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• What Is Peer Review? | Types & Examples

What Is Peer Review? | Types & Examples

Published on December 17, 2021 by Tegan George . Revised on June 22, 2023.

Peer review, sometimes referred to as refereeing , is the process of evaluating submissions to an academic journal. Using strict criteria, a panel of reviewers in the same subject area decides whether to accept each submission for publication.

Peer-reviewed articles are considered a highly credible source due to the stringent process they go through before publication.

There are various types of peer review. The main difference between them is to what extent the authors, reviewers, and editors know each other’s identities. The most common types are:

• Single-blind review
• Double-blind review
• Triple-blind review

Collaborative review

Open review.

Relatedly, peer assessment is a process where your peers provide you with feedback on something you’ve written, based on a set of criteria or benchmarks from an instructor. They then give constructive feedback, compliments, or guidance to help you improve your draft.

Table of contents

What is the purpose of peer review, types of peer review, the peer review process, providing feedback to your peers, peer review example, advantages of peer review, criticisms of peer review, other interesting articles, frequently asked questions about peer reviews.

Many academic fields use peer review, largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the manuscript. For this reason, academic journals are among the most credible sources you can refer to.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

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Depending on the journal, there are several types of peer review.

Single-blind peer review

The most common type of peer review is single-blind (or single anonymized) review . Here, the names of the reviewers are not known by the author.

While this gives the reviewers the ability to give feedback without the possibility of interference from the author, there has been substantial criticism of this method in the last few years. Many argue that single-blind reviewing can lead to poaching or intellectual theft or that anonymized comments cause reviewers to be too harsh.

Double-blind peer review

In double-blind (or double anonymized) review , both the author and the reviewers are anonymous.

Arguments for double-blind review highlight that this mitigates any risk of prejudice on the side of the reviewer, while protecting the nature of the process. In theory, it also leads to manuscripts being published on merit rather than on the reputation of the author.

Triple-blind peer review

While triple-blind (or triple anonymized) review —where the identities of the author, reviewers, and editors are all anonymized—does exist, it is difficult to carry out in practice.

Proponents of adopting triple-blind review for journal submissions argue that it minimizes potential conflicts of interest and biases. However, ensuring anonymity is logistically challenging, and current editing software is not always able to fully anonymize everyone involved in the process.

In collaborative review , authors and reviewers interact with each other directly throughout the process. However, the identity of the reviewer is not known to the author. This gives all parties the opportunity to resolve any inconsistencies or contradictions in real time, and provides them a rich forum for discussion. It can mitigate the need for multiple rounds of editing and minimize back-and-forth.

Collaborative review can be time- and resource-intensive for the journal, however. For these collaborations to occur, there has to be a set system in place, often a technological platform, with staff monitoring and fixing any bugs or glitches.

Lastly, in open review , all parties know each other’s identities throughout the process. Often, open review can also include feedback from a larger audience, such as an online forum, or reviewer feedback included as part of the final published product.

While many argue that greater transparency prevents plagiarism or unnecessary harshness, there is also concern about the quality of future scholarship if reviewers feel they have to censor their comments.

In general, the peer review process includes the following steps:

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to the author, or
• Send it onward to the selected peer reviewer(s)
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made.
• Lastly, the edited manuscript is sent back to the author. They input the edits and resubmit it to the editor for publication.

In an effort to be transparent, many journals are now disclosing who reviewed each article in the published product. There are also increasing opportunities for collaboration and feedback, with some journals allowing open communication between reviewers and authors.

It can seem daunting at first to conduct a peer review or peer assessment. If you’re not sure where to start, there are several best practices you can use.

Summarize the argument in your own words

Summarizing the main argument helps the author see how their argument is interpreted by readers, and gives you a jumping-off point for providing feedback. If you’re having trouble doing this, it’s a sign that the argument needs to be clearer, more concise, or worded differently.

If the author sees that you’ve interpreted their argument differently than they intended, they have an opportunity to address any misunderstandings when they get the manuscript back.

Separate your feedback into major and minor issues

It can be challenging to keep feedback organized. One strategy is to start out with any major issues and then flow into the more minor points. It’s often helpful to keep your feedback in a numbered list, so the author has concrete points to refer back to.

Major issues typically consist of any problems with the style, flow, or key points of the manuscript. Minor issues include spelling errors, citation errors, or other smaller, easy-to-apply feedback.

Tip: Try not to focus too much on the minor issues. If the manuscript has a lot of typos, consider making a note that the author should address spelling and grammar issues, rather than going through and fixing each one.

The best feedback you can provide is anything that helps them strengthen their argument or resolve major stylistic issues.

Give the type of feedback that you would like to receive

No one likes being criticized, and it can be difficult to give honest feedback without sounding overly harsh or critical. One strategy you can use here is the “compliment sandwich,” where you “sandwich” your constructive criticism between two compliments.

Be sure you are giving concrete, actionable feedback that will help the author submit a successful final draft. While you shouldn’t tell them exactly what they should do, your feedback should help them resolve any issues they may have overlooked.

As a rule of thumb, your feedback should be:

• Easy to understand
• Constructive

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Below is a brief annotated research example. You can view examples of peer feedback by hovering over the highlighted sections.

Influence of phone use on sleep

Studies show that teens from the US are getting less sleep than they were a decade ago (Johnson, 2019) . On average, teens only slept for 6 hours a night in 2021, compared to 8 hours a night in 2011. Johnson mentions several potential causes, such as increased anxiety, changed diets, and increased phone use.

The current study focuses on the effect phone use before bedtime has on the number of hours of sleep teens are getting.

For this study, a sample of 300 teens was recruited using social media, such as Facebook, Instagram, and Snapchat. The first week, all teens were allowed to use their phone the way they normally would, in order to obtain a baseline.

The sample was then divided into 3 groups:

• Group 1 was not allowed to use their phone before bedtime.
• Group 2 used their phone for 1 hour before bedtime.
• Group 3 used their phone for 3 hours before bedtime.

All participants were asked to go to sleep around 10 p.m. to control for variation in bedtime . In the morning, their Fitbit showed the number of hours they’d slept. They kept track of these numbers themselves for 1 week.

Two independent t tests were used in order to compare Group 1 and Group 2, and Group 1 and Group 3. The first t test showed no significant difference ( p > .05) between the number of hours for Group 1 ( M = 7.8, SD = 0.6) and Group 2 ( M = 7.0, SD = 0.8). The second t test showed a significant difference ( p < .01) between the average difference for Group 1 ( M = 7.8, SD = 0.6) and Group 3 ( M = 6.1, SD = 1.5).

This shows that teens sleep fewer hours a night if they use their phone for over an hour before bedtime, compared to teens who use their phone for 0 to 1 hours.

Peer review is an established and hallowed process in academia, dating back hundreds of years. It provides various fields of study with metrics, expectations, and guidance to ensure published work is consistent with predetermined standards.

• Protects the quality of published research

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. Any content that raises red flags for reviewers can be closely examined in the review stage, preventing plagiarized or duplicated research from being published.

• Gives you access to feedback from experts in your field

Peer review represents an excellent opportunity to get feedback from renowned experts in your field and to improve your writing through their feedback and guidance. Experts with knowledge about your subject matter can give you feedback on both style and content, and they may also suggest avenues for further research that you hadn’t yet considered.

• Helps you identify any weaknesses in your argument

Peer review acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process. This way, you’ll end up with a more robust, more cohesive article.

While peer review is a widely accepted metric for credibility, it’s not without its drawbacks.

• Reviewer bias

The more transparent double-blind system is not yet very common, which can lead to bias in reviewing. A common criticism is that an excellent paper by a new researcher may be declined, while an objectively lower-quality submission by an established researcher would be accepted.

• Delays in publication

The thoroughness of the peer review process can lead to significant delays in publishing time. Research that was current at the time of submission may not be as current by the time it’s published. There is also high risk of publication bias , where journals are more likely to publish studies with positive findings than studies with negative findings.

• Risk of human error

By its very nature, peer review carries a risk of human error. In particular, falsification often cannot be detected, given that reviewers would have to replicate entire experiments to ensure the validity of results.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Thematic analysis
• Discourse analysis
• Cohort study
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias
• Social desirability bias

Peer review is a process of evaluating submissions to an academic journal. Utilizing rigorous criteria, a panel of reviewers in the same subject area decide whether to accept each submission for publication. For this reason, academic journals are often considered among the most credible sources you can use in a research project– provided that the journal itself is trustworthy and well-regarded.

In general, the peer review process follows the following steps: 

• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

A credible source should pass the CRAAP test  and follow these guidelines:

• The information should be up to date and current.
• The author and publication should be a trusted authority on the subject you are researching.
• The sources the author cited should be easy to find, clear, and unbiased.
• For a web source, the URL and layout should signify that it is trustworthy.

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Peer Review Defined

What are peer-reviewed journals, characteristics of scholarly articles, using ulrich's.

• Other Questions?

Peer review is a quality control process used by publications to help ensure that only high quality, methodologically sound information is presented in the publication. In the peer review process, material submitted for publication is sent to individuals who are experts on the topic. Those experts read the material and suggest to the editor whether the material should be rejected, should be accepted, or should be sent back to the authors with a request for revisions. 

Peer-reviewed journals are journals that use the peer review process (defined in the box above). Almost all peer-reviewed journals are scholarly journals. 

According to Cornell University Libraries, there are several characteristics that define a scholarly journal:

• They generally have a more “serious” look meaning there’s less emphasis on glossy pages and fancy photographs and more put on text, graphs, and charts.  
• Scholarly journals always cite their sources.  This is usually in the form of footnotes or bibliographies. 
• Articles are written by scholars in that particular field or who have done research in that field.  
• The language of the article contains language used in that discipline.  
• The author assumes the audience has some prior knowledge of the research or background in that field. 
• Lastly, the purpose of a scholarly journal is to report on original research and make that information available to other people. 

In your research, you will find articles from many different sources. The sources might be scholarly (intended to be used by scholars in the field), or they might be popular (intended to be used by the general public). Here are some things you can look for to determine if your article is scholarly:

• Look at the title. The title is usually a brief summary of the article often with specific terminology related to that field.
• Look at the authors. Are the author’s credentials at the beginning of the article or somewhere easily found? This helps establish the author’s authority as an expert in that field.
• Look for an abstract. This is the summary of the article. It helps readers determine whether the article suits their research needs. Sometimes it will even be labeled “Abstract.”
• Look for charts, graphs, tables, or equations. These are often found in scholarly research. Pictures are rare.
• Look for references. You will find these scattered throughout the article as footnotes or endnotes at the end of an article. Authors will usually also include a full reference list at the end of the article. This is a good way to find additional articles on your topic.

If you want to be absolutely sure a journal is peer reviewed, use the database Ulrich's International Periodicals Directory . Look up the journal by title. Titles that are peer reviewed are indicated by the black and white referee's jersey ("refereed" is another term for peer-reviewed.) Note that there may be some parts of the journal (e.g. letters to the editor, book reviews, etc.) that are not peer reviewed.

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• Published: 05 December 2023

Reviewing a review

Nature Reviews Psychology volume  2 ,  page 715 ( 2023 ) Cite this article

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• Peer review

Peer review for a narrative review article can be quite different from the process for an empirical manuscript. We demystify the aims of and procedures for peer review at Nature Reviews Psychology .

All of our Review and Perspective articles are peer reviewed by experts in the field. Our peer review process for these papers has many broad similarities to peer review of empirical research papers. However, it also has a few unique aspects. For one thing, review-type articles do not report any original analyses, so there are no methodological details or statistical analyses to evaluate. Instead, papers in our journal should organize, synthesize and critically discuss the literature, as well as conveying recommendations for future research in the field.

Our instructions to reviewers echo these broad aims, as well as the specific aims of each article type. We ask reviewers whether the scope of the article is clear and whether the coverage of material is appropriate, both within the specific topic area and across the broader context of the field. For a Review, discussion of major theories or findings should be balanced, and any intentional omissions should be explained to the reader. For a Perspective, the authors should not ignore alternative points of view even as they centre their own account.

Another major aspect of the manuscript that we ask peer reviewers to consider is its timeliness: does the article provide a needed update, an authoritative synthesis, a unique angle or a useful framework? Importantly, we do not ask reviewers to evaluate the ‘novelty’ of a manuscript. Because reviews must be based in existing literature, a truly novel manuscript with only original ideas wouldn’t be a review at all!

Finally, we ask reviewers to evaluate how the paper might be received by our broad audience of researchers, academics and clinicians across psychology. We want our articles to strike a balance between authoritative and accessible so that a broad audience of topic experts and non-experts alike can benefit from their insights. That said, we want reviewers to focus on the article content. Writing issues such as typos, run-on sentences or grammatical mistakes will be addressed when the paper undergoes a detailed edit before publication.

Like editors at any journal, we aim to secure expert reviewers in each major topic covered by the manuscript. For some topics, we might invite researchers who don’t primarily consider themselves academics, such as clinicians or industry researchers. We aim for a diverse reviewer panel with respect to geographical location, racial and ethnic background, gender and career stage. All of these aspects can influence a reviewer’s evaluation of a manuscript, and a range of perspectives helps contribute to an overall evaluation that is fair and unbiased. Of course, we avoid reviewers with conflicts of interest, such as past or current collaborators.

The majority of our papers will go back to authors for a revision, and we want that revision to be as productive as possible. To that end, we annotate individual points of feedback in the peer review reports to help authors focus their revision efforts. We highlight comments that are particularly valuable or that we see having a substantial impact on the final article. For instance, some reviewers ask authors to motivate or reconsider a particular decision they made, such as to focus on a particular phenomenon, omit a specific outdated theory, or discuss topic A before topic B. Revisions in response to these types of request are often straightforward to implement but have a big impact on the eventual reader. We also adjudicate when two reviewers ask for opposing changes or make contradictory remarks, adding editorial guidance to help authors break the stalemate. Finally, we keep the scope and narrative cohesiveness of the article in mind. If a reviewer seems to be asking for the paper to be refocused around a different topic or wants extensive discussion of a tangential issue, we might tell authors that they can politely decline that particular piece of feedback. Ultimately, our aim is to provide authors with a clear path to a successful revision.

