Category: my research Page 1 of 21

Professional development institute applications open: Expanding the Methods of Education Research with Generative AI (EMERGai)

On March 2, 2026

In AI, emerging technologies, GenAI, generative artificial intelligence, my research, scholarship

I’m the PI on a newly-funded NSF-funded professional development Institute and we are now open for applications!

Our institute focuses on Expanding the Methods of Education Research with Generative AI (EMERGai). In short: we provide participants with stipends, training to use GenAI in a research project of their choice, and support them to complete that research project. We do this through an in-person workshop (Raleigh, North Carolina, on July 20-24, 2026) followed by an online community of practice during the academic year.

A woman and a man sitting in front of a computer screen, pointing at something on the screen and talking, with a colourful stencil design on the wall behind them.

Data annotators discussing the correct labeling of a dataset by Nacho Kamenov & Humans in the Loop / Licenced by CC-BY 4.0

Overall, EMERGai is intended to address a critical need in STEM education research by empowering early- and mid-career researchers employed at resource-limited colleges and universities to effectively and ethically leverage Generative AI tools and processes in their research workflow, including literature reviews, data collection, data analysis, interpretation, and writing. By participating in this program, researchers will be able to engage in innovative, rigorous, responsible, and ethical GenAI-supported STEM education research practices across the research workflow and cultivate the mindsets and skills necessary for pursuing long-term learning, enhancing their career trajectory, and contributing to scientific progress beyond the institute.

Institute participation is limited to those who are

Early- or mid-career STEM education researchers (e.g., postdoctoral researchers through associate professors, or equivalent roles such as research associates/scientists or research directors)
Employed at a US resource-limited institution.

We define early-and mid-career researchers as those within 14 years of receiving their highest terminal degree.

We define resource-limited colleges and universities as those operating with constrained budgets, smaller/limited endowments, and little infrastructure or support services for advanced research. This may include institutions located in rural or remote regions and smaller public or private colleges or universities with limited capacity for large-scale research initiatives.

If you’re not eligible, please also consider forwarding this invitation to colleagues who may be eligible.

Applications will close on April 1, 2026.

For more information, including institute structure, stipends, FAQs, expectations, and to apply, please visit our website.

Contact me directly with questions!

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

By George Veletsianos

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.

Kind and accurate summary of “Learning Online: The student experience”

By George Veletsianos

On September 9, 2024

In my research, online learning, sharing

I just came across the following summary of my book Learning Online: The student experience from librarians at the University of Northern Iowa, within a list of books focused on Universities and Education in the Digital Age. I am posting it here for posterity and to be able to track it later:

Online learning is ubiquitous for millions of students worldwide, yet our understanding of student experiences in online learning settings is limited. The geographic distance that separates faculty from students in an online environment is its signature feature, but it is also one that risks widening the gulf between teachers and learners. In Learning Online, George Veletsianos argues that in order to critique, understand, and improve online learning, we must examine it through the lens of student experience. Approaching the topic with stories that elicit empathy, compassion, and care, Veletsianos relays the diverse day-to-day experiences of online learners. Each in-depth chapter follows a single learner’s experience while focusing on an important or noteworthy aspect of online learning, tackling everything from demographics, attrition, motivation, and loneliness to cheating, openness, flexibility, social media, and digital divides. Veletsianos also draws on these case studies to offer recommendations for the future and lessons learned. The elusive nature of online learners’ experiences, the book reveals, is a problem because it prevents us from doing better: from designing more effective online courses, from making evidence-informed decisions about online education, and from coming to our work with the full sense of empathy that our students deserve. Writing in an evocative, accessible, and concise manner, Veletsianos concretely demonstrates why it is so important to pay closer attention to the stories of students–who may have instructive and insightful ideas about the future of education.

2033 – Future education scenario 3 of 3

By George Veletsianos

On August 28, 2024

In future education, futures, my research, speculative fiction, speculative futures

In a recent paper* we describe three education scenarios and ask youth to respond to them. Positioned in 2033, these futures represent three distinct possibilities for what education could look like in a decade. I’m curious what others think about them, and I’ll post one per day here, as my “back to school return to reviving this blog.” What are your reactions, thoughts, and feelings to this one? I’d love to know!

