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.
