Know about Media and Technology. Thousand Oaks, CA: Corwin Press.
• Turel, Y.K. and Johnson, T.E. 2012. ‘Teachers’ belief and use of interactive whiteboards for teaching and learning.’ Educational Technology and Society . 15(1).
• Volman, M., van Eck, E., Heemskerk, I. and Kuiper, E. 2005. ‘New technologies, new differences. Gender and ethnic differences in pupils’ use of ICT in primary and secondary education.’ Computers and Education. 45 .
• Voogt, J., Knezek, G., Cox, M., Knezek, D. and ten Brummelhuis, A. 2013. ‘Under which conditions does ICT have a positive effect on teaching and learning? A call to action.’ Journal of Computer Assisted Learning. 29 (1).
• Warschauer, M. and Ames, M. 2010. ‘Can one laptop per child save the world’s poor?’ Journal of International Affairs. 64 (1).
• Zuker, A.A. and Light, D. 2009. ‘Laptop programs for students.’ Science. 323 (5910).

Related information

• Information and communication technologies (ICT)

• Share full article

Advertisement

Supported by

current events conversation

What Students Are Saying About Tech in the Classroom

Does technology help students be more organized, efficient and prepared for the future? Or is it just a distraction?

By The Learning Network

Is there a problem with screens in schools?

We invited students to weigh in on that question in our Picture Prompt Tech in the Classroom , which was based on an Opinion essay arguing that we should “get tech out of the classroom before it’s too late.”

Is there too much tech in your school day? — we asked students. Would you prefer more screen-free time while you are learning, or even during lunch or free periods?

Below, they share the good, the bad and the ugly about technology use in school.

Thank you to everyone who participated in the conversation on our writing prompts this week!

Please note: Student comments have been lightly edited for length.

Some students saw the value of technology in schools, including its ability to prepare students for the future.

I believe that technology in the classroom is a good thing when it is properly moderated. I think completely taking away screens from a student will not help them develop computer skills which they will most likely need in a world like ours, where most of everything is online. Sometimes phones cannot get the job done, and computers will be needed. If schools completely remove devices from the curriculum, then students will be completely clueless when they take classes involving a computer. Too much screen time can be bad for the student, but if it is well moderated, then screen time won’t be an issue.

— Saheed, GMS

I personally do not mind the amount of technology in the classroom. I personally find typing to be a lot easier instead of writing. On top of that, this amount of technology is used in adults’ day to day lives, too. Writing has become less and less relevant for everyone, because most jobs require a computer nowadays. So I think it’s actually better to have the amount of technology we do in the classroom.

— Timothy, Greenbelt Middle

They said, even though there might be down sides, the good outweighs the bad.

Screens in the classroom allows students to complete work in a more organized manner and use online resources to help them learn. It helps teachers to be able to make sure students turn work in before a certain time. However, having screens in the classroom raises students overall screen time which is bad for their eye health and sleep.

— Emily, Greenbelt Middle

I believe that computers should definitely be used at school because it has more pros than cons. They help with everything. The only problem with them is the people using them. The people using them are often misusing them and not charging them.

— Deegan, California

And they argued that tech is so entrenched in the student experience that taking it away would cause a lot of disruption.

There are no problems with screens in school. I believe without screens, school would be much less productive, produce so much waste of paper, and assignments would be lost a lot. Also when I have paper homework, which is almost never, almost every time I get it I forget because everything is on the iPad. This is important because if there is any change in the iPads we use, it’ll affect everyone drastically. Also it would just be really annoying to get used to a whole new thing.

— August, GBW

But another contingent of students said, “There is definitely a problem with screens in school.” They called them a distraction.

There is definitely a problem with screens in school. While regular technology use in school is highly efficient and much more convenient than using textbooks and paper, I still feel like using technology as the main method for learning is detrimental. There are plenty of students in my classes who are hiding behind their iPads to play games or go on their phones rather than utilizing their technology to enhance their learning experience. So in turn, I think we need to minimize (but not completely take away) the prominence of tech in our classrooms. This matters because it’s so important for students to learn how to completely pay attention and focus in on one task so that they are prepared for the moments in life where they don’t get the opportunity to look at their phone if they’re bored or to text their friends. Trust me, this may seem like I’m one hundred percent anti-phones but the truth is I love my phone and am somewhat addicted to it, so I realize that it’s a major distraction for myself in the classroom. Moreover, staring at an iPad screen for 7 hours a day puts significant strain on our eyes, so for the sake of our health and our attention spans, we need to minimize tech use in school.

— Mary, Glenbard West High School

Tech inside classrooms has had many positive effects and many negative effects. Without technology, it would take forever to find sources/information and it would also take ages to do complex things. With technology, people can easily find information and they can easily do many things but the big downside is that they can easily just search up games and get distracted. On one side, it has provided many different changes to students so they can learn in a fun and entertaining way but in another, people are mostly on their phones scrolling through YouTube or Instagram. Many people don’t have control over their body and have a big urge to go on their cellphones.

— Srikanth, Greenbelt Middle School

In my opinion, yes there is a problem with screens in schools. It distracts kids from focusing on their work. Many students are always on their phone during class, and it is disrespectful as well as sad for them. They will not be able to learn the material that is being taught. Personally, I think that screens should be reduced in class, but I do not think that is possible. Whenever a teacher takes away someone’s phone, they get very mad and say that it is their right to have their phone. In these cases it is very confusing on how to act for the teacher!

— Kadambari, gms

Some reported that their peers use technology to cheat.

It might be a problem depending on what people are doing. If it is used for school, like typing an essay, working on homework, or checking your grades it’s okay, but I know people who abuse this privilege. They go onto YouTube and watch things, listen to music when they aren’t supposed to, and play games. Many people cheat to the point where it takes forever to start a test because people don’t close out their tabs. It helps to be able to do these ‘Quick Writes’ as we call them in my ELA class because I can write faster (I know it’s called typing). It’s harder to access things because of the restriction because people mess around so they block so many useful websites and words from our computer. I like to type on the computer, but I feel people abuse this privilege too much.

— Nina, California

When the teachers assign tests on computers, sometimes teachers have to lock students’ screens to make sure they’re not cheating. Sometimes they do it on paper and they try to cheat while hiding their phones in their laps. And then if another student sees them doing that, they will tell and the student who would have the phone out could start a big argument.

— Taylor, Huntington Beach

Several lamented the sheer number of hours teenagers spend in front of screens.

I feel that we have become too comfortable with using screens for nearly every lesson in school, because it has gotten to the point where we are spending upwards of 4 hours on our laptops in school alone. I understand that it would be hard to switch back to using journals and worksheets, but it would be very beneficial for kids if we did.

— Chase, school

I think we should reduce the tech a little just because most students are going straight to screens when they get home, after a full day of screens … Although I know this would be very difficult to do because everything in the world now seems to go online.

— Jaydin, California

And they even worried about their handwriting in a world full of typing.

I think technology in a class is very helpful, but I think that we should incorporate more writing. Since the pandemic, most of the work has been online and it never gave students the opportunity to write as much. When we came back from lockdown, I almost forgot how to write with a pencil. My handwriting was very different. And now we don’t get much time to write with our hands so I think we should have fewer screens.

— Eric, Greenbelt

Some students said that less time spent on screens in school would give them a break from the always-on digital culture they live in.

