Category: papers Page 1 of 7

New paper: Is educational research available to the broader public?

On October 8, 2025

In my research, oer, papers, scholarship

We have published a new paper examining the extent to which educational research is available to the broader public, and I am shamelessly copying and pasting Josh Rosenberg’s announcement of it below:

This paper came from a curiosity (maybe even a frustration) — what is returned when you search for an educational research article on Google Scholar? And, relatedly, how widely accessible is research in our field? And, relatedly, how accessible is research in our own field?

We looked at over 2,500 articles published between 2010 and 2022 across six AERA journals. Using what we described as a “Public Internet data mining” approach we asked a simple set of questions: Is the article available? In what form? And where? The work was just published in Teachers College Record. This is the first study of its kind to empirically document the accessibility of educational research and our hope is that it could inform efforts to make our work more accessible to teachers, leaders, and the public.

Here’s what we found:

About 65% of articles were accessible in some form—a much higher rate than the roughly 28% reported for scholarly articles in general in other, prior work.

Most of those accessible versions were the published PDFs, often posted on sites like ResearchGate.

Only about 6% were openly licensed, meaning they can be freely reused.

The rest were a mix of preprints, temporary “free” versions, or other file types.

On the one hand, this is encouraging: many more articles are available than we might expect. On the other hand, the picture is messy. Access depends on whether an author uploaded a copy to a site, whether you know where to look, and whether reuse is even allowed.

Perhaps the bigger question is what kind of field we want educational research to be. If our work is meant to inform teaching, policy, and public understanding, shouldn’t the default be that anyone—teachers, school board members, families—can actually read it?

Shout out to my fantastic colleagues George Veletsianos, Enilda Romero-Hall, and Emilie Allen for the collaboration on this. You can access the article on TCR’s homepage here.

And (of course!) there is an open-access version — that’s on OSF here.

I loved working with Josh, Enilda, and Emilie on this. Writing some of the code for the data mining work that went into this paper gave me an idea for the use of AI in education research, which is a topic I’ve been working on for about a year now. More on this soon.

Simple Checklists to Verify the Accuracy of AI-Generated Research Summaries

By George Veletsianos

On August 18, 2025

In AI, emerging technologies, GenAI, generative artificial intelligence, my research, networked scholars, NPS, papers, research shorts

Do you share AI-generated audio/video summaries of your research with students? or with the broader public on social media? Below is a short article I wrote encouraging researchers to share a checklists alongside those summaries verifying their accuracy and noting their limits (the final version is at Veletsianos, G. (2025). Simple Checklists to Verify the Accuracy of AI-Generated Research Summaries. Tech Trends, XX(X), Xx-xx but here’s a public pre-print too).

Simple Checklists to Verify the Accuracy of AI-Generated Research Summaries

Picture this: An educational technology researcher shares a seven-minute AI-generated audio or video of their latest paper on social media. It sounds engaging and professional. But buried in that smooth narration, the AI has quietly transformed “may suggest” into “proves,” dropped crucial limitations, and expanded the study’s claims beyond what the data supports. The listeners, including students, policymakers, and journalists, have no way of knowing.

The proliferation of AI-generated audio and video summaries of research papers—through tools like Google’s NotebookLM and others—represents both an opportunity and a challenge for scholarly communication. These summaries are promising as they can expand the reach, accessibility, and consumption of our research for diverse audiences (cf. Veletsianos, 2016). They also allow us to efficiently engage with literature outside of our expertise. A seven-minute podcast consumed during a commute may reach audiences who would never read a 30-page paper.

Yet this convenience comes with risks. Peters and Chin-Yee (2025) for example, found that summaries generated by Large Language Models omitted study details and made overgeneralizations. Such risks can propagate misunderstandings, particularly when summaries circulate without clear indicators of their accuracy or limitations.

While some technical solutions to address this problem exist (e.g., including fine-tuning models and implementing algorithmic constraints) these approaches remain inaccessible to most researchers. We need a low-barrier intervention that empowers authors to assess and communicate the quality of AI-generated summaries to listeners.

