Interview and post by Lisa Abrams, PhD

Ane Turner Johnson, PhD
Professor, Educational Leadership, Administration and Research
Rowan University, Glassboro, New Jersey, United States

As generative artificial intelligence (AI) becomes more and more ubiquitous, I have many questions about what this means for research – both in general and for the scholarship of teaching and learning (SoTL) in particular. I can see many ways in which AI technologies can help SoTL researchers work more efficiently and possibly more accurately. I can also see ways in which AI may infiltrate SoTL work and decrease both the quality and integrity of the research, ultimately having a negative impact on the field and, more importantly, our students’ learning.

Many researchers call into question the impact of using AI in the research process, with much of the focus being on the use of large language models (LLMs) to write or help write published research (Grove, 2026). Because of biases inherent in LLM training (e.g., being trained with content with biases related to gender, ability, race, & ethnicity), generative AI is poised to be a significant threat to the integrity of science worldwide (Lund et al., 2023), SoTL included. There are also serious ethical concerns related to:

• LLM distortion based on types of training materials
• Potential misinformation included in scholarly work
• Copyright law
• Citation practices (e.g., hallucinations, the “Matthew Effect”, effects on research expertise)
• Intellectual ownership
• Authorship
• Plagiarism (e.g., LLMs using people’s work/wording without giving credit)
• Methodological issues (e.g., transparency in science & the replicability of research)
• Tenure & promotion processes (the use of LLMs to speed up the research process can lead to decreases in creativity & innovation in research, inhibiting scientific progress; Lund et al., 2023).

On the other hand, AI technologies may have significant benefits to scientific progress and research by:

• Facilitating peer review
• Taking over tedious and repetitive tasks (e.g., editing for grammar, adherence to formatting guidelines)
• Helping to disseminate research findings through thorough indexing. summaries, and metadata about existing research
• Translating complex ideas into accessible language for non-experts
• Improving the clarity of already drafted writing
• Suggesting citations, sources, etc. (above from Lund et al., 2023)
• Pulling themes from qualitative data quickly (minutes/hours instead of weeks; Baniassad, 2025)

The use of AI in SoTL research is increasing (Baniassad, 2025) along with the use of AI in other kinds of research. SoTL will likely see a further increase in the use of AI as more information and guidance for its use becomes available (Baniassad, 2025), and since academic writing is a necessary and incredibly important part of research and education alike (Fute et al., 2026), it is imperative that we think critically about the role that AI can play in our research worlds and the consequences of its use (good, bad, and neutral).

OUR PROJECT

When I joined the International Society for the Scholarship of Teaching & Learning (ISSoTL) International Collaborative Writing Group (ICWG) focused on the impacts of AI on SoTL research and dissemination, I was excited to learn about what different stakeholders in the SoTL field had to say about the issue of AI. To that end, I interviewed Rowan University Professor of Educational Leadership, Administration, and Research and Chair of Rowan’s Institutional Review Board (IRB), Dr. Ane Johnson. As a researcher, Ane has two primary focuses: 1) African refugees’ access to postsecondary education in the United States (US), and 2) faculty and staff in African higher education, “particularly how they incorporate Indigenous knowledge systems into their curricula”. As part of her teaching work, Ane mentors Educational Leadership doctoral students on their dissertation research. As Chair of the IRB, Ane oversees the ethical evaluation of all research at the institution that proposes to use human subjects.

When the ICWG started talking about interviews for this project, one of the topics we discussed was the ethical issues related to AI use in SoTL – I immediately thought of Ane. Last year, Ane presented at Rowan University’s inaugural SoTL Summer Institute, discussing the IRB process and common ethical issues in SoTL work with faculty largely unfamiliar with human subjects research. Her discussions were both frank and helpful. I wanted to highlight Ane’s voice in our larger conversation about the intersection of AI and SoTL because she 1) is intimately familiar with the ethical issues related to SoTL research, 2) is an education researcher herself, and 3) is familiar with research going on across the university (as IRB Chair).