Although typing is useful and using the internet is very useful, I think we should go back to how it was about 20-40 years ago when all people used the computer for was to type an essay. Drama didn’t get spread in a millisecond, we didn’t have to worry as much about stereotypes. Now all kids want to do is text each other and watch videos. I’m well aware that I have fallen into this trap and I want out, but our lives revolve around technology. You can’t get away from it. I know this is about schools not using technology, which the world without it would be impossible now, but life would be so much simpler again.

— Ivy, Huntington Beach, CA

I will say that my phone is usually always with me during school hours, but I don’t use it all the time. I may check the time or play a short game as a brain break. But I do see some people absolutely glued to their phones during class time, and it’s honestly embarrassing. You really can’t go without your phone for an hour?? It’s almost like an addiction at this point. I understand using your phone to quickly distract yourself; I do it too. And I also think it’s okay to have your phone/electronic during lunch time or free periods. But using it to the point that you can’t properly pay attention in class is just embarrassing. So, in summary, I do think that schools are having a problem with screens.

— Allison, Greenbelt Middle School

And they named classes in which they think screens do and do not have a place.

I feel like for classes for younger kids, technology is definitely not good. Kids should be playing, using their hands, and actually experiencing things instead of being on tablets in kindergarten. I think using computers in school is good though. It’s a lot more efficient, and we live in a society where fast and efficient things are the trend.

— sarah, maryland

I think screens have their place, and will always have their place, in schools and education. The capabilities of computers will always surpass anything else, and they should not be banned from school environments. Still, I have one exception: English class. Other than final drafts of essays, everything in English should be on paper. You can formulate ideas better and minimize outside influence on your thinking.

— Addie, The Potomac School

Learn more about Current Events Conversation here and find all of our posts in this column .

Featured Topics

Featured series.

A series of random questions answered by Harvard experts.

Explore the Gazette

Read the latest.

Epic science inside a cubic millimeter of brain

Complex questions, innovative approaches

Early warning sign of extinction?

What is ‘original scholarship’ in the age of ai.

Melissa Dell (from left), Alex Csiszar, and Latanya Sweeney.

Photos by Stephanie Mitchell/Harvard Staff Photographer

Anne J. Manning

Harvard Staff Writer

Symposium considers how technology is changing academia

While moderating a talk on artificial intelligence last week, Latanya Sweeney posed a thought experiment. Picture three to five years from now. AI companies are continuing to scrape the internet for data to feed their large language models. But unlike today’s internet, which is largely human-generated content, most of that future internet’s content has been generated by … large language models.

The scenario is not farfetched considering the explosive growth of generative AI in the last two years, suggested the Faculty of Arts and Sciences and Harvard Kennedy School professor.  

Sweeney’s panel was part of a daylong symposium on AI hosted by the FAS last week that considered questions such as: How are generative AI technologies such as ChatGPT disrupting what it means to own one’s work? How can AI be leveraged thoughtfully while maintaining academic and research integrity? Just how good are these large language model-based programs going to get? (Very, very good.)

“Here at the FAS, we’re in a unique position to explore questions and challenges that come from this new technology,” said Hopi Hoekstra , Edgerley Family Dean of the Faculty of Arts and Sciences, during her opening remarks. “Our community is full of brilliant thinkers, curious researchers, and knowledgeable scholars, all able to lend their variety of expertise to tackling the big questions in AI, from ethics to societal implications.”

In an all-student panel, philosophy and math concentrator Chinmay Deshpande ’24 compared the present moment to the advent of the internet, and how that revolutionary technology forced academic institutions to rethink how to test knowledge. “Regardless of what we think AI will look like down the line, I think it’s clear it’s starting to have an impact that’s qualitatively similar to the impact of the internet,” Deshpande said. “And thinking about pedagogy, we should think about AI along somewhat similar lines.”

Computer science concentrator and master’s degree student Naomi Bashkansky ’25, who is exploring AI safety issues with fellow students, urged Harvard to provide thought leadership on the implications of an AI-saturated world, in part by offering courses that integrate the basics of large language models into subjects like biology or writing.

Harvard Law School student Kevin Wei agreed.

“We’re not grappling sufficiently with the way the world will change, and especially the way the economy and labor market will change, with the rise of generative AI systems,” Wei said. “Anything Harvard can do to take a leading role in doing that … in discussions with government, academia, and civil society … I would like to see a much larger role for the University.”

The day opened with a panel on original scholarship, co-sponsored by the Mahindra Humanities Center and the Edmond & Lily Safra Center for Ethics . Panelists explored ethics of authorship in the age of instant access to information and blurred lines of citation and copyright, and how those considerations vary between disciplines.

David Joselit , the Arthur Kingsley Professor of Art, Film, and Visual Studies, said challenges wrought by AI have precedent in the history of art; the idea of “authorship” has been undermined in the modern era because artists have often focused on the idea as what counts as the artwork, rather than its physical execution. “It seems to me that AI is a mechanization of that kind of distribution of authorship,” Joselit said. He posed the idea that AI should be understood “as its own genre, not exclusively as a tool.”

Another symposium topic included a review of Harvard Library’s law, information policy, and AI survey research revealing how students are using AI for academic work. Administrators from across the FAS also shared examples of how they are experimenting with AI tools to enhance their productivity. Panelists from the Bok Center shared how AI has been used in teaching this year, and Harvard University Information Technology gave insight into tools it is building to support instructors. 

Throughout the ground floor of the Northwest Building, where the symposium took place, was a poster fair keying off final projects from Sweeney’s course “Tech Science to Save the World,” in which students explored how scientific experimentation and technology can be used to solve real-world problems. Among the posters: “Viral or Volatile? TikTok and Democracy,” and “Campaign Ads in the Age of AI: Can Voters Tell the Difference?”

Students from the inaugural General Education class “ Rise of the Machines? ” capped the day, sharing final projects illustrating current and future aspects of generative AI.

Share this article

You might like.

Researchers publish largest-ever dataset of neural connections

Seven projects awarded Star-Friedman Challenge grants

Fossil record stretching millions of years shows tiny ocean creatures on the move before Earth heats up

Excited about new diet drug? This procedure seems better choice.

Study finds minimally invasive treatment more cost-effective over time, brings greater weight loss

How far has COVID set back students?

An economist, a policy expert, and a teacher explain why learning losses are worse than many parents realize

Technology Used in Education Today

Today's education relies on diverse tech tools, from interactive whiteboards to online platforms, enhancing accessibility, engagement, and efficiency.

Key points:

• Today’s edtech creates a dynamic, inclusive, and effective learning environment
• Edtech tools are versatile and applicable to many learning scenarios
• Stay up-to-date on higher ed tech innovation news

In today’s educational landscape, technology plays a pivotal role, transforming teaching, learning, and administrative processes. From interactive whiteboards and educational apps to online learning platforms and data analytics, higher ed tech innovation news  illustrates how technology offers innovative tools and methods to enhance accessibility, engagement, and efficiency in education. These advancements have revolutionized traditional classrooms, providing personalized, interactive, and flexible learning experiences that cater to diverse student needs and preferences.

As technology continues to evolve, its integration into education today shapes a more dynamic, inclusive, and effective learning environment, preparing students for success in an increasingly digital world.

What is the use of technology in education today?