I propose that researchers who share AI-generated summaries complete and publish a brief verification checklist alongside their summary. This practice serves two purposes: it encourages authors to critically review AI output before dissemination, and it provides audiences with transparency about the summary’s accuracy and limitations. Just as we expect ethics approval for research, we should normalize quality assurance for AI-generated scholarly content.

To facilitate this practice, below are two verification checklists, one for academic audiences and another for the general public, even though the latter could serve both audiences. Both are deliberately concise to enable sharing across digital platforms where these summaries circulate, from social media to publishers’ websites to course management systems.

Checklist 1: For Academic Audiences Checklist 2: For the General Public

Author verification: This summary of [paper title] was AI-generated using [tool name] on [date] and reviewed by the author(s). It accurately represents our work. For full details, nuance, and context, please refer to the original work at [URL].

The following items were verified:
✓ Research purpose or questions stated correctly
✓ Study design described correctly
✓ Summary matches study results (no fabricated data)
✓ Conclusions are explicitly limited to the study’s scope and context
✓ Key terminology used properly
✓ Theoretical, conceptual, and/or methodological frameworks are framed appropriately and are neither omitted, nor misrepresented
✓ Major limitations are included
✓ Context and scope are clear
✓ There summary does not omit anything of significance
✓ The tone is consistent with the original work

Issues noted: [Note any issues]
Author verification: This summary of [paper title] was AI-generated using [tool name] on [date] and reviewed by the author(s). It accurately represents our work. For full details, nuance, and context, please refer to the original work at [URL].

What we checked:
✓ Main findings are correct – nothing made up
✓ Doesn’t overstate what we found
✓ Includes what we studied and who participated
✓ Mentions important limitations
✓ Uses language appropriately
✓ Matches our original tone and message

Issues noted: [Note any issues, using plain language]

These checklists are a starting point, not a comprehensive solution. I have attempted to make them flexible enough to accommodate different research paradigms, but if you do use them, you should refine them to fit your needs and orientation. The point is not to develop the perfect checklist, but to provide a flexible tool that can be adapted and improved to minimize the risks of AI-generated research summaries. As AI tools become increasingly integrated into research dissemination, we must develop community standards for responsible use. Normalizing transparency practices now contributes toward maintaining the integrity that underpins scholarly communication.

In an academic landscape saturated with contested claims, particularly in education and educational technology where myths and zombie theories persist (e.g., Sinatra, & Jacobson, 2019; Suárez-Guerrero, Rivera-Vargas, & Raffaghelli, 2023), our commitment to accuracy and transparency must remain constant. Verifying AI-generated summaries constitutes a form of reputational stewardship. This quality assurance practice encourages authors to critically review AI output before it circulates, signaling to colleagues, institutions, and the public that they take seriously their role as knowledge custodians. By proactively verifying summaries, researchers can protect the integrity of their findings and build a reputation for reliability that enhances the trustworthiness of their entire body of work. At the end of the day, the few minutes invested in verifying AI-generated summaries of one’s work pale in comparison to the time that might be required to correct a misleading summary that gains traction on social media. Once an AI-generated misrepresentation goes viral, no amount of clarification can fully revise it. In this sense, verification checklists function as both quality control and professional insurance. They are a small investment that yield returns in credibility and peace of mind.

I encourage researchers to adopt versions of these checklists, journals to consider requiring them for AI-generated supplementary materials, and the broader academic community to refine and expand upon this framework. In an era of rapid AI developments, our commitment to scholarly accuracy and transparency must remain constant.

Author notes and transparency statement, as suggested by Bozkurt (2024): This editorial was reviewed, edited, and refined with the assistance of ChatGPT o3 and Gemini Pro 2.5 as of July 2025, complementing the human editorial process to address grammar, flow, and style. I critically assessed and validated the content and assessed potential biases inherent in AI-generated content. The final version of the paper is my sole responsibility.

References

Bozkurt, A. (2024). GenAI et al. Cocreation, authorship, ownership, academic ethics and integrity in a time of generative AI. Open Praxis, 16(1), 1-10.