When we receive the revised version of a manuscript, we do a thorough read of the point-by-point rebuttal letter and the revised manuscript to determine whether the reviewers’ requests have been conscientiously addressed. Typical reviewer comments are about how concepts are explained, the space authors dedicate to discussing particular aspects of the literature, or alignment between the manuscript’s stated aims and its content. As professional editors with doctoral degrees in psychology, we are trained to evaluate the extent to which revisions to the text satisfy these types of reviewer concern; many manuscripts are not returned to peer reviewers for a second round after our evaluation. However, if substantial scientific information has been added or if we’re uncertain whether a reviewer’s concerns have been addressed, we will enlist all or a subset of the original peer reviewers to re-evaluate the manuscript.

“The peer review process…is a collaborative effort by the authors, the peer reviewers and the editor to bring out the best version of each article.”

The peer review process at Nature Reviews Psychology is designed to facilitate the transfer of critical yet constructive feedback between experts. It is a collaborative effort by the authors, the peer reviewers and the editor to bring out the best version of each article.

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Understanding and Conducting Peer Review

Peer review is an essential part of the scientific process.

Posted June 25, 2017

“ Peer review ” involves scientists reading other scientists’ work and deciding whether it is worthy of being published. I had a mentor who used to say that science wasn’t truly science until it was disseminated (published or at least presented at scientific conferences). Until then, all that tinkering in the lab is really nothing more than a hobby, because the efforts have not contributed to our shared body of scientific knowledge.

The peer review process is an essential component of that, because not just anything counts as science. Regrettably, there is a certain amount of “scientism,” among academics as well as critics of science, who dress up their language with science-sounding terms but are not really practicing science. It is only science if there is a systematic attempt to meaningfully contribute to scientific knowledge.

In practice, the way it is determined if this bar has been met is by sending scientific reports to independent reviewers for comment and critique. They will give suggestions about how to improve the presentation or clarify details about the scientific method. It is rare to see an article in a scientific journal that has not gone through this review and revision process.

Although this is an essential part of the scientific process, surprisingly it is one that scientists often receive relatively little training in. Many graduate students, junior scholars, or people working at low-resource institutions have not had the opportunity to get some good instruction about how to conduct a scientific review. As an editor of a scientific journal, I am often asked for such guidelines for reviewers and share some suggestions below.

The peer review process relies on thoughtful, rigorous reviews. The field of psychology is fortunate because most researchers are dedicated to provided balanced, constructive reviewers that appropriately identify the main strengths and weaknesses of a manuscript. Providing reviews to other scholars is one important avenue for influencing the field and maximizing the quality of violence research.

The result is a body of evidence that is probably stronger in many ways than it is sometimes portrayed in the media or by politicians. It is not easy to publish a scientific study, especially in a top journal.

General Standards for the Review Process

1) It is important to maintain a constructive tone and identify strengths as well as weaknesses. Regardless of whether the manuscript gets published at a particular journal, the ultimate goal of the peer review process is to improve the overall level of science in the field. Hopefully, even when manuscripts are rejected, authors will find the comments useful to their future work. Over the years, I have received many appreciative comments from authors, even to rejection letters, about helpful advice for continuing their research.

If you are reading a paper that seems to have numerous problems, remember that many of those might be early efforts by students or other junior scholars. We don’t want to scare them out of the field with needlessly hostile reviews, we want to mentor people to reach the highest scientific standards.

2) There are usually numerous acceptable methodological and statistical choices. One of the first pieces of advice that I give new reviewers or new editors is to realize that other scientists need not have made the choices you did. There are almost always multiple reasonable options for measures, statistics, citations to other work. A plurality of approaches benefits the field.

The Elements of a Review

Narrative comments. The main part of the review is detailed narrative comments. If your only experience with reviews is getting a paper back in a college or high school class, you would probably be surprised how detailed the comments are. Usually there are comments from 2 or 3 anonymous reviewers plus the editor. Altogether, it is common that these would add up to several typed pages of feedback. I’ve gotten reviews back for several of my own papers that add up to 9 or 10 single-spaced pages of feedback!

The typical individual review ranges from a couple of paragraphs to a few pages of narrative that includes specific feedback for the authors. It is common for reviews to be organized in the order of the manuscript. Some reviewers prefer to organize their reviews from what they consider to be the most to least important comments. Either organization is acceptable. Authors often find it helpful if the comments are numbered, so they are more easily referred to in their cover letter explaining how they have revised the manuscript.

It might seem nice to write a very short, completely positive review, but in the long run these are not only harmful to science, they do not help scientists improve either. You would not tell someone that they could win Wimbledon who can barely swing a tennis racket, and it is not nice to suggest that a study is great if it is significantly flawed.

Narrative comments are shared with authors.

Overall recommendation . The names for different publication recommendations vary from journal to journal, but usually the following categories are included in some form. These recommendations CANNOT be seen by authors, only by the editor:

Accept As Is : This is generally reserved for manuscripts that have been through one or more revisions. It is important that manuscripts be as strong as possible, both in terms of the science and the clarity of presentation. Virtually every manuscript has room for improvement. “Accept As Is” is an extremely rare initial decision.

Minor Revision : Minor, specific revision refers to less extensive edits, such as clarifying points or elaborating on implications.

Questions about the adequacy of the rationale, the suitability of the measures, or the soundness of the statistical approach are seldom minor.

“Minor” also usually implies that there are only a few remaining issues to be addressed. Your review will be compiled with others who are likely to raise different points, so if you have identified many points to address (certainly 10 or more), then it is unlikely to fall into the “minor revision” category.

Major Revision : This recommendation is appropriate for manuscripts that have potential to make a sound contribution, but require more substantive revisions in order to fully evaluate the science. Recommendations for to re-do statistical analyses usually fall into this category, as a re-analysis of the data might cast the results in a very different light. This is also appropriate for papers that provide incomplete descriptions of one or more measures, if the adequacy of the operationalization cannot be determined. None of these issues, however, are likely to dramatically change the results or interpretation of the data, and there is no indication of a major insurmountable problem (such as a problem with the procedure that cannot be “fixed”).

Reject : This option is for manuscripts that have limited potential to contribute to the literature. This could be due to insurmountable sampling or measurement problems (statistical problems are potentially more “fixable”). It can also be due to the limited scope of the study, especially if that seems to indicate a piecemeal approach to publication (for example, focusing on only 2 or 3 variables from a larger dataset that could potentially provide a more comprehensive analysis).

Although of course no author hopes to receive a “reject” decision, rejection does serve several important purposes. Principally, these decisions help to maintain high standards of scientific rigor. Hopefully, reject decisions will also help researchers design future studies or give them ideas about how to strengthen their submission for another outlet.

Preparing a Review

There are an almost infinite number of issues that might be raised in a review, but some of the most commonly mentioned concerns involve the following:

1) Empirical papers need to establish how the current manuscript meaningfully builds on existing work (including the most recent work) and especially how the current manuscript will make a novel contribution to the field. Beyond this, it is not necessary, nor is there room in a manuscript, to provide detailed literature reviews in a paper presenting new data.

2) The introduction needs to provide a specific rationale, ideally one based in theory, for including the main variables under study.

3) Literature reviews and theoretical papers should also clearly indicate how the synthesis will advance the field.

4) Regarding the methodology, one of the most important issues is to focus on the adequacy of the operationalization of the constructs. High quality measurement is essential. No amount of sophisticated statistical analyses can make up for problematic measures or flawed sampling procedures.

5) Statistics need to be clearly justified and explained, and appropriate for testing the hypotheses.

6) The purpose of discussions is not to simply re-cap results, but to put the findings in the context of prior literature, acknowledge limitations of the current study, and suggest specific implications for future research and applications to prevention, intervention, or policy.

Learning about peer review and how to do good reviews is great for becoming a better scientist and also a better consumer of scientific information.

Note: This is adapted from an earlier reviewer guide I prepared for Psychology of Violence reviewers.

Sherry Hamby, Ph.D. , is a research professor of psychology at Sewanee, the University of the South.

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What does a peer-reviewed article look like?

Eli Moody, Vanderbilt University, 2007 . Used by permission.

How do I read a peer-reviewed article?

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The Role Of Peer Review In The Scientific Process

March 7, 2021 - paper 2 psychology in context | research methods.

• Back to Paper 2 - Research Methods

The Role of Peer Review in the Scientific Process

Psychology, in common with all scientific subjects, develops its knowledge base through conducting research and sharing the findings of such research with other scientists. Peer review is an essential part of this process and scientific quality is judged by it. It is in the interest of all scientists that their work is held up for scrutiny and that any work that is flawed or downright fraudulent is detected and its results ignored.

Why is Peer Review so Important?

In order to remember the answer to this question, it is good to use the mnemonic  ‘People Make Very Interesting Statements!’

(1)  P eople   Prevent Plagiarism:  Psychologists/Scientists carrying out a peer review can make sure that the work due to be published isn’t simply a regurgitation of work that has been published previously by other Psychologists or Scientists.

(2)  M ake   Methodology:  Other researchers can check the report/study in terms of how appropriate the methodological choices were (e.g. sample of participants, (are they truly reflective of the  target population?)   research method (does the method used hinder reliability of ecological validity), design etc

(3)  V ery-  Validity:  Other researchers can check the report/study for accuracy in terms of testing, measuring and results analysis. Researchers can make comments in terms of whether they feel the study is ecologically valid or holds high population validity.

(4)  I nteresting   Integrity:  ( Integrity definition the quality of being honest and sound in construction).  This helps to ensure that any research paper published in a well-respected journal has integrity and can, therefore, be taken seriously by fellow researchers and by lay people.

(5)  S tatements   Significant   Peers are also in a position to judge the importance or significance of the research in a wider context, i.e. whether it’s relevant and worth doing, the implications of the research findings on world wide practices (e.g. looking at how research into attachment can inform practices in nurseries and schools etc )

Peer Reviews can also be used to:

(1)  Allocate funding to Universities/decide on a rating for University departments  based in the quality and impact of their research studies. For example, if a University is consistently producing high quality research that is having a massive/positive impact on the treatment of mental illnesses, it is likely that this University department will be awarded funding in order to continue with the production of this research.

(2)  Suggest amendments and improvements  reviewers may recommend that the procedure of the experiment is modified to make it more valid/accurate, they may suggest that the sample of participants used is expanded/increased to make the research hold higher population validity etc

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Psychological Research

The Reliability and Validity of Research

Learning objectives.

• Define reliability and validity

Interpreting Experimental Findings

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this experiment 100 times, we would expect to find the same results at least 95 times out of 100.

The greatest strength of experiments is the ability to assert that any significant differences in the findings are caused by the independent variable. This occurs because random selection, random assignment, and a design that limits the effects of both experimenter bias and participant expectancy should create groups that are similar in composition and treatment. Therefore, any difference between the groups is attributable to the independent variable, and now we can finally make a causal statement. If we find that watching a violent television program results in more violent behavior than watching a nonviolent program, we can safely say that watching violent television programs causes an increase in the display of violent behavior.

Reporting Research

When psychologists complete a research project, they generally want to share their findings with other scientists. The American Psychological Association (APA) publishes a manual detailing how to write a paper for submission to scientific journals. Unlike an article that might be published in a magazine like Psychology Today, which targets a general audience with an interest in psychology, scientific journals generally publish peer-reviewed journal articles aimed at an audience of professionals and scholars who are actively involved in research themselves.

Link to Learning

The Online Writing Lab (OWL) at Purdue University can walk you through the APA writing guidelines.

A peer-reviewed journal article is read by several other scientists (generally anonymously) with expertise in the subject matter. These peer reviewers provide feedback—to both the author and the journal editor—regarding the quality of the draft. Peer reviewers look for a strong rationale for the research being described, a clear description of how the research was conducted, and evidence that the research was conducted in an ethical manner. They also look for flaws in the study’s design, methods, and statistical analyses. They check that the conclusions drawn by the authors seem reasonable given the observations made during the research. Peer reviewers also comment on how valuable the research is in advancing the discipline’s knowledge. This helps prevent unnecessary duplication of research findings in the scientific literature and, to some extent, ensures that each research article provides new information. Ultimately, the journal editor will compile all of the peer reviewer feedback and determine whether the article will be published in its current state (a rare occurrence), published with revisions, or not accepted for publication.

Peer review provides some degree of quality control for psychological research. Poorly conceived or executed studies can be weeded out, and even well-designed research can be improved by the revisions suggested. Peer review also ensures that the research is described clearly enough to allow other scientists to replicate it, meaning they can repeat the experiment using different samples to determine reliability. Sometimes replications involve additional measures that expand on the original finding. In any case, each replication serves to provide more evidence to support the original research findings. Successful replications of published research make scientists more apt to adopt those findings, while repeated failures tend to cast doubt on the legitimacy of the original article and lead scientists to look elsewhere. For example, it would be a major advancement in the medical field if a published study indicated that taking a new drug helped individuals achieve a healthy weight without changing their diet. But if other scientists could not replicate the results, the original study’s claims would be questioned.

Dig Deeper: The Vaccine-Autism Myth and the Retraction of Published Studies

Some scientists have claimed that routine childhood vaccines cause some children to develop autism, and, in fact, several peer-reviewed publications published research making these claims. Since the initial reports, large-scale epidemiological research has suggested that vaccinations are not responsible for causing autism and that it is much safer to have your child vaccinated than not. Furthermore, several of the original studies making this claim have since been retracted.

A published piece of work can be rescinded when data is called into question because of falsification, fabrication, or serious research design problems. Once rescinded, the scientific community is informed that there are serious problems with the original publication. Retractions can be initiated by the researcher who led the study, by research collaborators, by the institution that employed the researcher, or by the editorial board of the journal in which the article was originally published. In the vaccine-autism case, the retraction was made because of a significant conflict of interest in which the leading researcher had a financial interest in establishing a link between childhood vaccines and autism (Offit, 2008). Unfortunately, the initial studies received so much media attention that many parents around the world became hesitant to have their children vaccinated (Figure 1). For more information about how the vaccine/autism story unfolded, as well as the repercussions of this story, take a look at Paul Offit’s book, Autism’s False Prophets: Bad Science, Risky Medicine, and the Search for a Cure.