Technology in education today serves various purposes, enhancing teaching, learning, and administrative processes. Firstly, it expands access to education through online learning platforms, enabling students to access educational materials anytime, anywhere, and facilitating distance learning. This accessibility is particularly beneficial for non-traditional learners, those with busy schedules, or those living in remote areas.

Secondly, technology fosters engagement by providing interactive and multimedia-rich learning experiences. Interactive whiteboards, educational apps, and virtual simulations make learning more dynamic and enjoyable, catering to diverse learning styles and preferences. This increased engagement leads to improved retention and understanding of complex concepts.

Moreover, technology enables personalized learning experiences tailored to individual student needs and abilities. Adaptive learning systems use data analytics to track student progress and provide targeted interventions, ensuring that each student receives the support they need to succeed.

Additionally, technology facilitates collaboration among students and educators, transcending geographical barriers. Virtual classrooms, discussion forums, and collaborative documents enable meaningful interactions and knowledge sharing, fostering a sense of community and global connectivity.

Furthermore, technology streamlines administrative tasks such as attendance tracking, grading, and course management, improving efficiency and freeing up time for educators to focus on teaching.

Overall, looking at technology in the classroom, examples show how tech democratizes learning, making it more accessible, engaging, and efficient. By leveraging technology effectively, educators can create dynamic learning environments that empower students to reach their full potential, preparing them for success in the digital age.

What is the latest technology used in education?

The latest technology used in education encompasses a range of innovative tools and platforms designed to enhance teaching, learning, and administrative processes. Examples of technology used in education today include artificial intelligence (AI), which offers personalized learning experiences through adaptive algorithms. AI-powered tutoring systems provide real-time feedback and support, improving student outcomes and engagement.

Another emerging technology is augmented reality (AR), which overlays digital content onto the physical world, creating immersive and interactive learning experiences. AR applications allow students to visualize abstract concepts, explore virtual environments, and engage in hands-on learning activities.

Moreover, virtual reality (VR) technology creates immersive learning environments that simulate real-world scenarios, providing opportunities for experiential learning and skill development. VR simulations can be used in various subjects, from science and engineering to history and literature.

Additionally, blockchain technology is gaining traction in education for secure credentialing and verification. Blockchain-based platforms enable transparent record-keeping and secure sharing of academic credentials, reducing fraud and ensuring the integrity of educational records.

Furthermore, learning analytics utilizes data to provide insights into student learning behaviors and performance. By tracking student progress and identifying areas for improvement, educators can personalize instruction and interventions, optimizing learning experiences.

Overall, the latest technology in education offers opportunities to revolutionize teaching and learning, making it more personalized, immersive, and efficient. By embracing these advancements, educators can create dynamic learning environments that prepare students for success in the digital age.

What is an example of technology currently used in education today?

One prominent example of technology currently used in education is the Learning Management System (LMS). LMS platforms, such as Moodle, Canvas, or Blackboard, serve as central hubs for course administration, content delivery, and student interaction.

Educators utilize LMS to upload course materials, such as lecture slides, readings, and multimedia resources, making them accessible to students anytime, anywhere. These platforms also facilitate communication between students and instructors through discussion forums, messaging, and announcement features.

Moreover, LMS offer tools for assessment and feedback, allowing educators to create quizzes, assignments, and exams, and provide timely feedback on student submissions. Additionally, LMS often integrate with other educational technologies, such as video conferencing tools for virtual lectures or plagiarism detection software for assignment submissions.

For students, LMS provide a centralized location to access course materials, track progress, and communicate with peers and instructors. They offer flexibility for remote learning and self-paced study, enhancing accessibility and convenience.

Overall, this is one of many examples of educational technology tools that exemplify how technology is used in education today to streamline administrative tasks, facilitate communication and collaboration, and enhance the learning experience for students and educators alike.

How technology helps in teaching and learning

Technology plays a vital role in modern teaching and learning by providing innovative tools and resources that enhance engagement, accessibility, and effectiveness. Interactive whiteboards, multimedia presentations, and educational apps enable educators to deliver dynamic and engaging lessons that cater to diverse learning styles and preferences. These interactive tools facilitate active learning and encourage student participation, leading to improved understanding and retention of complex concepts.

Moreover, technology offers personalized learning experiences tailored to individual student needs and abilities. Adaptive learning systems use data analytics to track student progress and provide targeted interventions, ensuring that each student receives the support they need to succeed. Additionally, educational software and online platforms offer opportunities for self-paced learning and exploration, empowering students to take ownership of their learning journey.

Furthermore, technology fosters collaboration among students and educators, transcending geographical barriers. Virtual classrooms, discussion forums, and collaborative documents enable meaningful interactions and knowledge sharing, promoting critical thinking and problem-solving skills.

Overall, technology used in education today enhances teaching and learning by making education more dynamic, accessible, and student-centered. By leveraging technology effectively, educators can create engaging learning experiences that prepare students for success in the digital age.

What technology is being used in schools today?

Schools today are utilizing a diverse range of technology to enhance teaching, learning, and administrative processes. One of many prominent examples of technology used in education is interactive whiteboards, which enable educators to deliver dynamic and engaging lessons through multimedia presentations and interactive activities. Additionally, educational software and apps provide access to a wealth of educational resources, from digital textbooks to interactive learning modules, catering to diverse student needs and preferences.

Learning management systems (LMS) serve as central hubs for course administration, content delivery, and communication between students and instructors. These platforms streamline administrative tasks such as attendance tracking, grading, and assignment submission, improving efficiency and organization.

Moreover, schools are increasingly adopting one-to-one device programs, providing students with laptops or tablets to facilitate digital learning both in and out of the classroom. These devices offer opportunities for personalized learning experiences, collaborative projects, and access to online resources.

Furthermore, schools are integrating technology to support special education and inclusive practices. Assistive technology tools such as text-to-speech software, speech recognition programs, and adaptive keyboards assist students with disabilities in accessing educational materials and participating in classroom activities.

Additionally, schools are leveraging video conferencing tools to facilitate virtual classrooms, guest lectures, and remote learning opportunities. This technology enables students to connect with peers and experts worldwide, fostering global collaboration and cultural exchange.

Overall, technology in schools today encompasses a wide range of tools and platforms that enhance accessibility, engagement, and efficiency, ultimately shaping a more dynamic and inclusive learning environment for students and educators alike.

What is the use of educational technology?

Educational technology encompasses a wide range of tools, resources, and platforms designed to enhance teaching, learning, and administrative processes in education. The primary use of educational technology is to facilitate and improve the learning experience for students. Through interactive multimedia presentations, educational apps, and online learning platforms, educational technology offers dynamic and engaging ways to deliver content, cater to diverse learning styles, and promote active participation in the learning process. It’s easy to see the importance of technology in the classroom.

Additionally, educational technology enables personalized learning experiences tailored to individual student needs and abilities. Adaptive learning systems use data analytics to track student progress, identify areas for improvement, and provide targeted interventions, ensuring that each student receives the support they need to succeed.

Furthermore, educational technology fosters collaboration among students and educators, transcending geographical barriers. Virtual classrooms, discussion forums, and collaborative documents enable meaningful interactions and knowledge sharing, promoting critical thinking and problem-solving skills.