INTERVIEW INSIGHTS

During my conversation with Ane, several important themes emerged, discussed in more detail below:

1. AI as a “thought partner”
2. AI & student mentorship
3. The ethical issues related to AI use in research
4. AI’s impact on research quality

AI as a “Thought Partner”

Using AI while working can contribute to our ability to think more thoroughly about the issues we’re studying. For instance, Ane finds that it’s “extremely helpful for things like outlining ideas or identifying key areas within a topic”, with AI significantly decreasing the amount of time it takes her to orient herself to new topics within the field. The time-saving benefit of using AI for research is notable for some scholars. For Ane, AI has “helped me to get a better picture of my field when I’m preparing scholarship. What I see is AI is providing that assistance that before would take me so much time to figure out on my own, and now it is really cut back on that. So I see that as improving efficiencies, essentially, in higher ed”. Ane does an excellent job at explaining the potential for AI to be a helpful support tool in research: “I can see real potential for SoTL research, particularly in helping researchers design projects that are more impactful for both their students and their own practice”. She goes on to add that how SoTL scholars use AI in their work will probably look different based on their field of study.

AI & Student Mentorship

One of the ways that I hadn’t thought about the use of AI in research is its impact on mentoring students. Ane describes an experience that shows how much AI can help a student research advisor guide their student. When faced with a research report format she didn’t have previous experience with, Ane used ChatGPT to ask “‘How do I advise the student on how to write about an intervention in their dissertation?’”, and found it “incredibly helpful”. In an academic world where a faculty member might be overseeing a wide range of research topics and/or methodologies, AI can help orient the faculty member to what’s required for outputs/formats that they don’t have direct experience or expertise with. For instance, it helped Ane be able to guide her student through discussing an intervention in their dissertation. Used in this way, AI doesn’t fully replace expertise, but allows faculty to get quick and concise feedback or guidance for unknown aspects of their research field.

Another way in which AI can impact student research mentorship is navigating student use of AI in research. Part of Ane’s job now is to help “students think through the ethical implications [of AI use], especially given the pressures they face around time, cost, and completion”. In our conversation, Ane discussed her approach to communicating what was acceptable in terms of AI use in her advisees’ research. “I had to send an email out to my doctoral advisees the other day of, like, ‘here’s what I’m okay with, and here’s what I’m not okay with’”. Even so, Ane mentioned that because there’s not a lot of guidance for use of AI in research, she felt like her guidance had “no teeth to it. It’s just, ‘here’s what Ane thinks’”. This highlights the insecurity that many faculty have when it comes to guiding students on the use of AI in research (or in schoolwork). It seems particularly challenging because students “struggle with a sort of not thinking necessarily about the ethical [aspects of AI use]. They have a timeline, right? They’re paying for their degree. They have this pressure, and so sometimes they see an easier path to help facilitate that timeline. My job is, of course, to help them to see the ethical issues at play here. And so that’s been really challenging.”

At one point in our discussion about student mentorship, Ane said that she sees AI “improving my efficiency, but it also raises important questions for working with students: How do we guide students to use it ethically? And how do we address situations where the work no longer reflects an authentic learning process?” This calls for more guidance and agreed-upon conventions in order to be able to use AI effectively but not overstep the boundaries of AI use in research (what those boundaries are, we don’t really know yet). But since we don’t quite have stable guidance as far as appropriate AI use, guiding student use in AI can be particularly challenging.

The Ethical Issues Related to AI Use in Research

Many people agree that AI has presented new challenges to teaching, learning, and research alike. Ane points out that “students are using it, but not always effectively or ethically. Faculty are still figuring out how to respond. There’s a lack of shared understanding around appropriate use, and that creates uncertainty in how we guide and evaluate student work”. Even faculty within the same institution don’t necessarily agree on acceptable AI use for students. “We’ve had instances where a faculty member told a student it was acceptable to use AI for data analysis and that immediately raises the question, like, is it? The reality is, we haven’t reached consensus on this, even among faculty in my program.”