Peters, U., & Chin-Yee, B. (2025). Generalization bias in large language model summarization of scientific research. Royal Society Open Science, 12(4), 241776. https://doi.org/10.1098/rsos.241776

Sinatra, G. M., & Jacobson, N. (2019). Zombie Concepts in Education: Why They Won’t Die and Why You Cannot Kill Them. In P. Kendeou, D. H. Robinson, & M. T. McCrudden (Eds.), Misinformation and fake news in education (S. 7–27). Information Age Publishing, Inc.

Suárez-Guerrero, C., Rivera-Vargas, P., & Raffaghelli, J. (2023). EdTech myths: towards a critical digital educational agenda. Technology, Pedagogy and Education, 32(5), 605-620.

Veletsianos, G. (2016). Networked Scholars: Social Media in Academia. New York, NY: Routledge.

New publication: How do Canadian Faculty Members Imagine Future Teaching and Learning Modalities?

By George Veletsianos

On May 8, 2024

In future education, futures, papers, scholarship, speculative fiction, speculative futures

What do future learning environments look like? Is online learning “the new normal?” Or, are we back to the “old normal?” What does the “new normal” look like? Never mind concepts of “normal,”… what do learners and faculty imagine future learning environments, technologies, and modalities looking like? Colleagues and I completed and are planning a series of studies around these ideas, bringing together threads in our research that examines online learning, emerging technologies, challenges facing higher education, and speculative methods. We recently published one of these and I am sharing the pre-print below.

When I prompted ChatGPT to generate an image depicting this paper it generated the image below. This image provides an interesting juxtaposition to our findings, because our findings highlight the relative persistence of the status quo and reveal a lack of more radical futures.

Here’s the paper: Veletsianos, G., Johnson, N., & Houlden, S. (2024). How do Canadian Faculty Members Imagine Future Teaching and Learning Modalities? Educational Technology Research & Development, 72(3), 1851 – 1868.. The final version is available at https://doi.org/10.1007/s11423-024-10350-4 but here is a public pre-print version.

Abstract

This study, originally prompted by the impact of the COVID-19 pandemic on educational practices, examined Canadian faculty members’ expectations of teaching and learning modalities in the year 2026. Employing a speculative methodology and thematic analysis, interview responses of 34 faculty members led to the construction of three hypothetical scenarios for future teaching and learning modalities: a hybrid work model, a high tech and flexible learning model, and a pre-pandemic status quo model. In contrast to radical education futures described in the literature, the findings do not depart significantly from dominant modes of teaching and learning. Nevertheless, these findings offer insights into the expectations that Canadian faculty members have with respect to future teaching and learning modalities, the contextual issues and concerns that they face, the use of speculative methodologies in educational technology research, and the potential impacts remote learning trends have on the future of education in Canada.

Open Access fees are exorbitant

By George Veletsianos

On March 8, 2024

In open, papers, scholarship, sharing

After signed another publishing agreement, and I was, once again, taken aback by the exorbitant OA fees that publishers charge.

Publishing open access with us (gold OA) lets you share and re-use your article immediately after publication.

The article processing charge (APC) to publish an article open access in Educational technology research and development is:

Article processing charge (excluding local taxes)

£2,290.00 / $3,290.00 / €2,590.00

Some organisations will pay some or all of your APC.

If you want to publish subscription, instead of open access, there will be an option to do that in the following steps.

I know, I know, we probably shouldn’t have submitted to journal that isn’t gold and free OA by default, *but* the system is structured in such ways that my junior co-authors would benefit from being published in this journal.

While not a solution to this problem, it’s worth noting the terms in the publishing agreement around sharing the article. This is in the terms:

The Assignee grants to the Author (i) the right to make the Accepted Manuscript available on their own personal, self-maintained website immediately on acceptance.

This is the approach that I use for nearly all my papers, but it’s worth remembering that what this really does is suggest an individual solution to a systemic problem, which will do little to solve the broader problem of lack of access to research.