Moreover, educational technology streamlines administrative tasks such as course management, grading, and communication, improving efficiency and organization for educators and administrators.

Overall, the use of educational technology enhances accessibility, engagement, and efficiency in education, ultimately preparing students for success in the digital age. By leveraging technology effectively, educators can create dynamic and inclusive learning environments that empower students to reach their full potential.

How technology has changed the way students learn today

Technology has significantly transformed the way students learn today, revolutionizing traditional teaching methods and providing innovative opportunities for engagement, collaboration, and personalized learning experiences.

Firstly, when it comes to the impact of technology on education, technology has made learning more accessible and flexible. With the proliferation of online learning platforms, students can access educational materials anytime, anywhere, breaking down geographical barriers and accommodating diverse learning styles and preferences. This flexibility enables students to learn at their own pace and engage with content in ways that best suit their individual needs.

Moreover, technology has enhanced engagement and interaction in the learning process. Interactive multimedia presentations, educational apps, and virtual simulations offer dynamic and immersive learning experiences, fostering active participation and increasing student motivation and retention of information.

Additionally, technology facilitates collaboration and communication among students and educators. Virtual classrooms, discussion forums, and collaborative documents enable meaningful interactions and knowledge sharing, promoting critical thinking, problem-solving skills, and social-emotional learning.

Furthermore, technology enables personalized learning experiences tailored to individual student needs and abilities. Adaptive learning systems use data analytics to track student progress, identify areas for improvement, and provide targeted interventions, ensuring that each student receives the support they need to succeed.

Overall, technology has changed the way students learn today by making learning more accessible, engaging, and personalized, ultimately preparing students for success in the digital age.

What is an example of technology as a learning tool?

One exemplary technology serving as a learning tool is the interactive whiteboard. This innovative device highlights the benefits of technology in education as it combines traditional whiteboard capabilities with digital features, creating dynamic and engaging learning experiences for students.

Interactive whiteboards allow educators to present information in a multimedia format, integrating text, images, videos, and interactive elements into their lessons. This multimedia approach caters to diverse learning styles, increasing student engagement and comprehension of complex concepts.

Moreover, interactive whiteboards promote active participation in the learning process. Students can interact directly with the board, manipulating digital content, solving problems, and collaborating with peers. This hands-on approach fosters critical thinking, problem-solving skills, and social-emotional learning.

Additionally, interactive whiteboards offer opportunities for differentiation and personalized learning. Educators can adapt their lessons to accommodate students’ individual needs and abilities, providing targeted interventions and support where necessary. For example, interactive whiteboards can be used to create interactive games, quizzes, and simulations that cater to varying skill levels and learning preferences.

Furthermore, interactive whiteboards facilitate formative assessment and feedback. Educators can instantly assess student understanding through interactive activities and quizzes, providing timely feedback to guide instruction and support student learning.

Overall, interactive whiteboards exemplify how technology can enhance teaching and learning by providing dynamic, interactive, and personalized learning experiences that engage students, promote collaboration, and facilitate meaningful learning outcomes.

Technology has become an integral part of education, revolutionizing teaching, learning, and administrative processes. Its diverse tools and platforms enhance accessibility, engagement, and efficiency, shaping a dynamic and inclusive learning environment. Embracing technology empowers educators and prepares students for success in the digital age.

Sign up for our newsletter

• Recent Posts

• Chief Releases Fit Mobile Cart for Interactive Displays - May 6, 2024
• Higher Ed Tech Innovation News - April 25, 2024
• How is Technology Used in Higher Education? - April 25, 2024

Username or Email Address

Remember Me

For IEEE Members

Ieee spectrum, follow ieee spectrum, support ieee spectrum, enjoy more free content and benefits by creating an account, saving articles to read later requires an ieee spectrum account, the institute content is only available for members, downloading full pdf issues is exclusive for ieee members, downloading this e-book is exclusive for ieee members, access to spectrum 's digital edition is exclusive for ieee members, following topics is a feature exclusive for ieee members, adding your response to an article requires an ieee spectrum account, create an account to access more content and features on ieee spectrum , including the ability to save articles to read later, download spectrum collections, and participate in conversations with readers and editors. for more exclusive content and features, consider joining ieee ., join the world’s largest professional organization devoted to engineering and applied sciences and get access to all of spectrum’s articles, archives, pdf downloads, and other benefits. learn more →, join the world’s largest professional organization devoted to engineering and applied sciences and get access to this e-book plus all of ieee spectrum’s articles, archives, pdf downloads, and other benefits. learn more →, access thousands of articles — completely free, create an account and get exclusive content and features: save articles, download collections, and talk to tech insiders — all free for full access and benefits, join ieee as a paying member., ai copilots are changing how coding is taught, professors are shifting away from syntax and emphasizing higher-level skills.

Generative AI is transforming the software development industry. AI-powered coding tools are assisting programmers in their workflows, while jobs in AI continue to increase. But the shift is also evident in academia—one of the major avenues through which the next generation of software engineers learn how to code.

Computer science students are embracing the technology, using generative AI to help them understand complex concepts, summarize complicated research papers, brainstorm ways to solve a problem, come up with new research directions, and, of course, learn how to code.

“Students are early adopters and have been actively testing these tools,” says Johnny Chang , a teaching assistant at Stanford University pursuing a master’s degree in computer science. He also founded the AI x Education conference in 2023, a virtual gathering of students and educators to discuss the impact of AI on education.

So as not to be left behind, educators are also experimenting with generative AI. But they’re grappling with techniques to adopt the technology while still ensuring students learn the foundations of computer science.

“It’s a difficult balancing act,” says Ooi Wei Tsang , an associate professor in the School of Computing at the National University of Singapore . “Given that large language models are evolving rapidly, we are still learning how to do this.”

Less Emphasis on Syntax, More on Problem Solving

The fundamentals and skills themselves are evolving. Most introductory computer science courses focus on code syntax and getting programs to run, and while knowing how to read and write code is still essential, testing and debugging—which aren’t commonly part of the syllabus—now need to be taught more explicitly.

“We’re seeing a little upping of that skill, where students are getting code snippets from generative AI that they need to test for correctness,” says Jeanna Matthews , a professor of computer science at Clarkson University in Potsdam, N.Y.

Another vital expertise is problem decomposition. “This is a skill to know early on because you need to break a large problem into smaller pieces that an LLM can solve,” says Leo Porter , an associate teaching professor of computer science at the University of California, San Diego . “It’s hard to find where in the curriculum that’s taught—maybe in an algorithms or software engineering class, but those are advanced classes. Now, it becomes a priority in introductory classes.”

“Given that large language models are evolving rapidly, we are still learning how to do this.” —Ooi Wei Tsang, National University of Singapore

As a result, educators are modifying their teaching strategies. “I used to have this singular focus on students writing code that they submit, and then I run test cases on the code to determine what their grade is,” says Daniel Zingaro , an associate professor of computer science at the University of Toronto Mississauga . “This is such a narrow view of what it means to be a software engineer, and I just felt that with generative AI, I’ve managed to overcome that restrictive view.”

Zingaro, who coauthored a book on AI-assisted Python programming with Porter, now has his students work in groups and submit a video explaining how their code works. Through these walk-throughs, he gets a sense of how students use AI to generate code, what they struggle with, and how they approach design, testing, and teamwork.