Ane and her colleague, Dr. Cecile Sam, recently published a book called Short Stories for Social Research. In it, they collaborated on the epilogue titled  “Don’t Ignore the AI in the Room” that discusses some of these issues. “In that piece, we grapple with how authorship is shifting. There may come a point where we are comfortable thinking about AI as a co-author or co-creator, but right now, we are still very much in the middle of figuring that out”.

In addition to the academic integrity issues that AI brings up, Ane discussed the ethical use AI in general.  “For example, dumping large amounts of potable water just to make our lives easier is probably not ethical”. The environmental and sustainability impacts of AI use has been widely discussed (e.g., United Nations, CBS News, BBC, Time Magazine), and it’s not a nominal problem. One of the problems that Ane proposes is that people are going to use AI regardless of its environmental impact since “we tend to prioritize short-term convenience”. She proposed that “one way to really be an ethical user of it is to be an investigator of it. You can’t just start wholesale using it, just assuming that the information you’re getting is right”.

An additional ethical issue with AI use is related to equity, something that Ane & I are both passionate about. Ane brought up the digital divide issue in relation to AI use. Syracuse University defines digital divide as “the gap between people who have meaningful access to digital technology and those who don’t” (Syracuse University, 2026). Ane pointed out that “because [Rowan’s] a regional institution and we have students who are first gen”, there are “students who may not have had the same level of access growing up as others. That affects not only their comfort with LLMs [like ChatGPT and Claude], but even with basic computer use.” Assuming that all students are completely comfortable and skilled with technology because of widespread smartphone use “doesn’t always translate well to more complex tasks on a computer. ” This is a significant barrier to the potential benefits that LLMs can have when it comes to knowledge, productivity, etc. and a clear access, inclusion, and equity issue. 

AI’s Impact on Research Quality

One topic that is important in the discussion about AI use in research is the impact on the quality of research being conducted and the quality of the scientific publications. According to Ane, being intentional about what people are feeding into LLMs is a big determinant of the quality of the output. “It still depends on how [the LLMs are] used. If you ask poor questions, you will get poor results….I think just because you have done a project doesn’t mean that it is a good project.” With the increasing pace of publications that using LLMs allow, Ane argues that publications are “coming out so quickly that a lot of it just isn’t very good. And I’m not just saying that for effect—I genuinely think a lot of it lacks rigor and isn’t particularly useful.”

Specifically referring to how people frame their SoTL projects, Ane notes the lack of grounding in prior SoTL discourse. “I think a lot of people do classroom-based research, and they don’t weight it into the tradition of SOTL.” She elaborates by pointing out that “if the author doesn’t do enough to address transferability or broader relevance, then it’s hard to know what to do with the findings.” SoTL has a philosophy and a deep tradition associated with it which should be respected, acknowledged, and used as the basis for situating new SoTL projects within the broader field. It’s important to make “sure that researchers…doing this type of work — because SOTL is research — are taking into consideration that large body of work that came before….you have to actually situate your work in what’s already been written.” Ane shared a personal reflection about this: “I often find myself wondering, ‘what are you really contributing if you’re not aware of what has come before?’” Ane suggests striking a balance between tradition and innovation: “I’m not suggesting that we need to be traditionalists. And I think AI has a lot of potential for us to sort of rethink some of our previous practices, but that doesn’t mean that we should forget that these things exist, and therefore our work should still touch upon them.” Ane adds that “for those who have become more skilled at working with large language models, there is a lot of potential to strengthen research design and execution.”

