Category: research shorts

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

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.

Research Dissemination, Research Mobilization, and Reaching Broader Audiences

By George Veletsianos

On June 21, 2017

In emerging technologies, engagement, my research, open, research shorts

I gave an ignite talk at the Canadian Society for the Study of Higher Education in early June, sharing some of the lessons learned in creating whiteboard animation videos for mobilizing research and reaching broader audiences. We’ve now turned that talk into a whiteboard animation video. It’s all very meta. Here it is below:

Video and audio summaries of our research

By George Veletsianos

On August 29, 2016

In my research, research shorts, scholarship, sharing

I wrote a guest post for the Chronicle’s Prof Hacker section describing our use of video and audio to summarize our research findings. The post was published today and it is available here, but I am reposting it below as well.

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Street: First pass – CC licensed image

I use an eclectic assortment of learning resources in my courses. Books, peer-reviewed journal articles, op-eds, white papers, websites, documentaries, lecture videos, podcasts. Readings – especially peer-reviewed journal articles – are integral to my teaching, but I am intentional in my desire to go beyond text, to be inclusive and diverse in my selection of learning resources. In my research, and in my attempts to include multimodal learning resources in my teaching, I discovered that we could do a better job at sharing our scholarship.

One of the ways that I am using to share my scholarship in different ways is through the creation of short video and audio clips that accompany each one of my published papers. I believe these might be helpful to colleagues, students, and broader audiences. Colleagues might use them as a way to introduce, humanize, and explore a topic. Students might access them at times when listening is preferable to reading. For example, I listen to podcasts on bus rides because reading on the bus makes me feel dizzy. Others might be in the same predicament. Some students in our research noted that they watched video lectures when engaging in other activities – such as cooking – as a way to accommodate their studies in their busy lives. Broader audiences, such as the general public or journalists, might find video and audio clips valuable as well, as these clips contain information that usually resides behind journal paywalls.

We have created a dedicated YouTube channel to host these videos. Here is a playlist of some of them:

The audio is hosted on my personal SoundCloud channel. Here’s a playlist:

We follow a simple process to create these. For each published paper, I collaborate with members of my research group to (a) write a script, (b) record an mp3 file, and (c) produce an animated movie. These media are produced by two individuals using off-the-shelf software. One person writes the script and shares it with the other using a shared Dropbox folder. When I narrate the script, I use Audacity to create the audio file. When my colleague Laura Pasquini narrates, she uses GarageBand. We use instrumental music shared under Creative Commons licenses as background. Once the audio file is created, I post it in on my SoundCloud channel and users can stream it or download it from there. Next, we use VideoScribe to create the animation and since the software is cloud-based, we can both review the draft version of the video prior to publication. The final video is then posted on a YouTube account dedicated to these videos.

My research team and I are enjoying exploring the many ways available at our disposal to share our scholarship. We know that creating a video trailer or writing a blog post about a publication isn’t a substitute for high-quality scholarship, but we are enthused at the opportunity to use new technologies to mobilize our research. What are some other ways that you have discovered to share your research with colleagues, students, and the broader public?

A short video on issues important to Digital Learning Environments

By George Veletsianos

On June 21, 2016

In research shorts

Digital learning environments are everywhere. Learning Management Systems (LMS) are used almost universally. People learn how to play the ukulele by watching YouTube videos. They learn how to grow organic veggies by joining Facebook groups. And they join a vast array of specialist communities that help them improve their skills, from knitting, to drawing, to managing their finances. All these technologies, resources, and platforms that people use to learn online, constitute digital learning environments. I wrote a chapter (pdf) that draws attention to four important issues that researchers and designers need to consider when designing/studying digital learning environments (organizational structures, design, guidance, and technology’s lack of neutrality). Below is a video I created with Laura Pasquini summarizing this chapter. And if you just want the audio, it’s available on my soundcloud channel.

Veletsianos, G. (2016). Digital Learning Environments. In Rushby, N. & Surry D. (Eds) Handbook of Learning Technologies (pp. 242-260). Wiley.