“It’s an opportunity for me to assess their learning process of the whole software development [life cycle]—not just code,” Zingaro says. “And I feel like my courses have opened up more and they’re much broader than they used to be. I can make students work on larger and more advanced projects.”

Ooi echoes that sentiment, noting that generative AI tools “will free up time for us to teach higher-level thinking—for example, how to design software, what is the right problem to solve, and what are the solutions. Students can spend more time on optimization, ethical issues, and the user-friendliness of a system rather than focusing on the syntax of the code.”

Avoiding AI’s Coding Pitfalls

But educators are cautious given an LLM’s tendency to hallucinate . “We need to be teaching students to be skeptical of the results and take ownership of verifying and validating them,” says Matthews.

Matthews adds that generative AI “can short-circuit the learning process of students relying on it too much.” Chang agrees that this overreliance can be a pitfall and advises his fellow students to explore possible solutions to problems by themselves so they don’t lose out on that critical thinking or effective learning process. “We should be making AI a copilot—not the autopilot—for learning,” he says.

“We should be making AI a copilot—not the autopilot—for learning.” —Johnny Chang, Stanford University

Other drawbacks include copyright and bias. “I teach my students about the ethical constraints—that this is a model built off other people’s code and we’d recognize the ownership of that,” Porter says. “We also have to recognize that models are going to represent the bias that’s already in society.”

Adapting to the rise of generative AI involves students and educators working together and learning from each other. For her colleagues, Matthews’s advice is to “try to foster an environment where you encourage students to tell you when and how they’re using these tools. Ultimately, we are preparing our students for the real world, and the real world is shifting, so sticking with what you’ve always done may not be the recipe that best serves students in this transition.”

Porter is optimistic that the changes they’re applying now will serve students well in the future. “There’s this long history of a gap between what we teach in academia and what’s actually needed as skills when students arrive in the industry,” he says. “There’s hope on my part that we might help close the gap if we embrace LLMs.”

• How Coders Can Survive—and Thrive—in a ChatGPT World ›
• AI Coding Is Going From Copilot to Autopilot ›
• OpenAI Codex ›

Rina Diane Caballar is a writer covering tech and its intersections with science, society, and the environment. An IEEE Spectrum Contributing Editor, she's a former software engineer based in Wellington, New Zealand.

Yes! Great summary of how things are evolving with AI. I’m a retired coder (BS comp sci) and understand the fundamentals of developing systems. Learning the lastest systems is now the greatest challenge. I was intrigued by Ansible to help me manage my homelab cluster, but who wants to learn one more scripting language? Turns out ChatGPT4 knows the syntax, semantics, and work flow of Ansible and all I do is tell is to “install log2ram on all my proxmox servers” and I get a playbook that does just that. The same with Docker Compose scripts. Wow.

Disney's Robots Use Rockets to Stick the Landing

Video friday: robot bees, the new shadow hand can take a beating, related stories, ai spam threatens the internet—ai can also protect it, what is generative ai, generative ai has a visual plagiarism problem.

• Teaching with Technology

Five Tips for Writing Academic Integrity Statements in the Age of AI 

• May 10, 2024
• Torrey Trust, PhD

Author Rie Kudan received a prestigious Japanese literary award for her book, The Tokyo Tower of Sympathy, and then disclosed that 5% of her book was written word-for-word by ChatGPT (Choi & Annio, 2024).  

Would you let your students submit a paper where 5% of the text was written by ChatGPT?  

What about if they disclosed their use of ChatGPT ahead of time?  

Or, if they cited ChatGPT as a source?  

The rise of generative AI tools like ChatGPT, Copilot, Claude, Gemini, DALL-E, and Meta AI has created a pressing new challenge for educators: defining academic integrity in the age of AI.  

As educators and students grapple with what is allowed when using generative AI (GenAI) tools, I have compiled five tips to help you design or redesign academic integrity statements for your syllabus, assignments, exams, and course activities.  

1. Banning GenAI tools is not the solution  

Many students use GenAI tools to aid their learning. In a meta-analysis on the use of AI chatbots in education, Wu and Yu (2023) found that AI chatbots can significantly improve learning outcomes, specifically in the areas of learning performance, motivation, self-efficacy, interest, and perceived value of learning. Additionally, non-native English speakers and students with language and learning disabilities often turn to these tools to support their thinking, communication, and learning.  

Students also need opportunities to learn how to use, and critically analyze, GenAI tools in order to prepare for their future careers. The number of US job postings on LinkedIn that mention “GPT” has increased 79% year over year and the majority of employers believe that employees will need new skills, including analytical judgment about AI outputs and AI prompt engineering, to be prepared for the future (Microsoft, 2023). The Modern Language Association of America and Conference on College Composition and Communication (MLA-CCCC) Joint Task Force on Writing an AI recently noted that: 

Refusing to engage with GAI helps neither students nor the academic enterprise, as research, writing, reading, and other companion thinking tools are developing at a whirlwind rate and being integrated into students’ future workplaces from tech firms to K–12 education to government offices. We simply cannot afford to adopt a stance of complete hostility to GAI: such a stance incurs the risk of GAI tools being integrated into the fabric of intellectual life without the benefit of humanistic and rhetorical expertise. (pp. 8-9) 

Ultimately, banning GenAI tools in a course could negatively impact student learning and exacerbate the digital divide between students who have opportunities to learn how to use these tools and those who do not (Trust, 2023). And, banning these tools won’t stop students from using them–when universities tried to ban TikTok, students just used cellular data and VPNs to circumvent the ban (Alonso, 2023).  

However, you do not have to allow students to use GenAI tools all the time in your courses. Students might benefit from using these tools on some assignments, but not others. Or for some class activities, but not others. It is up to you to decide when these tools might be allowed in your courses and to make that clear to your students. Which leads to my next point… 

2. Tell your students what YOU allow  

Every college and university has an academic integrity/honesty or academic dishonesty statement. However, these statements are either written so broadly that there can be different interpretations of the language, or these statements indicate that the responsibility of determining what is allowable depends on the instructor, or both!  

Take a look at UC San Diego’s Academic Honesty Policy (2023) (highlights were added for emphasis).  

While this policy is detailed and specific, there is still room for interpretation of the text; and the responsibility of determining whether students can use GenAI tools as a learning aid (section “e”) relies solely on the instructor.  

Keeping the UC San Diego academic honesty policy in mind, consider the following: 

• A student prompted Gemini to rewrite their text to improve the quality of their writing and submitted the AI-generated version of their text.  
• A student used ChatGPT to write their conclusion word-for-word, but they cited ChatGPT as a source. 
• A student prompted ChatGPT to draft sentence starters for each paragraph in their midterm paper.   
• A student used ChatGPT as an aid by prompting it to summarize course readings and make them easier to understand. 

Which of these examples is a violation of the policy? This is up to you to determine based on your interpretation of the policy.  

Now, think about your students. Some students take three, four, five, and even six classes a semester. Each class is taught by a different instructor who might have their own unique interpretation of the university’s academic integrity policy and a different perspective regarding what is allowable when it comes to GenAI tools and what is not.  

Unfortunately, most instructors do not make their perspectives regarding GenAI tools clear to students. This leaves students guessing what is allowed in each course they take and if they guess wrong, they could fail an assignment, fail a course, or even get suspended – these are devastating consequences for a student who is unsure about what is allowed when it comes to using GenAI tools for their learning because their instructors do not make it clear to them.  