Future 3: The year is 2033. Public universities and colleges around the world struggle to stay open due to sharp declines in enrolments and continuing social and economic instability. Many schools close, and those that remain become increasingly unaffordable. Students that pursue higher education usually come from wealthy families. However, a variety of companies emerge to fill gaps. These companies offer short courses that help people develop work skills, such as how to use different kinds of software and how to analyze data. Some of the teachers in these companies are individuals who found success in their industries and are well-known chefs, international authors, famous engineers, and business executives of all kinds, for example. They have huge social media followings and are celebrity instructors. These companies do not provide any kind of financial aid, and access to their courses usually comes with strings attached, such as contracts to do temp work for the company.

This scenario describes a situation in which traditional universities are rare and inaccessible for most and the “social media university” emerges to fill the gap. It anticipates a future in which technology companies, particularly social media companies, further commodify education according to neoliberal logics. This style of university is a platform-based form of digital higher education in which celebrity experts and influencers occupy the role of educator as a function of their social media followings and professional prestige. Without financial support, learners/users exchange labor for skills development, while wealthy students continue to attend more conventional institutions to pursue their interests. The notion of a social media university reflects the interest of education technology startups which feature online education experiences offered by celebrities and influencers, as we examined in this paper.

* published in the the inaugural issue of the Journal of Open, Distance, and Digital education (see a review by Tony Bates).

2033 – Future education scenario 2 of 3

By George Veletsianos

On August 27, 2024

In future education, futures, my research, speculative fiction, speculative futures

Future 2: The year is 2033. After a period of instability brought about by the disastrous effects of climate change, biodiversity loss, and global conflict, higher education has become totally focused on addressing these crises. Earlier efforts have been vastly scaled up to focus education resources on supporting climate justice for the most vulnerable people and places in the world. Universities have become hubs of local knowledge and places for community cultural and scientific development. In these spaces students develop climate and peoplefriendly trades and skills. They also develop their critical and creative thinking focused on decolonization and anti-racism. Learning happens through projects and through solving local problems, and learners of all ages join programs based on interest, curiosity, and community need.

The second future – the university for local community and local knowledge – pivots towards more regenerative forms of education, with a focus on systems-level solutions to imagined disasters of the next decade. With respect to anticipatory regimes, this future departs from the strict techno-utilitarian approach to embrace more relational modes of teaching and learning. Universities in this scenario have a mission grounded in justice and supporting knowledge for communities, with an emphasis on inter-generational learning relevant to specific places. This scenario is more utopian in its vision, even as it contends with a proposed future history of increasing climate and ecological catastrophe.

* published in the the inaugural issue of the Journal of Open, Distance, and Digital education (see a review by Tony Bates).

2033 – Future education scenario 1 of 3

By George Veletsianos

On August 26, 2024

In future education, futures, my research, speculative fiction, speculative futures

Future 1: The year is 2033. In the decade following the COVID-19 pandemic, higher education has increasingly become driven by collecting and analyzing vast amounts of student data, such as tracking student time online, physiological data, employment rates, etc. Learners attending public colleges and universities primarily pursue technical skills associated with a few streams of programs, including computer programming (such as the development of Artificial Intelligence and green technology), health, economics, finance, and business. The arts, social sciences, and humanities are no longer publicly funded. Learners can pursue such programs in expensive private universities, but only a few can afford them.

This is a future in which technical and business education dominates. This is the scenario in which higher education is almost totally oriented towards economic demands and expectations. We modeled this scenario after work in the literature which emphasizes futures in which the arts and humanities decline due to their lack of economic practicality. In such examples, the survival and growth of higher education heavily features future labor as a key indicator of institutional success, including meeting demands for skilled technologists and finance workers. Additionally, surveillance technologies are further integrated into institutional apparatuses, with data being a key management tool of student learning and outcomes. Already a concern in education at all levels, a number of education scholars have speculated about the risks of increasing use of these types of education technologies, many suggesting negative outcomes resulting from it.

* published in the the inaugural issue of the Journal of Open, Distance, and Digital education (see a review by Tony Bates).