Reliability and Validity

Everyday connection: how valid is the sat.

Standardized tests like the SAT are supposed to measure an individual’s aptitude for a college education, but how reliable and valid are such tests? Research conducted by the College Board suggests that scores on the SAT have high predictive validity for first-year college students’ GPA (Kobrin, Patterson, Shaw, Mattern, & Barbuti, 2008). In this context, predictive validity refers to the test’s ability to effectively predict the GPA of college freshmen. Given that many institutions of higher education require the SAT for admission, this high degree of predictive validity might be comforting.

However, the emphasis placed on SAT scores in college admissions has generated some controversy on a number of fronts. For one, some researchers assert that the SAT is a biased test that places minority students at a disadvantage and unfairly reduces the likelihood of being admitted into a college (Santelices & Wilson, 2010). Additionally, some research has suggested that the predictive validity of the SAT is grossly exaggerated in how well it is able to predict the GPA of first-year college students. In fact, it has been suggested that the SAT’s predictive validity may be overestimated by as much as 150% (Rothstein, 2004). Many institutions of higher education are beginning to consider de-emphasizing the significance of SAT scores in making admission decisions (Rimer, 2008).

In 2014, College Board president David Coleman expressed his awareness of these problems, recognizing that college success is more accurately predicted by high school grades than by SAT scores. To address these concerns, he has called for significant changes to the SAT exam (Lewin, 2014).

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• Analyzing Findings. Authored by : OpenStax College. Located at : https://openstax.org/books/psychology-2e/pages/2-3-analyzing-findings . License : CC BY: Attribution . License Terms : Download for free at https://openstax.org/books/psychology-2e/pages/1-introduction.

consistency and reproducibility of a given result

accuracy of a given result in measuring what it is designed to measure

General Psychology Copyright © by OpenStax and Lumen Learning is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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PSYCH 3002 - Research Methods in Psychology

• APA PsycInfo
• Google Scholar
• Find Cited Articles
• Find "Cited by" Articles
• Browsing Best Journals
• Psychology Journals
• Is it Peer Reviewed?
• Interlibrary Loan

Checking for Peer-Review - Three Options

Here are three methods for the checking peer-reviewed (refereed) status of a journal:

• OneSearch - easiest method if you have the title of an article  
• UlrichsWeb - most comprehensive list of journals ( currently only works on campus ) (must have journal name)  
• Scholar's Portal - if you're off campus and you only have the name of the journal

About Peer Review and Databases that limit to Peer-Reviewed Journals

Peer-review check - option #1 - onesearch (library website).

Peer-Review Check - Option #2 - "UlrichsWeb"

For instance if I search for journals with the word "Psychology" in the title UlrichsWeb indicates that:

Film Psychology  is not refereed (it is not peer reviewed)

Political Psychology is refereed (it is peer reviewed )

Pastoral Psychology is  refereed (it is peer reviewed )  

WARNING  the black-and-white  referee icon  is the icon to watch for when determining if a journal is peer-reviewed (refereed).  Do not pay any attention to the gold stars (which are labeled "reviewed" ... but in this case that just means the journal was "reviewed" by a librarian for UlrichsWeb ... it does NOT mean that the journal is "peer reviewed").

You can also click on the title of the entry - e.g., Political Psychology - which will generate a table with details about the journal - including whether it is refereed (peer-reviewed).

Peer-Review Check - Option #3 - "Scholar's Portal"

• More about Scholar's Portal

To maintain high levels of quality and reliability, the most respected and dependable journals in Sociology require all manuscripts (potential articles) to be reviewed by other experts (peers) to determine whether the submitted scholarship meets the high standards of the journal.

This process is called peer review and journals that utilize peer review are often referred to as refereed journals .  

To be sure you are using the highest quality research and scholarship in your projects you should gather your materials from peer reviewed journals / refereed journals    

How can you be sure you are working with a peer-reviewed journal?

When searching OneSearch you can limit your results after you search by clicking on the " Peer-reviewed Journals " option found on the right-hand column of the results page:   

However, you might find an article using Google Scholar or from a reference list or using the cited by option in Google Scholar. Since you can't limit to peer-reviewed journals with these techniques you need to use the database UlrichsWeb  or the Scholar's Portal to determine if the journal that published your article was refereed (peer-reviewed).      

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Peer review is a process that takes place before a study is published to check the quality and validity of the research, and to ensure that the research contributes to its field. The process is carried out by experts in that particular field of psychology.

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MINI REVIEW article

Career construction theory: tools, interventions, and future trends.

• School of Education, Huainan Normal University, Huainan, China

With the emergence of the borderless career era in the 21st century, career coaching has experienced a change from career guidance and career education to career counseling. Career construction theory has been widely used in career counseling and has substantial application value. Introducing career construct theory’s assessment tools and intervention strategies is necessary and meaningful. In this mini-review, the qualitative assessment tools and intervention approaches of career construct theory are introduced and analyzed; the qualitative assessment tools include the Career Construction Interview and “My Career Story” workbook, and the intervention approaches include the Computer-Assisted Career Counseling System, workshops, group counseling, and individual counseling. Finally, future research directions are proposed, including an analysis of what kinds of career construction interventions are most effective for which groups and under what conditions, career intervention in the digital age, and the standardization of assessment tools. The novelty of this paper lies in the fact that it purposefully proposes future directions for career construction theory from the perspectives of assessment tools and intervention approaches and that research on the assessment tools and intervention approaches of career construction theory still needs further attention.

Introduction

Career counseling has changed from career guidance and career education to career counseling. In the 19th century, career counseling was centered on the matching career guidance model, which is making rational decisions based on self and career information. After entering the 20th century, career counseling is based on career development theory, focusing on how individuals make decisions, a process-oriented career intervention. Furthermore, beginning in the 21st century, career counseling centers on career construction theory, focusing on vocational personality, career adaptability, and life theme, emphasizing constructing careers. These three theoretical models are the career guidance model to determine the person-job match, career education to promote career development, and career counseling to design work-life.

Career construction theory

Savickas (2005) proposed the career construction theory based on personal constructivism, social constructionism, and post-modernity. Career construction theory believes that the essence of individual career development is the dynamic construction process of pursuing mutual adaptation between the subjective self and the external objective world, and different people construct different stories. Career construction theory provides a dynamic perspective to give personal meaning to memories, present experiences, and plans, constructing careers through a sense of meaning and clarifying future directions. The theory includes three parts: vocational personality, career adaptability, and life theme. Occupational personality refers to an individual’s career-related abilities, needs, values, and interests. Career adaptability is described as “a psychosocial construct that denotes an individual’s resources for coping with current and imminent vocational development tasks, transitions, traumas” ( Savickas and Porfeli, 2012 , p. 662). The difference between occupational personality and career adaptability is that occupational personality emphasizes the content of a career, while career adaptability emphasizes the coping process of constructing a career. Career adaptability deals with how individuals construct careers, while occupational personality deals with what careers they construct. Career adaptability deals with how individuals construct their careers, while occupational personality deals with what careers they construct. Unlike vocational personality and career adaptability, life theme is a dynamical system that primarily explains why individuals make career choices and the significance of those choices and expresses the uniqueness of the individual in a particular context, which provides a way of looking at the world. Career counseling, developed from career construction theory, focuses on vocational personality, career adaptability, and constructing meaning in life themes ( Savickas, 2013 ). Vocational personality focuses on the “what,” career adaptability is about the “how,” and life theme responds to the “why” ( Guan and Li, 2015 ).

Compared to other career theories, career construction theory helps students adapt to the future’s complex and changing career world and inspires a richer perspective on career development ( Gao and Qiao, 2022 ). Meta-analysis has shown that social construction theory is more effective than individual-environmental matching theory ( Langher et al., 2018 ). Career construction theory seeks to explain the interpersonal process in which individuals construct the self, establish the direction of career behavior, and assign meaning to careers, providing a unique perspective on how to view the subject of career counseling ( Hou et al., 2014 ). Career construction theory provides specific ideas to help the case make career decisions and enhance work satisfaction ( Savickas, 2005 ). Therefore, this review aims to introduce the tools, interventions, and future directions of career construction theory to help individuals better adapt to the rapidly evolving situation.

To ensure the quality of the literature, the terms “career construction” and “intervention” were used as search terms in this study, both of which appeared in the title, abstract, or keywords. A comprehensive search was conducted on the “Web of Science, PsycINFO, and EBSCO.” The search was limited to English-language articles. Specifically, the literature was searched from 2013 to 2023. In addition, only standard research papers were included in this study, excluding review-type articles.

Life design counseling

Life design counseling is based on career construct theory, which gives meaning to life and supports adaptive responses by helping the individual to tell a career story, constructing the past, present, and future to form continuity and consistency. The five assumptions of the life design model of vocational intervention are contextual possibilities, dynamic processes, non-linear progression, multiple perspectives, and personal patterns. Life design counseling is lifelong, holistic, contextual, and preventive. It aims to increase the client’s adaptability, narratability, and activism ( Savickas et al., 2009 ).

The life design paradigm relies on story construction and action. The first stage of life design counseling is constructive, which involves clarifying the problem and what one hopes to achieve through counseling. The counselor encourages the client to find the life theme by describing the problem to be solved through a story. The second is deconstruction, which helps the client reflect on and shape the story by allowing them to clearly express experiences, expectations, actions, and expectations for the future. The third stage is reconstruction. The counselor and the client can interpret the story from different perspectives, thus enabling the client to rewrite his or her story. The fourth is the co-construction stage. The issues raised by the client are put into the rewritten story, and a new story is co-constructed as a solution. The fifth stage is action. Assign participation in some of the narrative’s possible self-relevant activities. It is necessary to specify what they will do and what this means to help the client make a plan ( Savickas et al., 2009 ).

Career construction interview

The Career Construction Interview is a structured process based on life design counseling designed to help clients tell, hear, and enact their life career stories. Counselors help them to coherently tell their career story, cope with changes in the environment, design a meaningful life, and take action by conducting a qualitative career assessment with a narrative model and methodology. The career construction interview comprises five questions, each leading to a thematic story. Role Models are to identify adjectives that describe self-constructs and concepts. Favorite magazines/TV/websites are to identify the types of environments and activities that interest the client. Favorite stories are understanding the stories or cultural scripts the client might use to envision transformational outcomes. Favorite mottos can give the client some advice. Early recollections can provide insight into how the client perceives the issues presented in the transition narrative ( Savickas, 2011 ).

At the beginning of the second phase, the counselor draws a portrait of life-occupation based on the client’s answers to CCI questions, combined with observation and reflection. By reviewing the story together and encouraging reflection and reflexivity in the conversation, the counselor and client construct a powerful new life-career identity that has coherent meaning for the client’s life. In the third phase, the client develops an action plan with the counselor. The career interest results obtained from the CCI correlate moderately with the quantitative Career Interest Inventory results, which suggests that the CCI agrees with traditional quantitative assessment tools ( Barclay and Wolff, 2012 ). Barclay (2018) provided three additional ways to use the CCI: written exercises, career collages, and career portfolios. Lindo and Ceballos (2019) developed the Child and Adolescent Career Construction, which includes the development of appropriate expressive arts to promote self-expression and career exploration in children ages nine and older. The CACCI includes a socio-emotional focus that encourages clients to explore self-concepts, life themes, and career awareness.

My Career Story

“My Career Story” is a career autobiographical workbook developed based on the Life Design Paradigm and contains written exercises and goal-setting activities essential to successful career planning ( Brown and Ryan Krane, 2000 ). It corresponds to the construction, deconstruction, reconstruction, co-construction, and action of life design counseling ( Hartung and Santilli, 2017 ).

MCS is designed to help clients tell, hear, and enact their life-career stories about who they are, who they want to be in the world of work, and how they can connect themselves to careers they might enjoy. Individuals, groups, and educators can use MCS to guide self-reflection to increase narrative identity, intentionality, and adaptability in career planning, career choice, and work adjustment. The MCS workbook consists of three sections to guide clients in telling their life stories. The first section, “Telling My Story,” begins by defining the participant’s problem, listing the careers they have considered pursuing and how they would like the workbook to benefit them. Next, participants answered questions related to life-career topics: (1) role models they admired while growing up, (2) favorite magazines and television shows, (3) stories from favorite books or movies, (4) favorite mottos. The second part is “Listening to My Story.” Portrait their lives by integrating smaller stories into a more cohesive career story. Including (1) Who will I be? (2) Where do I like to be? (3) The portrait summarizes (4) Rewrite my story. The third section, “Enacting My Story,” involves creating a realistic plan to implement the story ( Santilli et al., 2019 ). The MCS can be used by clients alone or with the counselor’s assistance. As an adjunct to career counseling, the MCS can be used in one-on-one individual and group counseling and career development learning activities in the classroom or other settings ( Hartung and Santilli, 2017 ).

Career intervention

Twenty-two studies published between 2013 and 2023 met all criteria and provided the necessary data for the systematic review. Databases included Web of Science, PsycINFO, and EBSCO. Two authors screened all titles and abstracts. In addition, they considered the eligibility of full-text articles. First, the databases were searched with the keywords “career construct theory” and “intervention.” Furthermore, a citation search was conducted for key papers, and reference checking was performed as suggested by Tuttle et al. (2009) . Thus, the search strategy was iterative and multi-stage, including computerized and manual searches. Therefore, it can be concluded that these searches were adequate for a systematic review. Finally, 22 studies were identified, including three qualitative and 19 quantitative studies. The two authors evaluated these studies against the selection criteria and agreed on the final 22 studies. Figure 1 depicts the process of selection.

Figure 1 . PRISMA flow chart.

These studies review intervention research on career construction theory, as shown in Table 1 .

Table 1 . Intervention studies.

Career group

Career Group is based on group counseling theory and can promote the cognitive, emotional, and behavioral aspects of an individual’s career development. Career group guidance and career group counseling are two forms of career groups. The difference is that career group guidance has more participants and focuses on transferring knowledge. Career counseling has fewer members and emphasizes interaction and communication between members ( Jin, 2007 ).