Ultimately, it is up to you, as the instructor, to determine what you allow, and then to let your students know! Write your own GenAI policy to include in your syllabus. Write your own GenAI use policies for assignments, exams, or even class activities. And then, talk with students about these policies and clarify any confusion they might have. 

3. Use the three W’s to tell students what you allow  

• W hat GenAI tools are allowed? 
• W hen are GenAI tools allowed (or not allowed)? 
• W hy are GenAI tools allowed (or not allowed)? 

The 3 W’s can be used as a model to write your academic integrity statement in a clear and concise manner.  

Let’s start with the first W: W hat GenAI tools are allowed ?  

Will you allow your students to use AI text generators? AI image creators? AI video, speech, and audio producers? What about Grammarly? Khanmigo? Or, GenAI tools embedded into Google Workspace ? 

If you do not clarify what GenAI tools are allowed, students might end up using an AI-enhanced tool, like Grammarly, and be accused of using AI to cheat because they did not know that when you said “No GenAI tools” you meant “No Grammarly, either” (read: She used Grammarly to proofread her paper. Now she’s accused of ‘unintentionally cheating.’ ).  

Please do not put students in a situation of guessing what GenAI tools are allowed or not. The consequences can be dire and students deserve the transparency.  

The next W is When are GenAI tools allowed (or not allowed) ? 

If you simply list what GenAI tools you allow, students might think it is okay to use the tools you listed for every assignment, learning activity, and learning experience in your class.  

Students need specific directions for when GenAI tools can be used and when they cannot be used. Do you allow students to use GenAI tools on only one assignment? Every assignment? One part of an assignment? Or, what about for one aspect of learning (e.g., brainstorming) but not another (e.g., writing)? Or, for one class activity (e.g., simulating a virtual debate) but not another (e.g., practicing public speaking by engaging in a class debate)?  

To determine when GenAI tools could be used in your classes, you might start with the learning outcomes for an activity or assessment and then identify how GenAI tools might support or subvert these outcomes (MLA-CCCC, 2024). When GenAI tools support, enhance, or enrich learning, it might be worthwhile to allow students to use these tools. When GenAI tools take away from or replace learning, you might tell students not to use these tools.  

Making it clear when students can and cannot use GenAI tools will eliminate any guesswork from students and reduce instances of students using GenAI tools when you did not want them to.  

The final W is W hy are GenAI tools allowed (or not allowed) ? 

Being transparent about why GenAI tools are allowed or are not allowed helps students understand your reasoning and creates a learning environment where students are more likely to do what you ask them to do.  

In the case of writing, for example, you might allow students to use GenAI tools to help with brainstorming ideas, but not with writing or rewriting their work because you believe that the process of putting pen to paper (or fingers to keyboards) is essential for deepening understanding of the course content. Telling students this will give them a clearer sense of why you are asking them to do what you are asking them to do.  

If you simply state: “Do not use GenAI tools during your writing process” students might wonder, Why? and might very well use these tools exactly how you asked them not to because they were not given a reason why not to.  

To sum up, the three W’s model brings transparency into teaching and learning and makes it clear and easy for students to understand when, where, and why they can use GenAI tools. This eliminates the guesswork from students, and reduces potential fears, anxieties, and stressors about the use of these tools in your courses.  

You can use the thre W’s as a model for crafting your academic integrity statement for your syllabus and also as a model for clarifying AI use in an assignment (see the image below), on an exam, or during a class activity. 

4. Clarify how you will identify AI-generated work and what you will do about it  

Even when you provide a detailed AI academic integrity policy and increase transparency around the use of GenAI tools in your courses, students may still use these tools in ways that you do not allow.  

It is important to let students know how you plan to identify AI-generated work.  

Will you use an AI text detector? (Note that these tools are notoriously unreliable, inaccurate, and biased against non-native English speakers and students with disabilities; Liang et al., 2023; Perkins et al., 2024; Weber-Wulff et al., 2023) 

Will you simply be on the lookout for text that looks AI-generated? If so, what will you look for? A change in writing voice and tone? Overuse of certain phrases like “delve”? A Google Docs version history where it appears as though text was copied and pasted in all at once? (see Detecting AI-Generated Text: 12 Things to Watch For ) 

Keep in mind that your own assumptions and biases might negatively impact certain groups of students as you seek to identify AI-generated work. The MLA-CCCC Joint Task Force (2024) noted that “literature across a number of disciplines has shown that international students and multilingual students who are writing in English are more likely to be accused of GAI-related academic misconduct” both because “GAI detectors are more likely to flag English prose written by nonnative speakers” and “suspicions of misuse of GAI are often due to complex factors, including culture, context, and unconscious ‘native-speakerism’ rather than actual misconduct” (p. 9).  

Also, consider what happens if a student submits content that looks or is identified by a detector as AI-generated. Will they automatically fail the assignment? Need to have a conversation with you? Need to prove their knowledge to you in another way (e.g., oral exam)? Be referred to the Dean of Students? 

Whatever you decide, being upfront about your expectations can foster a culture of trust between you and your students, and it might even deter students from using the tools in ways that you do not allow them to.  

5. Consider whether you will allow students to cite GenAI tools as a source  

One final point to consider as you are writing your academic integrity statement is whether students should be allowed to cite GenAI tools as a source. 

Many college and university academic integrity/honesty statements indicate that as long as the student cites their sources, including GenAI tools, they are not violating academic integrity. AI syllabus policies , too, often state that students can use GenAI tools as long as they cite them. 

But, should students really be encouraged to cite GenAI tools as a source?  

Consider, for example, that many of the popular GenAI tools were designed by stealing tons of copyrighted data from the Internet. The companies that created these tools “received billions of dollars of investment while using copyrighted work taken without permission or compensation. This is not fair” (Syed, 2023).  

While several companies are currently being sued for using copyrighted data to make their GenAI tools, in many cases, artists, authors, and other individuals whose work has been used without their permission to train these tools are losing their cases because of US copyright law and fair use. GenAI companies are arguing that their tools transform the copyrighted data they scraped from the Internet in a way that falls under fair use protections. However, a study found that large language models, like ChatGPT, sometimes generate text of over 1,000 words long that has been copied word-for-word from the original training data (McCoy et al., 2023) – making ChatGPT a plagiarism machine! 

Consider also that GenAI tools can make up (“hallucinate”) content and present harmful and biased information. Do you want students to cite information from a tool that is not designed with the intent of providing factual information?  

In a recent class activity, I asked my students (future educators) to write their own AI policy statements. Before they wrote their statements, I explained how GenAI tools were designed by scraping copyrighted data from the Internet and then they interrogated a GenAI tool by asking it at least 10 questions about whether it violated intellectual property rights. Across the board, my students decided that citing GenAI tools is not allowed and that they want their future students to cite an original source instead.  

It is up to you whether you allow students to cite GenAI as a source or not. The most important thing is to be transparent with your students about whether you allow them to cite GenAI tools as a source; and if you do, let them know how much text you would allow them to copy word-for-word into their work as long as they cite a GenAI tool. 

So, this returns us back to the question at the start: Would you let your students submit a paper where 5% of the text was written by ChatGPT…as long as they cited ChatGPT as a source?  