There are other statements in the terms around placing one’s article in an institutional repository, but author self-archiving is generally the first and immediate option available to individuals. And perhaps google scholar will index the author’s personal website, making the article available, as shown below. Google scholar’s approach of identifying articles and placing publicly-available versions in search results is a systemic solution to the problem. Unpaywall is similar in that respect.

[To be clear: this post isn’t about ETR&D. It’s about the publishers & the publishing system]

Google scholar alerts on citations

By George Veletsianos

On November 14, 2023

In Ideas, papers, scholarship

Email is a productivity killer. But, one kind of email I like to receive is from Google Scholar, alerting me of newly published research that is in conversation with my research, aka papers that cite my work.

a screenshot of emails from google scholar showing new citations to the author's research

I love this feature because it quickly allows me to

get a sense of how others are reacting to my work
track some of the literature surrounding my research interests (emphasis on some)
discover new authors
keep my never-ending “to read” list full

I’d love it even more if Google Scholar also

delivered all available papers in that same email (e.g., if there was a way that it would connect to my institutional library and retrieve them or just give me the open access ones)
kept an up-to-date spreadsheet of all citations that I could use for different purposes

It ought not need clarification, but to be clear: 1) citations don’t necessarily mean that one’s work is impactful or significant. What if they are all critical of the work?. 2) lack of citations doesn’t necessarily mean that a paper isn’t worthwhile or significant, as areas unrelated to the quality of the work often influence citations (e.g., timing)

Traxler’s review of our book: Critical Digital Pedagogy in Higher Education

By George Veletsianos

On July 13, 2023

In papers, scholarship, work

Johh Traxler wrote a very kind review of Critical Digital Pedagogy in Higher Education, the open access book that Suzan, Chris, and I co-edited. In it, he begins by noting that he is concerned of a growing chasm in digital education, as

there seem to be two parallel universes of learning, of two different sets of ideas about how we learn, what we learn, who we learned it from, and we show we have learnt it: one inside higher education, the other in the world outside. On one side are the closed systems around the dedicated EdTech systems in higher education, and around the different cadres and professions that develop, sell, procure, install, and deploy them to deliver the formal curriculum. On the other side are the ever fluid and informal groups and relationships that exploit social media and Web 2.0 to produce ideas, images, information, identities, and opinions, and to share, store, transform, merge, and discard them.

After reviewing individual chapters, he concludes that each chapter populates the spaces in the chasm and “makes an extraordinary contribution, tackling the chasm from a surprising variety of angles and should be valued and explored accordingly.”

I’m filing this into the “positive words” folder, which is a folder that I refer to when I need reminders that gloomy days are temporary.

Are cohort-based course platforms “universities of the future?”

By George Veletsianos

On June 28, 2023

In courses, future education, my research, online course marketplace, online learning, papers

The edtech industry includes numerous learning providers and platforms providing tools, technologies, and resources for course creators to create and sell online courses. These platforms are interesting for very many reasons. What roles do they play in the learning and development ecosystem? How do they measure effectiveness and learning outcomes? What kinds of pedagogical and instructional design practices do they support and advocate for? What education-related claims do they make?

two people working on five laptops. They sit at a table littered with other devices, like phones, headset, and ipads. Photo by Marvin Meyer on Unsplash

In a paper we published a few months ago, we examined one such platform because it describes itself as building ‘the university of the future’ and has recently received significant attention and funding. This makes it a compelling case study to better understand the potential roles and risks associated with education platforms operating outside of and alongside more traditional higher education institutions.

We highlight specific concerns about cohort-based platforms. These include lack of transparency, risk of surveillance, lack of adequate financial support for learners, and over-reliance on social media networks as signifiers of educator/instructor qualification (this last one is a big one). Suggested benefits include adaptability, suitability to changing skills needs, and responsiveness to changing environmental scenarios.

The published version of the paper is here, but here’s a pre-print pdf: Veletsianos, G., & Houlden, S. (in press). On the “university of the future”: A critical analysis of cohort-based course platform Maven. Learning, Media, & Technology.