Researchers examined the effects of life design group guidance on 9th grade. Findings supported the effects of life design group guidance on career identity, career adaptability, and career decision-making self-efficacy ( Cardoso et al., 2022 ). Maree (2019) used quantitative and qualitative research methods to conduct group career construct counseling with 11th-grade students. The Career Adaptability Scale was used for quantitative analysis. Career interest analysis and Maree Career Matrix were used for qualitative intervention. The findings revealed that students significantly improved career adaptability. Maree et al. (2019) explored the impact of life design group counseling on unemployed young adults’ career adaptability. First, the Career Interest Profile was used to obtain information about career choices: work-related information, five most and least preferred career preferences, six career choice questions, and 15 career story narrative questions. Career counseling techniques such as career genealogy charts, interviews, and personal statements were used. Results indicated that life design group counseling increased participants’ career adaptability.

Recently, Gai et al. (2022) used career construct theory to develop a peer motivational interview that included engagement, focus, arousal, and planning. The research involved senior students conducting one-on-one career motivational interviews with junior students. Results indicated that the intervention increased students’ career control and career confidence. Cook and Maree (2016) compared the effects of career construction group counseling and a life-oriented curriculum on 11th-grade students in different educational settings. The group counseling included Collage, the Career Interest Profile, and the lifeline technique. Participants demonstrated higher career adaptability after participating in career construction group counseling. Maree et al. (2017) used career construction group counseling. The experimental group completed narrative questions in the Career Interest Profile. They created career collages depicting how they see their future. In addition, “My Lifeline” was drawn to mark essential themes in their lives. The quantitative study results indicated that life design group counseling did not increase participants’ career adaptability compared to the traditional program.

Seminar is another form of group counseling. Seminars are less frequent and intensive than group counseling, with more fixed topics and less interaction between members, making them an efficient method ( Jin, 2007 ).

Life design counseling can reduce indecision, anxiety, uncertainty, and insecurity among college students ( Obi, 2015 ). Maree and Symington (2015) designed eight life design workshops with five 11th-grade students in a private school. The students demonstrated increased effort to address issues related to career concern, control, curiosity, and confidence, suggesting that the intervention facilitated the development of their career adaptability. Cadaret and Hartung (2021) designed career construction group counseling using the workshop format combining individual reflection and group discussion. The workshops were based on the My Career Story (MCS) workbook. The first session was “Telling My Story,” which included role models, favorite magazines/TV shows/websites, favorite books/movies, and favorite mottos. The second was “Hearing My Story,” which included describing myself and my interests, scripting roles, making suggestions, and constructing a life portrait. The third session, “Enacting My Story,” included co-setting goals, seeking more information, and exploring pathways to select and identify career goals. Results indicated that the career construction intervention increased students’ career control and confidence. Ginevra et al. (2017) used a life design approach to develop resources that help cope with career transitions, encourage thinking about the future, identify one’s strengths, and plan future projects. It is divided into three phases. Participants were encouraged to tell, revise, and construct their career stories in the first phase. In the second phase, participants were administered an online questionnaire on hope, optimism, resilience, future direction, and career readiness. Consider their strengths in response to the career change in the third phase. Results indicated that the life design approach improved their career adaptability.

Peila-Shuster et al. (2021) used career construct theory to conduct career workshops with adults who had been unemployed for more than six months. Workshops included current status, describing role models, favorite mottos, rewriting stories, reflections, and action plans. The counseling utilizes the My Career Storybook to help participants cope with their problems and prepare for their job search by facilitating narratability, intentionality, and career adaptability. Da Silva et al. (2022) conducted career construct interviews with students. The interview consisted of three workshops that (1) discussed role models, television shows, books or movies, mottos, and early memories; (2) Exploring participants’ answers to career construct interview questions; (3) Discuss the steps needed to implement a new career plan. The study showed that the Career Construct Interview promotes the development of students’ career adaptability and remains stable 3 months after the intervention. This suggests that the intervention of the Career Construct Interview has a good latency. Santilli et al. (2019) compared the impact of life design and traditional career counseling on adolescents. Life design group counseling utilized the “My Career Story” workshop format. The results showed that the intervention promoted the development of career adaptability in the Life Design group. This suggests that “My Career Story” may be an effective means of developing career adaptability in adolescents.

However, the study yielded inconsistent results. Researchers examined the impact of the life design workshop on 9th and 12th-grade students. The intervention utilized the “My Career Story” life design methodology. The results showed that the life design intervention did not impact students’ career adaptability ( Cardoso et al., 2018 ).

Online career group

The advantage of online interventions is the availability of audiovisual materials, including videos, slideshows, and animations, which help students explore values, interests, and skills independently. Online career counseling is more accessible than traditional career guidance, and students can access various practical information ( Chen et al., 2022 ).

Nota et al. (2016) compared the validity of online life-based design and traditional test interpretations. All students received personalized feedback, including suggestions for future schools and jobs related to their interests, values, and motivation. Results indicated that the online life design group demonstrated higher career adaptability, life satisfaction, and future aspirations. The researchers compared online and face-to-face life design counseling on career development. The online interventions included an introduction to online books, bilingual career videos, short animations, access to a virtual library, an introduction to similar websites that promote career development, and online chats with career counselors. The results showed that online and face-to-face career interventions improved students’ career development ( Pordelan et al., 2018 ). Later, they compared life design digital storytelling and face-to-face storytelling, and the study found that the digital storytelling group had higher career decision self-efficacy than face-to-face storytelling ( Pordelan et al., 2021 ).

Zammitti et al. (2023) conducted a life design paradigm online career intervention with college students to enhance their psychological resources. The online intervention consisted of career workshops and 13 online activities. The study showed that an online group career intervention in the life design paradigm promotes the development of resilience, subjective risk intelligence, career adaptability, self-efficacy, optimism, hope, and life satisfaction. Camussi et al. (2023) foster the development of student’s skills to face complexity and unpredictability, transforming their time perspective into optimism to face the future. The intervention was based on the theoretical model of Life Design. The intervention themes were “My future, why?” and “Who am I and who do I want to be?.” The intervention consisted of two online workshops. It included reflections on conscious life design and current global contextual challenges. The study demonstrated that the Life Design online intervention facilitated the development of students’ levels of career adaptability, courage, time perspective, and resilience.

Individual career counseling

Individual career counseling is usually a one-on-one approach that assists with career confusion to enhance career adaptability. Individual counseling has the highest cost but significantly impacts the client and the counselor.

The value of individual career counseling is to help all those challenged by unemployment and poverty (especially emerging adults) to become employable, find decent work, and increase their sense of self, and in the process, promote the idea of a fair and just society ( Maree and Twigge, 2016 ). Maree and Gerryts (2014) conducted narrative counseling with a newly young male engineer based on career construct theory. Methods included collage, Career Interest Profile, life chapters, lifeline, early recollections technique, and Career Construction Interview. Participants demonstrated an increase in willingness to cope with challenges and adaptive strategies. This suggests that narrative counseling can facilitate the development of career adaptability. Maree (2016) conducted career construction counseling with a mid-career Black man to construct, deconstruct, reconstruct, and co-construct the client’s life story. The interview included role models, favorite magazines/TV/websites, favorite stories, early memories, and favorite mottos. The client demonstrated an improved self-awareness and a willingness to be more flexible in dealing with challenges related to the career.

Career assessment

The Career Construction Interview and MCS workbook are two qualitative assessment tools under the Career Construction Theory. The groups for which the tools are applicable may be different. The adult population may be more suitable for the Career Construction Interview, and most individual counseling uses the Career Construction Interview ( Maree and Gerryts, 2014 ; Maree, 2016 ; Maree and Twigge, 2016 ). Quantitative tools for career construction intervention mostly use a career adaptability scale ( Maree and Symington, 2015 ; Ginevra et al., 2017 ; Maree et al., 2019 ).

Quantitative assessment is a standardized and scientific measurement tool but has certain disadvantages. The advantage of qualitative assessment is that it facilitates the discussion of group career counseling and can improve the shortcomings of quantitative tools. The case study of self-narrative can help the researcher to sort out the main conflicts and critical variables in career development ( Guan and Li, 2015 ). Integrative Structured Interview, based on the system’s theoretical framework, is a method that combines qualitative and quantitative measures to advance storytelling. Using Hollander’s interests as the basis for quantitative assessment, integrating assessment results with storytelling, the integrative structured interview facilitates this integration through quantitative score-based career storytelling that focuses not on the scores but on facilitating participants’ understanding of their scores, career decisions, and transitions ( McMahon et al., 2020 ). Therefore, it is necessary to develop a hybrid standardized assessment method based on career construction theory.

Career construction theory has been widely used in the field of career counseling. Career group counseling is guided by the theory of career construction, and career intervention programs are designed for the career construction process of different groups, which can effectively solve the problems faced by different groups in career development. Individual career counseling can help cases to link their self-concept with their work through the self-construction of work so that individuals can become the creators of their work and actively construct the meaning of their careers to be prepared for the new changes in the work pattern.

Brown and Ryan Krane (2000) meta-analysis identified five critical elements of career counseling: written exercises and workbooks, individualized explanations and feedback, career world information, role modeling, and building support. The MCS workbook corresponds to the written exercises and workbooks among the key elements. Another meta-analysis indicated that the three critical elements of career counseling are counselor support, value clarification, and psycho-educational intervention ( Whiston et al., 2017 ). The career construction interview gained direct counselor support and clarification of specific values. Combining the MCS manual and supporting materials may effectively develop career adaptability in adolescents ( Santilli et al., 2019 ). However, some research suggests that ninth graders show more difficulty than twelfth graders in recounting their own experiences ( Cardoso et al., 2018 ). This may be because the career construction interview is more helpful for lower grades, which require direct support and clarification of specific values from the counselor.

Currently, the primary interventions of career construction theory are computer-assisted career counseling systems, workshops, group counseling, and individual counseling. Career courses are the most effective ( Oliver and Spokane, 1988 ). Therefore, converting life design counseling into a career course is warranted, and a career construction orientation curriculum needs to be developed to enrich the career construction intervention. A meta-analysis by Whiston et al. (2003) demonstrated that intervention effectiveness significantly increases using a computerized career guidance system in counseling. Various career intervention approaches are often integrated into practice, mainly using computerized career guidance with other modalities. The study found that a comprehensive intervention combining online life design and written exercises was more likely to increase students’ career adaptability and life satisfaction ( Nota et al., 2016 ).

Future research trends

Although the assessment tools for career construct interventions have been enriched in recent years, the stability, validity, and applicability of the assessment tools still need to be tested in the further. Career construction interventions focus on the reconstruction of life stories. Some studies have found that career construct interventions did not increase students’ career adaptability ( Maree et al., 2017 ; Cardoso et al., 2018 ). This suggests that relying on the Career Adaptability Scale as a quantitative study is insufficient, some questionnaires should be designed to measure whether students can articulate and identify what is important to them before and after the intervention. Assessment tools for career construction intervention mainly consist of qualitative or quantitative tool, but standardization still needs to be improved ( Di Fabio, 2016 ; Cardoso et al., 2022 ). Some studies utilize quantitative and qualitative assessment tools ( Maree, 2020 ), but they need more cross-cultural validation.

Therefore, future research in assessment tools can consider the following aspects: In terms of assessment content, special assessment tools must be prepared for different and unique groups. Savickas (2011) developed a complete set of guidelines for career construction counseling. Online guidelines and assessment tools could be developed in the future, incorporating technologies such as computer networks and multimedia. In particular, comprehensive assessment tools that include quantitative and qualitative aspects should be developed to meet the needs of large-scale research with different groups and achieve standardization and stability of assessment methods.

First, there is a question of what groups and career interventions are most effective under what conditions. The economic benefits of career interventions in different modalities, age groups, and various intervention goals are critical. The meta-analysis result indicated that the career course was the most effective but required the most intervention time. Individual counseling produced more benefits per session than other interventions ( Oliver and Spokane, 1988 ). Subsequently, meta-analysis yielded different results. Individual career counseling was the most effective, followed by group career counseling, with career courses coming in third. Computerized online systems were the most cost-effective ( Whiston et al., 1998 ). A recent meta-analysis indicated that individual counseling was the most effective, while group and individual counseling and computer-based interventions varied widely ( Whiston et al., 2017 ). Meta-analyses have not yet yielded consistent conclusions. In addition to the results, individual and group counseling are effective methods. However, at the same time, it is essential to consider the number of people and the economic benefits that professional interventions can bring ( Whiston, 2011 ).

Additionally, the results showed differences in intervention impact based on the participants’ grades. Ninth graders only improved at the level of career certainty, while twelfth graders showed more significant development on all measured variables. This may be because higher-graders can better understand what is important to them and what they strive for. Therefore, it is essential to consider the characteristics and needs of different groups to maximize the effectiveness of career construction interventions in future research. Different intervention modalities affect individuals’ career development, which is best for group counseling and which works best for individuals. These issues must be better understood, requiring meta-analysis or systematic review to explore in the further.

Second, digital technology is essential for career interventions. In particular, Online interventions allow alternative experiences and role modeling to be more readily available through websites where short videos of successful people can be viewed and inspired. Therefore, career construction theory may benefit career interventions in the digital age. Online career construction interventions are very efficient and likely to be used more and more. Online career construction can present stories in short films, slideshows, or photographs, allowing the client and the counselor to discover hidden stories and help the client gain new concepts. The advantage of online career construction intervention is convenience, where stories can be opened on a computer or other electronic device. In the storytelling process, information technology is utilized as a platform for digital storytelling, where one’s life story is expressed as a photo, movie, or audio ( Pordelan et al., 2021 ). In the future, personalized interpretation and feedback procedures can be added to the computer-based online intervention to maximize the usefulness of the career construction intervention.