GenAI Disclosure : The author used Gemini and ChatGPT 3.5 to assist with revising text to improve the quality of the writing. All text was originally written by the author, but some of the text was revised based on suggestions from Gemini and ChatGPT 3.5.  

Author Bio  

Torrey Trust, PhD, is a professor of learning technology in the Department of Teacher Education and Curriculum Studies in the College of Education at the University of Massachusetts Amherst. Her work centers on the critical examination of the relationship between teaching, learning, and technology; and how technology can enhance teacher and student learning. Dr. Trust has received the University of Massachusetts Amherst Distinguished Teaching Award (2023), the College of Education Outstanding Teaching Award (2020), and the ISTE Making IT Happen Award (2018), which “honors outstanding educators and leaders who demonstrate extraordinary commitment, leadership, courage and persistence in improving digital learning opportunities for students.”   

References  

Alonso, J. (2023, January 19). Students and experts agree: TikTok bans are useless. Inside Higher Ed. https://www.insidehighered.com/news/2023/01/20/university-tiktok-bans-cause-concern-and-confusion   

Choi, C. & Annio, F. (2024, January 19). The winner of a prestigious Japanese literary award has confirmed AI helped write her book . CNN. https://www.cnn.com/2024/01/19/style/rie-kudan-akutagawa-prize-chatgpt/index.html   

Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4 (7).  

McCoy, R. T., Smolensky, P., Linzen, T., Gao, J., & Celikyilmaz, A. (2023). How much do language models copy from their training data? Evaluating linguistic novelty in text generation using raven. Transactions of the Association for Computational Linguistics, 11, 652-670. 

Microsoft. (2023). Work trend annual index report. https://www.microsoft.com/en-us/worklab/work-trend-index/will-ai-fix-work/   

Modern Language Association of America and Conference on College Composition and Communication. (2024). Generative AI and policy development: Guidance from the MLA-CCCC task force . https://cccc.ncte.org/mla-cccc-joint-task-force-on-writing-and-ai   

Perkins, M., Roe, J., Vu, B. H., Postma, D., Hickerson, D., McGaughran, J., & Khuat, H. Q. (2024). GenAI detection tools, adversarial techniques and implications for inclusivity in higher education. arXiv preprint. https://arxiv.org/ftp/arxiv/papers/2403/2403.19148.pdf   

Syed, N. (2023, November 18). ‘Unmasking AI’ and the fight for algorithmic justice . The Markup. https://themarkup.org/hello-world/2023/11/18/unmasking-ai-and-the-fight-for-algorithmic-justice   

Trust, T. (2023, August 2). Essential considerations for addressing the possibility of AI-driven cheating, part 1. Faculty Focus. https://www.facultyfocus.com/articles/teaching-with-technology-articles/essential-considerations-for-addressing-the-possibility-of-ai-driven-cheating-part-1/   

UC San Diego. (2023). UC San Diego academic integrity policy . https://senate.ucsd.edu/Operating-Procedures/Senate-Manual/Appendices/2   

Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., … & Waddington, L. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19 (1), 26. 

Wu, R. & Yu, Z. (2023). Do AI chatbots improve students learning outcomes? Evidence from a meta-analysis. British Journal of Educational Technology, 55 (1), 10-33.  

Stay Updated with Faculty Focus!

Get exclusive access to programs, reports, podcast episodes, articles, and more!

• Opens in a new tab

Welcome Back

Username or Email

Remember Me

Already a subscriber? log in here.

UT Partners With Grammarly to Guide Effective Generative AI Use in Higher Education

The University of Texas at Austin has teamed up with Grammarly for Education, an AI-enabled writing assistant, to investigate the adoption of generative artificial intelligence in a broad academic setting.

This project, led by UT’s Office of Academic Technology and in alignment with the University’s Year of AI initiative , will be broken into two phases. First, there will be a testing phase during which students, faculty and staff will interact with Grammarly’s generative AI assistant. Faculty and staff participants will design generative AI activities relevant to their own work areas and test those activities with students and peers. Second, faculty will create more detailed lesson plans to engage students in generative AI learning activities — all vetted to meet UT’s academic standards.

“We strive to be involved in projects that will influence higher education on and beyond the Forty Acres,” said Art Markman, vice provost for academic affairs. “We are in an era with a lot of uncertainty surrounding AI and education. This is a chance to demonstrate how to use generative AI as a positive source for education, teach responsibility to our students, and engage an industry leader to improve our understanding of classroom AI.”

All participants in the project will receive a short-term Grammarly for Education pilot license. Training on Grammarly for Education and the AI assistant will be provided.

“We’re thrilled to partner with UT on such a forward-looking project,” said Mary Rose Craycraft, head of customer success at Grammarly for Education. “We know that innovating with AI while preserving academic integrity and critical thinking is a key challenge that all institutions are grappling with right now. We look forward to working with UT to develop best practices that can scale responsible AI adoption across the sector.”

Projects like the Grammarly adoption are carefully assessed and vetted by the Office of Academic Technology through a Learning Technology Adoption Process (LTAP). LTAPs provide a strategic and coordinated approach to data-driven adoption of academic technology ensuring the University only adopts and promotes tools on campus that align with its principles of effective teaching. The process protects students and faculty from adopting short-term technologies or those unsuitable for information security regulations.

Ultimately, the University’s coordination and partnership with emerging learning technology platforms leads to decisions that are in the best interest of students, staff and faculty. By collaborating with those who will be using generative AI tools most through case studies and active feedback, the Office of Academic Technology aims to engage in both AI-forward and AI-responsible teaching and learning at UT Austin.

“Our primary generative AI strategy is to use evidence-based decision-making to drive effective, forward and responsible AI use in ways that advance the teaching and learning mission of the University,” said Julie Schell, assistant vice provost of academic technology. “We are very excited to work with Grammarly to engage the UT community and create generative AI activities and lesson plans vetted by UT faculty, staff and students that can be scaled with any generative AI tool.”

To participate in the Grammarly project, sign up on the project webpage . For all other questions, please contact [email protected] .

Explore Latest Articles

May 11, 2024

Class of 2024 Celebrated at 141st Commencement

May 09, 2024

Taylor-Based Foundation Gifts 68-Acre Property to UT to Support Semiconductor Ecosystem

UT for Me – Powered by Dell Scholars: Meet Ang Ruiz

Suggestions or feedback?

MIT News | Massachusetts Institute of Technology

• Machine learning
• Social justice
• Black holes
• Classes and programs

Departments

• Aeronautics and Astronautics
• Brain and Cognitive Sciences
• Architecture
• Political Science
• Mechanical Engineering

Centers, Labs, & Programs

• Abdul Latif Jameel Poverty Action Lab (J-PAL)
• Picower Institute for Learning and Memory
• Lincoln Laboratory
• School of Architecture + Planning
• School of Engineering
• School of Humanities, Arts, and Social Sciences
• Sloan School of Management
• School of Science
• MIT Schwarzman College of Computing

The power of App Inventor: Democratizing possibilities for mobile applications

Press contact :.

Previous image Next image

In June 2007, Apple unveiled the first iPhone. But the company made a strategic decision about iPhone software: its new App Store would be a walled garden. An iPhone user wouldn’t be able to install applications that Apple itself hadn’t vetted, at least not without breaking Apple’s terms of service.