INTERVIEW REFLECTIONS

My conversation with Ane sparked a lot of thought about how AI could be useful in our research work, things that, I’m willing to admit, I wasn’t particularly open to before. But there were 2 things that hit home the most when thinking about what Ane had to say about SoTL research and AI use:

1. “We definitely need to be talking about this.” I think that there are many individual people having these types of conversations, but I haven’t been able to find larger conversations that are happening past the individual and/or department-level conversations.  It’s incredibly important that we, as researchers, discuss the potential use and impacts of AI in research.
2. The accessibility and equity issues surrounding AI use. For people who don’t have access to or knowledge of how to interact with LLMs, it could put them at a distinct disadvantage compared to those who have such access/knowledge. When asked, “What does public SoTL mean to you?”, Ane focused on the accessibility of SoTL research, not just in terms of whether people can access the findings, but also accessibility in terms of the way the information is presented. She states that research findings “should be written in language people can actually understand, not just for [academics] but for a wider audience”, people with disabilities. “I’m thinking about accessibility in a much broader sense than we often do, and to me, that’s really what public SoTL is about.”

Ane’s insight as an education researcher and Chair of the Rowan University Glassboro IRB calls into question many ethical and pragmatic issues related to AI use in research, especially SoTL research. I feel very fortunate to have had such a candid conversation with my colleague and appreciate the balance that she brought to the conversation.

A CALL TO ACTION

How can you help contribute to this conversation and larger issue in SoTL research? I encourage you to have discussions with your colleagues/departments/researcher friends about the use of AI in research, discussing such questions as:

• How can we use AI ethically in research?
• How can we fight some of the risks that are inherent in using AI in research?

Or you can discuss the questions posed by Banaissad (2025):

• “While the technology is in flux, how do we ensure that AI-generated responses accurately reflect the nuances of the research?
• How can we maintain academic rigour in a system where responses may be generated probabilistically?” (p. 94).

The next time you reach for generative AI for work in your research, I invite you to pause and reflect, even just for a few seconds, about how to better use AI in upholding the integrity of science.

References

Baniassad, E. (2025). Generative SoTL: Exploring AI in Inquiry. In Hays, L., Miller-Young, J., & McCollum, B. (Eds.) Educational technology and the scholarship of teaching and learning: Asking questions about our practices (pp. 87-94).

British Broadcasting Corporation (BBC; 2026, March 28). How much water does AI consume? BBC Audio. https://www.bbc.com/audio/play/p0n95zxd

Fute, A., Oubibi, M., Amihere, A., Kangwa, D., & Msafiri, M., M. (2026; pre-print). Institutional support for ethical AI adoption in higher education amid the rising trend of manuscript retractions. Humanities & Social Sciences Communications. https://doi.org/10.1057/s41599-026-07235-7.

Grove, J. (2026, April 7). Tackle ‘AI slop’ in education research ‘or lose teacher trust’. Times Higher Education. https://www.timeshighereducation.com/news/tackle-ai-slop-education-research-or-lose-teacher-trust

Kelly, J., Pham, S., & Reilly, S. (2026, June 6). How much water does AI really use?. CBS News Data. https://www.cbsnews.com/projects/2026/how-much-water-ai-uses/

Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023). ChatGPT and a new academic reality: Artificial intelligence-written research papers and the ethics of large language models in scholarly publishing. J Assoc Inf Sci Techol, 74, 570-581. DOI: 10.1002/asi.24750.

Shah, S. (2026, June 3). AI could use as much water as 1.3 billion people by 2030, U.N. report warns. Time. https://time.com/article/2026/06/03/ai-global-water-resources-un-report/

Syracuse University (2026, January 29). What is the digital divide? Meaning, causes & impact. Syracuse University School of Information Studies. https://ischool.syracuse.edu/what-is-the-digital-divide/

United Nations (2026, June 4). AI’s environmental costs threaten water, land and climate. UN News. https://news.un.org/en/story/2026/06/1167658

AI Disclosure: Generative AI was not used in writing this article. However, ChatGPT, Claude, and Gemini were all used to generate a general outline of what this kind of article should address using the following AI prompt in all three programs: “I want to write a blog post highlighting a brief interview that I did with a colleague about AI use in the scholarship of teaching & learning (SoTL). This is more informal than I usually write. Please create an outline for this blog post to get me started”. I used ideas from all three responses to create the outline I used to write this article.