Finally, developing new content and a short career construction interview are necessary. Using career construction theory, the researcher developed a peer motivational interview for at-risk students that included engaging, focusing, evoking, and planning ( Gai et al., 2022 ). Questions include “What do you want to obtain from your future occupation? Why?” “What occupations are you likely to pursue in the future? What occupations are you unlikely to pursue? What occupations are you not sure about whether to pursue? How can you become certain?” Future research needs to focus on particular groups as subjects, focusing on those severely hindered in their career development or career transition, and test the effectiveness of career interviews through group interventions to maximize the effects of career interventions.

However, completing the career construction interview typically requires two 90-min sessions, which hinders its practical use with many students in school. Therefore, Rehfuss and Sickinger (2015) developed a short form of the career construction interview. Only three initial career construction interview questions were used in the short form. “Who did you admire when you were growing up? What are your favorite magazines, TV shows, or websites? Tell me your favorite saying or motto.” These three questions were used to learn about the students’ role models, self-advice to help solve current problems, and preferences for the work environment. In addition, there is a need to develop a short form of the Life Design Group Counseling and MCS. Also, some form of screening is necessary to determine what questions of the career construction intervention will benefit the individual the most.

Career construction theory applies to the current borderless career era, and such a career theory perspective is more helpful for individuals to adapt to the complex and changing career world in the future. Currently, the tools of career construction theory mainly include the structured career construction interview and the qualitative assessment manual of MCS. The interventions of the theory mainly include workshops, group counseling, online group counseling, and individual interviews. This study identified several challenges to the career construction tools and interventions.

Therefore, it offers some suggestions on how to deal with these challenges: Future researchers need to pay attention to the development of comprehensive quantitative and qualitative assessments to standardize and stabilize assessment methods for the tools. For the interventions, there is a need to examine the question of what groups and under what conditions career interventions are most effective. Second, future research should develop personalized interpretation and feedback procedures for computerized online interventions in the digital age. Finally, developing new content and a short career construction interview are necessary.

Author contributions

DW: Writing – review & editing, Writing – original draft. YL: Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

This study would like to thank and extend our sincere gratitude to the reviewers.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: career construction theory, My Career Story, career construction interview, career intervention, future trends

Citation: Wang D and Li Y (2024) Career construction theory: tools, interventions, and future trends. Front. Psychol . 15:1381233. doi: 10.3389/fpsyg.2024.1381233

Received: 03 February 2024; Accepted: 25 March 2024; Published: 05 April 2024.

Reviewed by:

Copyright © 2024 Wang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yanling Li, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Open access
• Published: 11 April 2024

Teachers and educators’ experiences and perceptions of artificial-powered interventions for autism groups

• Guang Li 1 ,
• Mohammad Amin Zarei 2 ,
• Goudarz Alibakhshi 2 &
• Akram Labbafi 3  

BMC Psychology volume  12 , Article number:  199 ( 2024 ) Cite this article

Metrics details

Artificial intelligence-powered interventions have emerged as promising tools to support autistic individuals. However, more research must examine how teachers and educators perceive and experience these AI systems when implemented.

The first objective was to investigate informants’ perceptions and experiences of AI-empowered interventions for children with autism. Mainly, it explores the informants’ perceived benefits and challenges of using AI-empowered interventions and their recommendations for avoiding the perceived challenges.

Methodology

A qualitative phenomenological approach was used. Twenty educators and parents with experience implementing AI interventions for autism were recruited through purposive sampling. Semi-structured and focus group interviews conducted, transcribed verbatim, and analyzed using thematic analysis.

The analysis identified four major themes: perceived benefits of AI interventions, implementation challenges, needed support, and recommendations for improvement. Benefits included increased engagement and personalized learning. Challenges included technology issues, training needs, and data privacy concerns.

Conclusions

AI-powered interventions show potential to improve autism support, but significant challenges must be addressed to ensure effective implementation from an educator’s perspective. The benefits of personalized learning and student engagement demonstrate the potential value of these technologies. However, with adequate training, technical support, and measures to ensure data privacy, many educators will likely find integrating AI systems into their daily practices easier.

Implications

To realize the full benefits of AI for autism, developers must work closely with educators to understand their needs, optimize implementation, and build trust through transparent privacy policies and procedures. With proper support, AI interventions can transform how autistic individuals are educated by tailoring instruction to each student’s unique profile and needs.

Peer Review reports

Introduction

Autism education has become an increasingly important area of focus in recent years due to the rising prevalence of autism spectrum conditions (ASC) among children. The estimated prevalence of ASC has increased from 1 in 10,000 in the 1960s to at least 1 in 100 today [ 1 , 2 , 3 ]. ASC is a neurodevelopmental condition characterized by impaired social interaction and communication abilities and stereotypical or obsessive behavior patterns. These impairments can significantly impact an individual’s social, educational, and employment experiences, leading to poor long-term outcomes and difficulties in social transactions, independent work, and job fulfillment [ 4 , 5 , 6 , 7 , 8 , 9 , 10 ].

The reported prevalence of autism spectrum disorders (ASDs) in developed countries is around 2% [ 11 ]. ASDs typically manifest within the first three years of life. They are characterized by challenges in social interaction, speech and language delays, avoidance of eye contact, difficulty adapting to changes in the environment, display of repetitive behaviors, and differences in learning profiles [ 11 , 12 , 13 ]. Those with ASDs, including children and adults, have a high frequency of anxiety and depression. Neurobiological research has revealed differences in brain development between children with ASDs and neurotypical children [ 14 ]. These excessive connections are thought to be due to reduced pruning of damaged neuronal connections during brain development, resulting in disordered neural patterning across the brain and dysregulation in cognitive function coordination between different brain regions [ 14 , 15 ].

The dominant perspective regarding AI technologies has revolved mainly around understanding these systems as a collection of processes and their corresponding responses, emphasizing autonomy, adaptability, and interactivity [ 16 , 17 , 18 , 19 , 20 , 21 ]. These characteristics are considered fundamental technological focuses that researchers argue should be integral to AI systems. Although autonomy, adaptability, and interactivity are significant, they may only cover some essential criteria for an adequate K-12 education. Specifically, these criteria are about skills taught by human educators, such as B. Self-efficacy, technical skills, and socialization skills. Samuel [ 22 ] emphasizes that AI technologies should replicate human actions and mimic expressions of “human intelligence, cognition, and logic.” This highlights the need to refine features that determine effective AI in education. The recent challenges in education due to the pandemic provide a unique opportunity to examine the demands on stakeholders, including educators, students, and parents [ 23 , 24 , 25 , 26 , 27 ].

The potential of artificial intelligence (AI) to drive developments in education is well-recognized [ 6 , 7 ]. Artificial intelligence is one of the technological advancements which can be used in education. AI encompasses a range of technologies that aim to simulate human intelligence, including machine learning, natural language processing, and computer vision [ 8 ]. These technologies have already been used in various applications, from speech recognition to image classification, and can potentially revolutionize how we think about education. In the context of autism, AI has the potential to provide personalized learning experiences that are tailored to the specific needs of each child [ 8 ]. For example, AI-powered systems can analyze a child’s behavior and responses to stimuli and use this information to adapt the learning materials and activities to suit their needs. Furthermore, AI can also be utilized to support communication and social interaction, which are areas of difficulty for many children with autism [ 9 ].

AI-powered interventions in the context of autism education refer to the utilization of artificial intelligence technologies to create tailored and interactive experiences for individuals on the autism spectrum. These interventions encompass a spectrum of applications, including educational tools, therapeutic programs, and support systems designed to address the unique learning and social communication needs of individuals with autism. AI technologies such as machine learning, natural language processing, and computer vision are employed to analyze and respond to the specific behaviors, preferences, and challenges exhibited by each individual [ 1 , 2 , 3 , 4 , 5 , 6 ]. The goal is to provide personalized and adaptive learning experiences, enhance social interaction skills, and offer targeted support for cognitive and emotional development. Examples of AI-powered interventions include virtual reality scenarios, interactive games, and educational software that can dynamically adjust content based on real-time feedback, creating a more individualized and effective educational approach for children with autism [ 2 , 3 , 4 , 5 ].

Moreover, there is a risk of bias and discrimination in AI-powered interventions for children with autism. For example, if the AI system is trained on data that is not representative of the diverse population of children with autism, it may not be effective for all individuals [ 10 ]. Moreover, there is a risk of perpetuating harmful stereotypes or reinforcing inappropriate behaviors if the AI system is not designed and programmed with ethical considerations (10). Third, there are concerns about data privacy and security when using AI in education for children with autism. For instance, if sensitive personal information is collected and stored by the AI system, there is a risk that it could be misused or accessed by unauthorized parties [ 16 ]. Therefore, it is essential to address these challenges and concerns to fully realize the potential of AI in education for children with autism. By doing so, we can create evidence-based and ethically sound interventions that support personalized learning and social communication skills while mitigating the risks associated with AI-powered education.

The potential of AI in autism education lies in its ability to offer personalized learning experiences, tailoring interventions to the unique needs of each child [ 8 ]. By analyzing a child’s behavior and responses, AI can adapt learning materials, potentially revolutionizing education for children with autism. However, this transformative potential is not without challenges. The risk of bias and discrimination looms large, as AI systems may not be effective if trained on non-representative data, perpetuating harmful stereotypes [ 10 ]. Ethical considerations become paramount, addressing concerns about data privacy and security, which, if overlooked, pose potential risks associated with unauthorized access and misuse of sensitive information [ 16 ]. Bridging the gap between the promise of AI in education and its responsible application is crucial. Therefore, this study aims to explore educators’ experiences and perceptions of AI-powered interventions for autism, shedding light on the nuanced landscape where technological advancements intersect with the delicate realm of autism education.

Research questions

In line with the research gap mentioned in the previous section, the following research questions are raised:

What are the benefits and challenges of using AI-powered interventions to support the learning and social communication skills of children with autism from teachers’ and educators’ perceptions?

How can AI-powered interventions be designed and implemented to ensure that they are culturally and linguistically appropriate for a diverse population of children with autism while also avoiding bias and discrimination in the learning materials and activities?

Review of literature

Theoretical background.

Machine learning is a component of artificial intelligence (AI) wherein models perform tasks autonomously without human intervention. Traditional machine learning models are trained using input data, enabling accurate outcome predictions. Deep learning, a subset of machine learning, employs extensive data to prepare models, achieving similarly high prediction accuracies. Both models are frequently utilized in diagnosing neurological disorders such as autism [ 28 , 29 ], ADHD [ 30 , 31 ], and depression [ 32 , 33 ]. Diagnostic inputs encompass images from computerized tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) scans, or electroencephalogram (EEG) signals.

AI has been instrumental in social skills training for children with autism spectrum disorders (ASDs), aiding in recognizing and responding to social cues. Belpaeme et al. [ 34 ] utilized sensory features (facial expressions, body movements, and voice recordings) as inputs to a machine-learning model implemented in a robot for analyzing autistic children’s behavior and engagement levels during therapy. This study demonstrated the robot’s potential to adapt to interactants, influencing engagement. Another survey by Sanghvi et al. [ 35 ] employed postural expressions, specifically silhouette images of the upper body during chess playing, to analyze the engagement levels of autistic children. The integration of representative data with an affect recognition model suggested the potential for the robot to serve as a game-mate for autistic children in real-world scenarios. Kim et al. [ 36 ] employed audio recordings to assess the emotional states of autistic children, enhancing the robot’s ability to evaluate engagement and modify responses for a more interactive learning environment.

Various studies explored diverse input features such as facial expressions [ 37 ], body movements [ 38 ], and biosignals [ 39 ]. Esteban et al. [ 40 ] investigated facial expressions, direction of look, body posture, and voice tones as input features to a model within the NAO robot for assessing the social engagement of autistic children, showcasing the capability of robots to possess increased autonomy. Rudovic et al. [ 41 ] developed a personalized deep model using coordinated video recordings, audio recordings, and biosignals to assess engagement in autistic children, outperforming non-personalized machine learning solutions. Another study created a hybrid physical education teaching tool using speech recognition and artificial intelligence, achieving a recognition accuracy of over 90% for a voice interactive educational robot. Collectively, these studies affirm that AI holds promise in enhancing social interaction and supportive education for children with mental disorders.

Artificial intelligence and education

The use of AI technology in education has led to increased published studies on the subject, with a reported growing interest and impact of research on AI in education [ 42 ]. AI literacy, which refers to the capacity to comprehend the essential processes and concepts underpinning AI in various products and services, has been discussed in several studies [ 43 , 44 , 45 , 46 , 47 ]. Ng et al. [ 48 ] proposed a four-dimensional AI literacy framework covering knowing and understanding AI, using and applying AI, evaluating and creating AI, and AI ethics.

Recent review papers on AI in education have highlighted several major AI applications, such as intelligent tutoring systems, natural language processing, educational robots, educational data mining, discourse analysis, neural networks, affective computing, and recommender systems [ 22 , 23 , 33 – 34 ]. However, Chen et al. [ 49 ] identified some critical issues in their review paper on AI in education, including a lack of effort in integrating deep learning technologies into educational settings, insufficient use of advanced techniques, and a scarcity of studies that simultaneously employed AI technologies and delved extensively into educational theories. Furthermore, there needs to be more knowledge and discussion on the role of AI in early childhood education (ECE), an area often ignored in cutting-edge research.

Using AI to teach children with ASD

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that affects communication, social interaction, and behavior (1). The disorder is characterized by various symptoms and severity levels, making it challenging to provide effective interventions for affected individuals [12]. Children with ASD often experience difficulties in learning and require specialized educational interventions to help them achieve their full potential [1]. In recent years, there has been growing interest in the potential of AI to improve the learning outcomes of children with autism [8). AI has the potential to provide personalized learning experiences that are tailored to the specific needs of each child with autism [ 9 ]. For example, AI-powered systems can analyze a child’s behavior and responses to stimuli and use this information to adapt the learning materials and activities to suit their needs [8].