That business decision, however, left educators out in the cold. They had no way to bring mobile software development — about to become part of everyday life — into the classroom. How could a young student code, futz with, and share apps if they couldn’t get it into the App Store?

MIT professor Hal Abelson was on sabbatical at Google at the time, when the company was deciding how to respond to Apple’s gambit to corner the mobile hardware and software market. Abelson recognized the restrictions Apple was placing on young developers; Google recognized the market need for an open-source alternative operating system — what became Android. Both saw the opportunity that became App Inventor.

“Google started the Android project sort of in reaction to the iPhone,” Abelson says. “And I was there, looking at what we did at MIT with education-focused software like Logo and Scratch , and said ‘what a cool thing it would be if kids could make mobile apps also.’”

Google software engineer Mark Friedman volunteered to work with Abelson on what became “Young Android,” soon renamed Google App Inventor. Like Scratch, App Inventor is a block-based language, allowing programmers to visually snap together pre-made “blocks” of code rather than need to learn specialized programming syntax.

Friedman describes it as novel for the time, particularly for mobile development, to make it as easy as possible to build simple mobile apps. “That meant a web-based app,” he says, “where everything was online and no external tools were required, with a simple programming model, drag-and-drop user interface designing, and blocks-based visual programming.” Thus an app someone programmed in a web interface could be installed on an Android device.

App Inventor scratched an itch. Boosted by the explosion in smartphone adoption and the fact App Inventor is free (and eventually open source), soon more than 70,000 teachers were using it with hundreds of thousands of students, with Google providing the backend infrastructure to keep it going.

“I remember answering a question from my manager at Google who asked how many users I thought we'd get in the first year,” Friedman says. “I thought it would be about 15,000 — and I remember thinking that might be too optimistic. I was ultimately off by a factor of 10–20.” Friedman was quick to credit more than their choices about the app. “I think that it's fair to say that while some of that growth was due to the quality of the tool, I don't think you can discount the effect of it being from Google and of the effect of Hal Abelson's reputation and network.”

Some early apps took App Inventor in ambitious, unexpected directions, such as “Discardious,” developed by teenage girls in Nigeria. Discardious helped business owners and individuals dispose of waste in communities where disposal was unreliable or too cumbersome.

But even before apps like Discardious came along, the team knew Google’s support wouldn’t be open-ended. No one wanted to cut teachers off from a tool they were thriving with, so around 2010, Google and Abelson agreed to transfer App Inventor to MIT. The transition meant major staff contributions to recreate App Inventor without Google’s proprietary software but MIT needing to work with Google to continue to provide the network resources to keep App Inventor free for the world.

With such a large user base, however, that left Abelson “worried the whole thing was going to collapse” without Google’s direct participation.

Friedman agrees. “I would have to say that I had my fears. App Inventor has a pretty complicated technical implementation, involving multiple programming languages, libraries and frameworks, and by the end of its time at Google we had a team of about 10 people working on it.”

Yet not only did Google provide significant funding to aid the transfer, but, Friedman says of the transfer’s ultimate success, “Hal would be in charge and he had fairly extensive knowledge of the system and, of course, had great passion for the vision and the product.”

MIT enterprise architect Jeffrey Schiller, who built the Institute’s computer network and became its manager in 1984, was another key part in sustaining App Inventor after its transition, helping introduce technical features fundamental to its accessibility and long-term success. He led the integration of the platform into web browsers, the addition of WiFi support rather than needing to connect phones and computers via USB, and the laying of groundwork for technical support of older phones because, as Schiller says, “many of our users cannot rush out and purchase the latest and most expensive devices.”

These collaborations and contributions over time resulted in App Inventor’s greatest resource: its user base. As it grew, and with support from community managers, volunteer know-how grew with it. Now, more than a decade since its launch, App Inventor recently crossed several major milestones, the most remarkable being the creation of its 100 millionth project and registration of its 20 millionth user. Young developers continue to make incredible applications, boosted now by the advantages of AI. College students created “ Brazilian XôDengue ” as a way for users to use phone cameras to identify mosquito larvae that may be carrying the dengue virus. High school students recently developed “ Calmify ,” a journaling app that uses AI for emotion detection. And a mother in Kuwait wanted something to help manage the often-overwhelming experience of new motherhood when returning to work, so she built the chatbot “ PAM (Personal Advisor to Mothers) ” as a non-judgmental space to talk through the challenges.

App Inventor’s long-term sustainability now rests with the App Inventor Foundation, created in 2022 to grow its resources and further drive its adoption. It is led by executive director Natalie Lao.

In a letter to the App Inventor community, Lao highlighted the foundation’s commitment to equitable access to educational resources, which for App Inventor required a rapid shift toward AI education — but in a way that upholds App Inventor’s core values to be “a free, open-source, easy-to-use platform” for mobile devices. “Our mission is to not only democratize access to technology,” Lao wrote, “but also foster a culture of innovation and digital literacy.”

Within MIT, App Inventor today falls under the umbrella of the MIT RAISE Initiative — Responsible AI for Social Empowerment and Education, run by Dean for Digital Learning Cynthia Breazeal, Professor Eric Klopfer, and Abelson. Together they are able to integrate App Inventor into ever-broader communities, events, and funding streams, leading to opportunities like this summer’s inaugural AI and Education Summit on July 24-26. The summit will include awards for winners of a Global AI Hackathon , whose roughly 180 submissions used App Inventor to create AI tools in two tracks: Climate & Sustainability and Health & Wellness. Tying together another of RAISE’s major projects, participants were encouraged to draw from Day of AI curricula, including its newest courses on data science and climate change .

“Over the past year, there's been an enormous mushrooming in the possibilities for mobile apps through the integration of AI,” says Abelson. “The opportunity for App Inventor and MIT is to democratize those new possibilities for young people — and for everyone — as an enhanced source of power and creativity.”

Share this news article on:

Related links.

• MIT App Inventor
• MIT RAISE Initiative (Responsible AI for Social Empowerment and Education)
• Hal Abelson
• Cynthia Breazeal
• MIT Open Learning
• Department of Electrical Engineering and Computer Science
• School of Architecture and Planning

Related Topics

• Education, teaching, academics
• Open access
• Online learning
• Programming
• K-12 education
• Open source
• Artificial intelligence
• STEM education
• History of MIT
• History of science
• Electrical Engineering & Computer Science (eecs)

Related Articles

Celebrating open data

“Kids are people too!”

At Mass STEM Week kickoff, MIT RAISE announces Day of AI

App Inventor launches second iteration

Previous item Next item

More MIT News

Using MRI, engineers have found a way to detect light deep in the brain

Read full story →

A better way to control shape-shifting soft robots

From steel engineering to ovarian tumor research

Professor Emeritus David Lanning, nuclear engineer and key contributor to the MIT Reactor, dies at 96

Discovering community and cultural connections

MIT Supply Chain Management Program earns top honors in three 2024 rankings

• More news on MIT News homepage →

Massachusetts Institute of Technology 77 Massachusetts Avenue, Cambridge, MA, USA

• Map (opens in new window)
• Events (opens in new window)
• People (opens in new window)
• Careers (opens in new window)
• Accessibility
• Social Media Hub
• MIT on Facebook
• MIT on YouTube
• MIT on Instagram