AI can also be used to support communication and social interaction, which are areas of difficulty for many children with autism [10]. Chatbots and virtual assistants can provide a non-judgmental and non-threatening environment for children to practice their social skills while providing feedback and guidance [ 23 ]. These interventions can be particularly valuable for children who struggle with face-to-face interactions or feel uncomfortable in social situations [ 24 ]. Despite the potential benefits of using AI in education for children with autism, several challenges and concerns need to be addressed:

First, there is a lack of consensus on the most effective ways to use AI to support learning for autistic children [ 8 ]. While there have been some promising results from initial studies, more research is needed to determine the most effective methods for using AI to personalize learning and support social communication skills in this population [10]. Second, there is a risk of bias and discrimination in AI-powered interventions for children with autism. For example, if the AI system is trained on data that is not representative of the diverse population of children with autism, it may not be effective for all individuals [ 9 ]. Moreover, there is a risk of perpetuating harmful stereotypes or reinforcing inappropriate behaviors if the AI system is not designed and programmed with ethical considerations [ 23 ]. And, third, there are concerns about data privacy and security when using AI in education for children with autism. For instance, if sensitive personal information is collected and stored by the AI system, there is a risk that it could be misused or accessed by unauthorized parties [10].

Several research studies have investigated the use of AI in education for children with autism. For example, Goodwin and Stone [8] developed an AI-powered system called Maki, which uses natural language processing to provide personalized feedback on social communication skills. The system was effective in improving social communication skills in children with autism. Similarly, Alzoubi et al. [ 50 ] developed an AI-powered system that uses virtual reality to provide social skills training for children with autism. The system was found to be effective in improving social skills and reducing anxiety in children with autism.

Other research studies have explored the potential of AI to improve different aspects of learning for children with autism. For example, Zhang et al. [ 10 ] developed an AI-assisted system that uses computer vision and machine learning to provide personalized feedback on handwriting skills. The system was effective in improving handwriting skills in children with autism. Similarly, Wang et al. [ 51 ] developed an AI-powered system that uses game-based learning to enhance math skills in children with autism.

There have also been efforts to develop AI-powered systems that can assist teachers and parents in providing effective interventions for children with autism. The system effectively improved the quality of interventions offered by teachers and parents. However, there are also concerns about the potential negative impacts of AI on children with autism. For example, some studies have suggested that excessive use of AI-powered interventions could reduce face-to-face interactions and social skills development [9]. Additionally, there are concerns about the potential for AI-powered interventions to replace human teachers and therapists, which could have negative implications for the quality of care provided to children with autism [8].

To address these concerns and maximize the potential benefits of AI for children with autism, it is essential to prioritize ethical considerations and involve stakeholders in designing and implementing AI-powered interventions [ 23 ]. This includes ensuring that AI systems are developed and programmed to avoid bias and discrimination, protecting the privacy and security of personal data, and promoting transparency and accountability in using AI in education for children with autism [ 10 ].

Other studies have investigated using chatbots and virtual assistants to support social communication skills in children with autism. For example, Kocaballi et al. [ 52 ] developed a chatbot called Tess that provides social skills training and support for children with autism. The chatbot was effective in improving social communication skills in children with autism. Similarly, Tanaka et al. [ 53 ] developed a virtual assistant called Miko that uses artificial empathy to support social communication skills in children with autism.

Further studies highlighted the importance of ethical consideration while using AL in education for children with autism. For example, there is a risk of perpetuating harmful stereotypes or reinforcing inappropriate behaviors if the AI system is not designed and programmed with ethical considerations [ 23 ]. Moreover, there is a risk of bias and discrimination if the AI system is trained on data that is not representative of the diverse population of children with autism [9]. Therefore, it is essential to carefully consider the ethical implications of using AI in education for children with autism. In conclusion, utilizing AI in education can transform how we think about learning and support children with autism to achieve their full potential.

Research Methodology

The study used purposive sampling to select 20 informants who met specific criteria. These individuals were parents or educators of autistic children and had valuable experience using AI-powered interventions to improve their children’s learning and social communication skills. They were all Iranian living in Tehran, Iran. 30% ( n  = 6) were female and 70% ( n  = 14) were male.

The participants in the study encompassed an age range spanning from 29 to 58 years old. Educators teaching experience was above 8 years. Recruitment efforts were conducted through various channels and social media platforms to ensure a diverse and representative sample. Potential participants were fully informed about the study’s purpose, procedures, and possible benefits throughout the recruitment process. They were also told of their rights as participants and the assurance of confidentiality. To confirm their willingness to participate, informants were asked for written consent before formal inclusion in the study.

Data collection

The study used semi-structured interviews and focus groups to collect data from the informants. The researcher developed the interview questions (Appendix), and a panel of three qualitative researchers reviewed their relevance. Interviews were conducted individually, either in person or virtually, and lasted approximately 45–60 min each. Focus groups with 3–5 participants conducted almost or in person were also organized. The duration of the focus group discussions was between 60 and 90 min. During the data collection process, the interviews and focus group sessions were audio-recorded to capture participants’ responses and insights accurately. These recordings were later transcribed verbatim, allowing a comprehensive analysis of the data collected. Through semi-structured interviews and focus groups, the study aimed to obtain complete and detailed information about participants’ experiences and perspectives regarding using AI-assisted interventions to support the learning and social communication skills of children with autism. The semi-structured nature of the interviews allowed for flexibility in exploring different topics while ensuring a consistent data collection framework for all participants. Additionally, the dynamic and interactive nature of the focus groups encouraged group discussions and allowed participants to share and build on one another.

Data analysis

Following the data collection phase, the study thoroughly analyzed the information collected. The audio recordings of the interviews and focus group sessions were transcribed verbatim, resulting in a comprehensive text dataset that captured participants’ responses and insights. The analysis began with a thorough familiarization process in which researchers immersed themselves in the transcribed data to understand participants’ accounts deeply. This immersion allowed researchers to identify recurring themes, patterns, and noteworthy information in the data set. A systematic analysis approach was used to ensure reliability and validity. Data were coded using a combination of inductive and deductive methods. First, an open coding process was conducted in which researchers generated initial codes by closely examining the data and labeling meaningful segments. As the analysis progressed, these codes were refined, grouped, and organized into categories and subcategories, creating a coding framework. After coding, researchers conducted a thematic analysis by identifying overarching themes from the data. The topics represented vital concepts, ideas, and perspectives shared by participants regarding the use of AI-assisted interventions to support the learning and social communication skills of children with autism. Throughout the analysis, the researchers ensured the accuracy and trustworthiness of the findings by employing techniques such as member checking, where participants were allowed to review and validate the interpretations made from their data.

Ethical considerations

The study adhered to ethical guidelines for conducting research with human subjects. Informed consent was obtained from all participants. Participants’ privacy and confidentiality were protected throughout the research process. The study also obtained ethical clearance from a relevant research ethics committee.

The study’s findings were presented in a report summarizing the themes and sub-themes that emerged from the data analysis. The report also provides recommendations for designing and implementing culturally and linguistically appropriate AI-powered interventions for children with autism while avoiding bias and discrimination in the learning materials and activities. The report also includes direct participant quotes to illustrate their experiences and perceptions. The findings are presented based on the order of research questions,

Benefits and challenges of AI-powered interventions

Informants of the study mentioned three benefits and some challenges of AI-empowered intervention for children with autism. Each is explained and exemplified as follows.

Increased engagement and motivation among children with autism

AI-powered interventions can use technologies like robots, virtual reality, and interactive games to provide personalized and engaging experiences for children with autism. Informants believed that AI-powered interventions can effectively increase engagement and motivation among children with autism. For example, educator 1 stated, “Children with autism who interacted with a humanoid robot showed increased engagement and motivation compared to those who received traditional therapy.” Educator 5 said, “By leveraging AI technologies, interventions for children with autism can be tailored to their needs and preferences, providing a more personalized and engaging learning experience. This can lead to improved outcomes and better quality of life for children with autism and their families. This finding is also supported by parent one, who stated, “My son used to struggle with traditional teaching methods, but with AI-powered interventions, he is more engaged and motivated to learn. The technology provides him with immediate feedback, which helps him understand his mistakes and learn from them.”

Customized and individualized interventions that cater to the unique needs of each child

Informants argued that every child with autism is unique, with their own set of strengths and challenges. Therefore, interventions tailored to each child’s specific needs and preferences can be more effective in promoting their development and well-being. This finding echoes the direct quotation by educator 6 who stated, “One size does not fit all when it comes to autism interventions. Each child is unique and requires a personalized approach that takes into account their individual strengths, challenges, and interests.” (Educator 6). Similarly, parent 6 stated, “As a parent, I have learned that the key to helping my child with autism is to focus on his individual needs. By working with his teachers and therapists to develop a personalized intervention plan, we have seen significant progress in his development and well-being.”

Real-time feedback to both children and educators about progress and areas for improvement

Real-time feedback involves providing immediate and ongoing information about a child’s performance and progress in a given activity or intervention. This feedback can reinforce positive behaviors, correct errors, and identify areas where additional support or instruction may be needed. Real-time feedback can be especially beneficial for children with autism, who may benefit from more frequent and targeted feedback to support their learning and development. By providing timely and specific feedback, children with autism can better understand their strengths and areas for improvement, and educators can adjust their interventions and supports accordingly. As an example, one of the educators stated, “Real-time feedback is crucial in helping children with autism learn and grow. By providing immediate and targeted feedback, we can reinforce positive behaviors and help children build new skills.” (Educator 4). Another educator stated, “Real-time feedback is not just important for children but for educators as well. By receiving ongoing feedback about a child’s progress, we can make more informed decisions about the interventions and supports that are most effective for them.“(Educator 8).

The potential for AI-powered interventions to enhance the work of educators and provide them with additional tools and resources

AI-powered interventions have the potential to enhance the work of educators and provide them with additional tools and resources to support the learning and development of children with autism. AI technologies like machine learning algorithms and natural language processing can analyze and interpret data from various sources, including assessment results, behavioral observations, and social communication interactions. This can provide educators with valuable insights and information about each child’s strengths, challenges, and learning needs. Educator 10 stated, “AI-powered interventions can provide educators with powerful tools and resources for supporting autistic children. By analyzing data and providing real-time feedback, these interventions can help educators tailor their teaching strategies and supports to the unique needs of each child.” Educator 3 also stated,” AI-powered interventions have the potential to transform the way we support children with autism in the classroom. By providing educators with insights and information about each child’s learning needs, these interventions can help us deliver more effective and personalized instruction.”

Challenges of AI-powered interventions

The content of interviews with informants was analyzed, and five main themes were extracted. Each is explained and exemplified as follows.

Lack of personalization

Informants stated that while AI-powered interventions have the potential to be personalized, there is a risk that they may not account for the unique needs and preferences of each child. For example, educator 3 stated, “We need to remember that technology is a tool, not a replacement for human interaction.”

Limited access to technology

Not all families and schools can access the necessary technologies for AI-powered interventions. As a parent of a child with autism notes, “Technology can be expensive, and not all families can afford it.”

Difficulty in interpreting and responding to social cues

Children with autism may have trouble analyzing and reacting to social cues, making it challenging to interact with AI technologies. A clinical psychologist notes: “Children with autism may struggle to understand that a robot or virtual character is not a real person, which can limit the effectiveness of AI-powered interventions.”

Ethical concerns

Ethical concerns surrounding using AI technologies with children include privacy, data security, and the potential for misuse or unintended consequences. The Director of Education at one School for Children with Autism notes: “We need to be mindful of the potential risks and unintended consequences of using AI technologies with children with autism.”

Lack of human interaction

While AI-powered interventions can be engaging and interactive, they cannot replace the importance of human interaction in promoting social and emotional development in children with autism. As a parent of a child with autism notes: “Technology can be helpful, but it is important to balance it with real-life experiences and interactions.”

Concerns about the cost and affordability of these interventions

One concern related to using interventions for children with autism is their cost and affordability. Many interventions, such as behavioral and developmental therapies, assistive technologies, and specialized education programs, can be expensive and may not be covered by insurance or other funding sources. This can create barriers for families, particularly those with limited financial resources, in accessing the interventions their child needs to thrive. As Educator 9 stated, “The cost of interventions for children with autism can be a significant burden for families, particularly those with limited financial resources. We must ensure these interventions are accessible and affordable for all families.” Similarly, parent 5 stated, “As a parent of a child with autism, the cost of interventions has been a major concern for our family. Based on our financial limitations, we have had to decide which interventions to prioritize.”

Suggestions for improving the quality of AL-empowered interventions

Interviews with informants were thematically analyzed, and different themes were extracted. Each theme is explained and exemplified as follows.

Using culturally and linguistically appropriate interventions

Participants emphasized the importance of designing and implementing AI-powered interventions that are culturally and linguistically appropriate for a diverse population of children with autism. Some of the suggestions made by participants include:

Ensuring that the language and content of the interventions are culturally sensitive and relevant to the target population.

Incorporating diverse perspectives and experiences into the design and development process.

Providing interventions in multiple languages to accommodate diverse linguistic backgrounds.

Quotations from educators and parents support these suggestions. For instance, educator 1 stated, “Cultural sensitivity is important when designing interventions for children with autism, particularly for those from diverse backgrounds. We need to ensure that the interventions are culturally relevant and take into account the unique needs and experiences of each child.” Similarly, parent 6 stated, “As a parent of a child with autism who comes from a different cultural background, I appreciate interventions that take into account my child’s unique needs and experiences. It’s important to have interventions that are culturally sensitive and relevant.”

Avoiding bias and discrimination

Participants also emphasized the importance of avoiding bias and discrimination in AI-powered interventions’ learning materials and activities. Some of the suggestions made by participants include:

Conducting regular audits of the interventions to identify and address any potential biases or discriminatory content.

Incorporating diverse perspectives and experiences into the design and development process to avoid perpetuating stereotypes.

Providing training and education to educators and developers to ensure that they are aware of and can address potential biases and discrimination.

Quotations from informants support these strategies. As an example, educator 8 stated,

“We need to be careful to avoid stereotypes and biases in the interventions we design and implement. It’s important to be aware of potential biases and to work to address them.” Similarly, parent 7 stated, “To ensure that AI-powered interventions are effective and inclusive, we need to make sure that they are designed with diversity and inclusivity in mind. This means avoiding discrimination and bias in the materials and activities.”

Training educators

Participants discussed the role of educators in implementing AI-powered interventions to support the learning and social communication skills of children with autism. Some of the key findings include:

The importance of providing training and education to educators to ensure that they can effectively implement these interventions.

The need for educators to work collaboratively with parents and other professionals to ensure that the interventions are tailored to the unique needs of each child.

“Educators play a critical role in implementing AI-powered interventions. They need to be trained and educated on how to use these interventions effectively and how to tailor them to the unique needs of each child.” [Educator 3).

We regularly audit the interventions to identify and address potential biases or discriminatory content

Conducting regular audits of interventions for children with autism is an essential step in ensuring that these interventions are effective, evidence-based, and free from biases or discriminatory content. Regular audits help identify areas for improvement, ensure that interventions are aligned with current best practices and ethical guidelines, and promote accountability and transparency in developing and implementing these interventions. Here are two quotations that address the importance of conducting regular audits of interventions for children with autism. To exemplify this finding, the following quotations are presented:

“As educators and researchers, it is our responsibility to ensure that interventions for children with autism are evidence-based, effective, and free from biases or discriminatory content. Regular audits can help us identify and address any areas of concern and promote the highest standards of quality and ethical practice.” (Educator 4). “Regular audits are essential to ensuring that interventions for children with autism are meeting the needs of all children, regardless of their race, ethnicity, gender, or other factors. We must be vigilant in identifying and addressing any biases or discriminatory content that may be present, and work to create interventions that are inclusive and equitable for all children.” (Educator 9).

Involving families and communities in the design and implementation process ensures their voices and perspectives are heard and valued

Involving families and communities in the design and implementation process of interventions for children with autism is crucial to ensuring that their voices and perspectives are heard and valued. Families and communities can provide valuable insights and feedback on the needs and preferences of children with autism and the effectiveness and cultural relevance of interventions. Here are two quotations that address the importance of involving families and communities in the design and implementation process:

“Families and communities are essential partners in the design and implementation of interventions for children with autism. Their insights and feedback can help us create interventions that are effective, culturally relevant, and responsive to the needs of all children.” (Educator 10). “As a parent of a child with autism, I know firsthand the importance of involving families and communities in the design and implementation of interventions. By listening to our voices and perspectives, researchers and educators can create interventions that are more meaningful and effective for our children.” (Parent 8).

Discussion and implications

The present study aimed at exploring the teachers and educators’ experiences and perceptions of artificial intelligence powered interventions for Autism groups. A qualitative research study was employed and interviews were analyzed thematically and different themes were extracted. Participant believed that AI-powered interventions represent a groundbreaking frontier in reshaping the support systems for the learning and social communication skills of children with autism [ 54 ]. Participants also highlighted several noteworthy benefits, with a critical emphasis on the heightened engagement and motivation witnessed among children with autism when exposed to AI-powered interventions [ 1 , 2 , 54 ]. Recognizing the limitations of traditional teaching methods in meeting the distinctive learning needs of these children, AI interventions emerge as a promising avenue [ 1 , 2 ].

The first advantage underscored by participants is the adaptability of AI-powered interventions to provide personalized and individualized support, furnishing real-time feedback to children and educators regarding progress and areas for improvement [ 3 , 4 , 5 ]. This tailored approach aligns seamlessly with the diverse and unique challenges presented by children with autism. However, embracing AI-powered interventions is full of challenges, and participants articulated various concerns [ 55 , 56 ]. Technical glitches and difficulties were identified as potential disruptors of the learning process, prompting apprehensions about an overreliance on technology [ 55 , 56 ]. Moreover, the limited access to technology and resources in specific communities and regions raises concerns about the equitable distribution of intervention benefits [ 55 , 56 ]. Addressing these challenges is imperative to ensure that all children with autism, irrespective of geographical location or socioeconomic status, have equitable access to effective interventions.

The second theme, cultural and linguistic appropriateness, emerged as a primary consideration, with participants highlighting the importance of interventions tailored to the diverse backgrounds of children with autism [ 55 , 56 ]. This includes ensuring that the language and content of interventions are culturally sensitive and relevant, integrating diverse perspectives into the design process, and providing interventions in multiple languages ​​to accommodate linguistic diversity [ 7 , 8 , 9 ]. This finding is consistent with the findings of the previous research which highlighted that language differences can pose significant barriers to accessing autism interventions, highlighting the urgent need for interventions in the child’s native language [ 66 ].

As the third extracted theme “mitigating bias and discrimination in AI-powered interventions” extracted as another critical aspect, necessitating regular audits to identify and rectify potential biases [ 57 ]. The imperative of incorporating diverse perspectives into the design process and providing training to educators and developers to address biases and discrimination was highlighted as crucial [ 10 , 11 ]. This finding confirms the findings of the study that emphasizes the pivotal role of involving families and communities in designing and developing autism interventions to ensure cultural sensitivity and effectiveness [ 67 ].

Despite the above-mentioned potential of AI-powered interventions, the participants concurrently acknowledged the need for further research to evaluate the effectiveness of remote interventions and ensure their cultural and linguistic appropriateness [ 12 , 13 ]. Simultaneously, there are apprehensions and concerns with the potential for these interventions to exacerbate existing disparities in access to care if not implemented equitably. Moreover, challenges have been discerned alongside these benefits, prompting a comprehensive approach to ensure effectiveness, inclusivity, and accessibility [ 55 , 56 ]. Technical glitches, concerns about overreliance on technology, and limited access to resources pose hurdles that need addressing [ 55 , 56 ]. Policymakers must prioritize equitable access, focusing on both technological infrastructure and training programs for educators [ 55 , 56 ].

In addition, ensuring cultural and linguistic appropriateness emerges as a critical consideration in designing and implementing AI-powered interventions [ 55 , 56 ]. Culturally sensitive content, diverse perspectives in development, and multilingual offerings are underscored as essential [ 7 , 8 , 9 ]. Recognizing potential biases and discrimination, participants advocate for regular audits, diversity in development teams, and education on bias mitigation as integral components of ethical AI intervention deployment [ 10 , 11 , 57 ].

AI-powered interventions have emerged as a promising avenue to revolutionize the support for children with autism, offering transformative benefits while presenting challenges that demand careful consideration [ 54 ]. One pivotal advantage emphasized by participants is the heightened engagement and motivation observed among children with autism undergoing AI-powered interventions [ 54 ]. This is particularly noteworthy as traditional teaching methods often need to catch up in meeting the unique learning needs of these children. AI interventions, utilizing technologies such as robots, virtual reality, and interactive games, create personalized and engaging experiences, as reported by educators and parents.

It can also be concluded that transformative potential of AI-powered interventions underscores the need for collaborative efforts among educators, parents, and developers, ensuring effectiveness, inclusivity, and accessibility for all children [ 60 , 61 , 62 , 63 , 64 , 65 ]. The imperative of providing interventions in multiple languages and incorporating diverse perspectives into the design and development process is underscored [ 63 ]. Additionally, including culturally responsive teaching practices alongside AI interventions emerges as a strategy to enhance engagement and outcomes, particularly for children from diverse cultural backgrounds [ 68 ]. Ongoing research, collaborative endeavors, and an unwavering commitment to addressing challenges are imperative to maximize the benefits of AI-powered interventions for children with autism.

It can also be inferred that the collaborative involvement of families and communities is championed to enhance interventions’ impact and cultural sensitivity [ 12 , 13 , 67 ]. Balancing technology with human interaction is deemed crucial, emphasizing the irreplaceable role of personal connections in social and emotional development [ 39 , 41 ]. Moreover, the potential for AI-powered interventions to address access disparities, especially in remote or underserved areas, highlights the importance of further research and evaluation [ 58 , 59 ]. However, concerns persist about exacerbating existing disparities, demanding meticulous attention to cultural, linguistic, and regional nuances.

As another concluding remark, it can be inferred that AI-powered interventions have the potential to revolutionize the way we support the learning and social communication skills of children with autism. These interventions can provide customized and individualized interventions that cater to the unique needs of each child, providing real-time feedback to both children and educators about progress and areas for improvement. AI-powered interventions can also improve access to care for children with autism, particularly for those in remote or underserved areas. The findings suggest that to ensure that AI-powered interventions are culturally and linguistically appropriate for a diverse population of children with autism while also avoiding bias and discrimination in the learning materials and activities, it is essential to incorporate various perspectives and experiences into the design and development process, provide interventions in multiple languages, ensure that the language and content of the interventions are culturally sensitive and relevant, deliver training and education to educators and developers, conduct regular audits of the interventions, involve families and community members in the design and implementation process, and use culturally responsive teaching practices. These efforts can help to address the challenges and considerations of using AI-powered interventions and ensure that all children with autism have access to practical, inclusive, and culturally appropriate interventions.

However, several challenges and considerations need to be taken into account to ensure that these interventions are effective, inclusive, and accessible to all children with autism. These challenges include technical difficulties, overreliance on technology, limited access to technology and resources in specific communities and regions, and the need to design and implement culturally and linguistically appropriate interventions to avoid bias and discrimination.

Finally, one recurring theme is the importance of professional development for educators, which recognizes their critical role in successfully applying AI-powered interventions. Providing educators with technological expertise, cultural sensitivity, and ethical awareness is essential. Furthermore, legislators, educators, and parents must work together to prioritize the financial accessibility of interventions. The ramifications in this complex environment suggest a comprehensive and collaborative strategy. The key to success is overcoming obstacles, adopting technology responsibly, and giving accessibility and inclusivity top priority in intervention and education initiatives. Because technology constantly changes, we must remain committed to ongoing iteration and improvement. Community, parent, and educator feedback loops help us refine AI-powered interventions.

Limitations and suggestions for further studies

The current body of research on AI-powered interventions for children with autism, while promising, grapples with several limitations that warrant careful consideration. Firstly, the generalization of findings remains a challenge, as many studies tend to focus on specific demographic groups or particular manifestations of autism spectrum disorder (ASD). This limits the broader applicability of the insights gained, as the diversity within the autism spectrum may not be comprehensively represented. Additionally, a notable gap exists in understanding the long-term efficacy of AI interventions. While short-term outcomes are frequently explored, there is a scarcity of research delving into the sustained impact of these interventions on the developmental trajectories of children with autism. Longitudinal studies are crucial to elucidating AI-powered approaches’ durability and lasting benefits.

Moreover, the current literature may lack ethnic and cultural diversity, raising concerns about AI interventions’ universal applicability and artistic sensitivity. This underrepresentation hinders our understanding of how these technologies might function across diverse populations. Ethical considerations, although acknowledged, need to be thoroughly examined. Privacy, data security, and potential biases in algorithmic decision-making demand a more in-depth investigation to ensure responsible and equitable use of AI technologies in educational settings.

To address these limitations, future research should prioritize several vital areas. Long-term impact assessments are imperative to ascertain the sustained efficacy of AI interventions over time. Diverse and inclusive studies encompassing a range of ethnicities and cultural backgrounds are essential to validate the universal applicability of these technologies. Robust ethical frameworks should be developed to guide the implementation of AI interventions, addressing privacy, security, and bias concerns. Comparative studies, pitting AI interventions against traditional methods, will offer nuanced insights into their relative advantages and limitations. Family and community involvement in designing and implementing AI interventions should be explored further, recognizing the unique insights these stakeholders bring. Finally, comprehensive cost-benefit analyses are necessary to evaluate the economic aspects of AI interventions, ensuring their affordability and long-term viability in diverse educational settings. In navigating these avenues, researchers can contribute substantively to the responsible and inclusive integration of AI-powered interventions for children with autism.

Data availability

The data will be made available upon request from the corresponding author (Corresponding author: email: [email protected].

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This work was supported by The General Project of Beijing Postdoctoral Research Foundation in 2023, “Research on the Representation of the Tacit Knowledge of High School History Teachers Based on Natural language processing”. (Project No.2023-zz-182)

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Li, G., Zarei, M.A., Alibakhshi, G. et al. Teachers and educators’ experiences and perceptions of artificial-powered interventions for autism groups. BMC Psychol 12 , 199 (2024). https://doi.org/10.1186/s40359-024-01664-2

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Memory: An Extended Definition

Gregorio zlotnik.

1 Clinique de la Migraine de Montreal, Montreal, QC, Canada

Aaron Vansintjan

2 Department of Film, Media and Cultural Studies, Birkbeck, University of London, London, United Kingdom

Recent developments in science and technology point to the need to unify, and extend, the definition of memory. On the one hand, molecular neurobiology has shown that memory is largely a neuro-chemical process, which includes conditioning and any form of stored experience. On the other hand, information technology has led many to claim that cognition is also extended, that is, memory may be stored outside of the brain. In this paper, we review these advances and describe an extended definition of memory. This definition is largely accepted in neuroscience but not explicitly stated. In the extended definition, memory is the capacity to store and retrieve information. Does this new definition of memory mean that everything is now a form of memory? We stress that memory still requires incorporation, that is, in corpore . It is a relationship – where one biological or chemical process is incorporated into another, and changes both in a permanent way. Looking at natural and biological processes of incorporation can help us think of how incorporation of internal and external memory occurs in cognition. We further argue that, if we accept that there is such a thing as the storage of information outside the brain – and that this organic, dynamic process can also be called “memory” – then we open the door to a very different world. The mind is not static. The brain, and the memory it uses, is a work in progress; we are not now who we were then.

Introduction

In the short story “Funes, the memorious,” Jorge Luis Borges invites us to imagine a man, Funes, who cannot forget anything. The narrator is ashamed in the inexactness of his retelling: his own memory is “remote and weak,” in comparison to that of his subject, which resembles “a stammering greatness.” Unlike Funes, he says, “we all live by leaving behind” – life is impossible without forgetting. He goes on to note that, even though Funes could remember every split second, he couldn’t classify or abstract from his memories. “To think is to forget a difference, to generalize, to abstract.” The reader may be led to wonder how Funes’ brain has the capacity to store all of that memory. doesn’t it reach its limits at some point? Borges leaves that question to our imagination.

In popular culture, memory is often thought of as some kind of physical thing that is stored in the brain; a subjective, personal experience that we can recall at will. This way of thinking about memory has led many to wonder if there is a maximum amount of memories we can have. But, this idea of memory is at odds with advances in the science of memory over the last century: memory isn’t really a fixed thing stored in the brain, but is more of a chemical process between neurons, which is not static. What’s more, advances in information technology are pushing our understanding of memory into new directions. We now talk about memory on a hard drive, or as a chemical change between neurons. Yet, these different definitions of memory continue to co-exist. A more narrow definition of memory, as the storage of experiences in the brain, is increasingly at odds with an extended definition, which acknowledges these advances. However, while this expanded definition is often implicitly used, it is rarely explicitly acknowledged or stated. Today, the question is no longer, how many memories can we possibly have, but, how is the vast amount of memory we process on a daily basis integrated into cognition?

In this paper, we outline these advances and the currently accepted definitions of memory, arguing that these necessarily imply that we should today adopt an extended definition. In the following, we first describe some key advances in the science of memory, cognitive theory, and information technology. These suggest to us that we are already using a unified, and extended, definition of memory, but rarely made explicit. Does this new definition of memory mean that everything is now a form of memory? We argue that looking at natural and biological processes of incorporation can help us think of how incorporation of internal and external memory occurs in cognition. Finally, we note some of the implications of this extended definition of memory.

Background: Advances in the Science of Memory

Already in the 19th century, the recognition that the number of neurons in the brain doesn’t increase significantly after reaching adulthood suggested to early neuroanatomists that memories aren’t primarily stored through the creation of neurons, but rather through the strengthening of connections between neurons ( Ramón y Cajal, 1894 ). In 1966, the breakthrough discovery of long-term potentiation (LTP) suggested that memories may be encoded in the strength of synaptic signals between neurons ( Bliss and Lømo, 1973 ). And so we started understanding memory as a neuro-chemical process. The studies by Eric Kandel of the Aplysia californica , for which he won the Nobel prize, for example, show that classical conditioning is a basic form of memory storage and is observable on a molecular level within simple organisms ( Kandel et al., 2012 ). This in effect expanded the definition of memory to include storage of information in the neural networks of simple lifeforms. Increasingly, researchers are exploring the chemistry behind memory development and recall, suggesting these molecular processes can lead to psychological adaptations (e.g., Coderre et al., 2003 ; Laferrière et al., 2011 ).

Memory is today defined in psychology as the faculty of encoding, storing, and retrieving information ( Squire, 2009 ). Psychologists have found that memory includes three important categories: sensory, short-term, and long-term. Each of these kinds of memory have different attributes, for example, sensory memory is not consciously controlled, short-term memory can only hold limited information, and long-term memory can store an indefinite amount of information.

Key to the emerging science of memory is the question of how memory is consolidated and processed. Long-term storage of memories happens on a synaptic level in most organisms ( Bramham and Messaoudi, 2005 ), but, in complex organisms like ourselves, there is also a second form of memory consolidation: systems consolidation moves, processes, and more permanently stores memories ( Frankland and Bontempi, 2005 ). Today, there are many models of how memory is consolidated in cognition. Single-system models posit that the hippocampus supports the neocortex in encoding and storing long-term memories through strengthening connections, finally leading the memory to become independent from the hippocampus (Ibid.). Multiple-trace theory instead proposes that each memory has a unique code or memory trace, which continues to involve the hippocampus to an extent ( Hintzman and Block, 1971 ; Hintzman, 1986 , 1990 ; Whittlesea, 1987 ; Versace et al., 2014 ; Briglia et al., 2018 ). In another theory, memory is understood as a form of negative entropy or rich energy ( Wiener, 1961 , 1988 ), which is then processed in a way that minimizes the expenditure of energy by the brain ( Friston, 2010 ; Van der Helm, 2016 ). Our heightened capacity to store information may be due to our ability to reduce disorder and process large amounts of information rapidly, a necessarily non-linear process ( Wiener, 1961 , 1988 ). The forgetting and fading of memories is also understood as being an important aspect of the functioning and utility of these memories ( Staniloiu and Markowitsch, 2012 ). As with a computer hard drive, memories can also be “corrupted” – false memories are commonly studied within forensic psychology ( Loftus, 2005 ). Together, these advances highlight how different kinds of memory storage are non-linear – that is, subject to complex systems interactions – contextual, and plastic. They also shed light on why, and how, we are able to live with such large quantities of information. It may not be that Funes has the special ability to remember everything, but that he lacks our ability to incorporate, and sort through, a potentially infinite amount of information.

The advance of the fields of genetics and epigenetics has also given us new metaphors to describe memory. We understand DNA as a structure that carries information that we call “genetic code” – kind of like a computer chip for biological processes. Today, the metaphor has come full circle and we can now use DNA to store and extract digital data ( Church et al., 2012 ). The study of epigenetics suggests that simple lifeforms pass on memories across generations through genetic code ( Klosin et al., 2017 ; Posner et al., 2019 ), suggesting a need to study whether humans and other complex life forms may do so as well. With these advances, our understanding of how memory is stored has expanded once again.

Further, we can now store memory in places that we haven’t been able to before. Smartphones, mind-controlled prosthetic limbs, and Google Glasses all offer new ways to store information and thereby interact with our surroundings. Our ability to produce information alters how we perceive the world, with far-reaching implications. As Stephen Hawking, the Nobel prize-winning physicist explained in his 1996 lecture, “Life in the universe,”

What distinguishes us from [our ancestors], is the knowledge that we have accumulated over the last 10000 years, and particularly, over the last three hundred. I think it is legitimate to take a broader view, and include externally transmitted information, as well as DNA, in the evolution of the human race ( Hawking, 1996 ).

The sheer quantity of available information today, as well as developments in an understanding of memory – from fixed and physical to dynamic, chemical, and a process of rich energy transfer – lead to a very different picture of memory than the one we had 100 years ago. Memory seems to exist everywhere, from an Aplysia ’s ganglion to DNA to a hard drive.

To account for these developments, cognitive scientists now propose that human cognition is actually extended beyond the brain in ways that theories of the mind did not previously recognize ( Clark and Chalmers, 1998 ; Clark, 2008 ). This approach is being called 4E cognition (Embodied, Embedded, Extended, and Enactive). For example, enactivism posits that cognition is a dynamic interaction between an organism and its environment ( Varela et al., 1991 ; Chemero, 2009 ; Menary, 2010 ; Rowlands, 2010 ; Favela and Chemero, 2016 ; Briglia et al., 2018 ). According to this framework, cognition is a process of incorporation between the environment and the body/brain/mind. To be clear, cognition is not incorporated in the surroundings, only the corpus can incorporate, and thus cognition (or what we call “mind”) is a product of the interaction between the brain, the body, and the environment.

Extending Memory

These developments indicate that we need to reconceptualize our definition of memory. What is the difference between trying to recall a childhood experience, and searching for an important email archived years ago? This distinction is best represented through the difference in how we use the words “memory” and “memories.” Usually, “memories” tends to refer to events recalled from the past, which are seen as more representational and subjective. In contrast, “memory” now is used to refer to storage of information in general , including in DNA, digital information storage, and neuro-chemical processes. Today, science has moved far beyond a popular understanding of memory as fixed, subjective, and personal. In the extended definition, it is simply the capacity to store and retrieve information . To illustrate why memory has extended beyond this original use, we want to ask the reader: what do a stressed-out driver and a snail have in common?

(1). A homeowner has been trying to sell her house for a year, and worrying about it. One day, she’s driving to work and becomes extremely anxious, for no apparent reason. She wasn’t thinking of anything in particular at the time. Confused, she looks around, and notices a billboard advertising a real estate agency. She realizes that she had seen it out of the corner of her eye, and her brain had then processed the information while she was thinking of something else, which then triggered the anxiety attack.

(2). Consider a nerve cell of an A. californica , a kind of sea snail, which is prodded vigorously for a short time period, provoking an immediate withdrawal response. Shortly afterward, it is prodded less intensely, but, it elicits the same withdrawal response. It is found that the slugs’ nerve cell is sensitive for up to 24 h – the nerve cells “remember” past pain.

Each example illustrates a different kind of chemical, biological process. In the first example, an outside stimulus triggers a stress response for the homeowner. We can surmise that though she didn’t “remember” anything, non-consciously, she did. In the second example, the snail certainly “remembers” the provocation, even though this memory is only stored in a few cells. But can we really call this memory?

However, on closer examination, we are forced to concede that each of them should be called a form of memory. First, consider the homeowner: her brain “remembers” something that does not occur to her as a conscious thought. It is clearly a chemical process occurring in the background. Most would grant that this would nevertheless be a form of memory, as it involves recalling information stored in her brain. Already, a broader definition of memory is used that does not imply conscious attention. Now, consider the snail: it is also storing information chemically. Once again, this does not involve a conscious, subjective process of storing and remembering – it is purely reactive, but information is being stored and recalled nonetheless. We would need to concede that if the homeowner’s experience counts as memory, then the slug’s automatic response does as well. There is in fact little difference between the first two examples: there is a transfer of information that causes a reaction. Both should be considered forms of memory.

A Slippery Slope?

If we agree with this expanded definition of memory, then it follows that experience is also a form of stored information, kinds of memory . We are not saying that a particular experience, as an event , is a memory. Rather, we here use the word “experience” as connoted by the phrase “an experienced driver,” an “experienced writer.” They have a set of experiences, remembered through practice, and retrieved when they drive, or write. When we accumulate knowledge, information, and techniques, then the accumulation of those separate processes constitute experience . This experience involves retrieval of information, conversely, being experienced is the process of retrieving memory.

Under this definition, even immunological and allergy processes may be considered memory. There is a storage of information of the allergen or the viral/bacterial aggressor and when the aggressor or allergen re-appears there is a cascade of inflammatory processes. This can be considered the storage and retrieval of information, and thus a form of memory. This does not contradict the accepted definition of memory within psychology, as it is still seen as the ability to encode, store, and recall information. Rather, it extends it to processes not just bound by the brain.

If memory is indeed defined as “the capacity to store and/or retrieve information,” then this may lead anyone to ask – what isn’t memory? Wouldn’t this definition of memory be far too broad, and include a vast range of phenomena? Is the extended definition of memory, as is being proposed by neurobiologists and cognitive theorists, a slippery slope?

As we suggested above, however, memory still involves a process of incorporation, that is, requiring a corpus . While memory may be stored on the cloud, it requires a system of incorporation with the body and therefore the mind. In other words, the “cloud” by itself is not memory, but operates through an infrastructure (laptops, smart phones, Google Glasses) that are integrated with the brain-mind through learned processes of storage and recall. The conditioning of an Aplysia ’s ganglion is incorporated into an organism. Memory, it seems, is not just mechanistic, but a dynamic process. It is a relationship – where one biological or chemical process is incorporated into another, and changes both in a permanent way. A broadened definition must account for this dynamic relationship between organisms and their environment.

How can we understand this process of incorporation? It appears that symbiotic incorporation of biological processes is quite common in nature. Recent studies offer more evidence that early cells acquired mitochondria by, at some point, incorporating external organisms into their own cell structure ( Thrash et al., 2011 ; Ferla et al., 2013 ). Mitochondria have their own genome, which is similar to that of bacteria. What was once a competitor and possibly a parasite became absorbed into the organism – and yet, the mitochondrion was not fully incorporated and retains many of its own processes of self-organization and memory storage, separate from the cell it resides in. This evolutionary process highlights the way by which external properties may become incorporated into the internal, changing both. Looking at natural and biological processes of incorporation can help us think of how incorporation of internal and external memory occurs in cognition.

Implications

This extended definition of memory may seem ludicrous and hard to accept. You may be tempted to throw up your hands and go back to the old, restricted, definition of memory – one that requires the transmission of subjective memories.

We beg you not to. There are several benefits of this approach to memory. First, in biology, expanding the definition of memory helps us shift from a focus on “experience” (which suggests an immaterial event) to a more material phenomenon: a deposit of events that may be stored and used afterward. By expanding the concept of memory, the study of memory within molecular neurobiology becomes more relevant and important. This expanded definition is in large part already widely accepted, for example, in Kandel’s Aplysia , conditioning is acknowledged to be a part of memory, and memory is not a part of conditioning. Memory would become the umbrella for learning, conditioning, and other processes of the mind/brain. Doing so changes the frame of observation from one which understands memory as a narrow, particular process, to one which understands it as a dynamic, fluid, and interactive phenomenon, neither just chemical or digital but integrated into our experience through multiple media. Second, it helps to conceptualize the relationship between biology, psychology, cognitive science, and computer science – as all three involve studying the transfer of information.

Third, it opens up an interesting way to imagine our own future. If we accept that there is such a thing as the storage of information outside the brain – and that this organic, dynamic process can also be called “memory” – then we open the door to a very different world. The mind is not static. Rather, like early cells acquiring mitochondria, it incorporates information from its surroundings, which in turn changes it. The brain, and the memory it uses, is a work in progress; we are not now who we were then. Many have already noted the extent to which we are cyborgs ( Harraway, 1991 ; Clark, 2003 , 2005 ); this neat line between human and technology may become more and more blurred as we develop specialized tools to store all kinds of information in our built environment. In what ways will the mind-brain function differently as it becomes increasingly more incorporated in its milieu, relying on it for information storage and processing?

Now let’s talk about Funes. His inability to forget his memories may seem familiar to some, a metaphor for our current condition. We may now recognize a bit of ourselves in him: we don’t see limits in our capacity to store new information, and the sheer availability of it is sometimes overwhelming. Even without the arrival of the Information Age, we carry with us through life a heavy load of disappointments, broken dreams, little tragedies and many memories. We know that forgetting is a must and a challenge. Yet, we are learning rapidly how to incorporate and use the massive amounts of data now available to us. The main challenge for each of us is to harness and control the unleashed powers given to us by technology. The future is uncertain, but some things remain the same. As Kandel (2007 , p. 10) wrote, “We are who we are in great measure because of what we learn, and what we remember.”

Author Contributions

GZ and AV drafted and edited the manuscript. Both authors contributed to manuscript revision, read, and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors wish to thank Michael Lifshitz, Ph.D. for reading an early copy of this article and providing feedback. The authors also wish to thank Steven J. Lynn, Alan M. Rapoport, and Morgan Craig for the feedback and encouragement.

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