In our previous blog post, students shared some of their perspectives on GenAI in higher education and reflections on interactions in the first of three summer meetings of the 2026-2028 research seminar on Rethinking Engaged Learning in the Age of GenAI. I learned much from considering their thoughts on GenAI in higher education and anticipate that you will gain some insights from this second half of the interview, as well, as we focused on supporting collaborative research and suggestions for faculty on thinking about the use of GenAI in teaching and learning in higher education. 

Learning From Students’ Perspectives

Aaron Trocki: Seminar participants met with the three of you to get your feedback on their research questions, overall research plan, and their specific research methods. Describe an interaction you had during this time that stands out to you as interesting. What was interesting about this interaction? Why? 

Chiebuka Tor: For background, the interactions varied widely across the different teams. Some groups already had a shared understanding and fleshed out research questions, and then they started making strong progress while others were still trying to create a sustainable research plan. One group stood out because it seemed like their members recognized and pulled on the strengths of one another. So, they already had a research plan and a question put together, and as they started to draft a survey, they used [one] team member that was more technical and had experience with statistics. While for the larger research plan, they used somebody who was versed in literature and had expertise in planning larger and collaborative works. They’ve also participated in the seminar before, so it seemed like pulling on the strengths of each individual in the group helped them to move along further and more efficiently. 

Mariama Jalloh: I think for me, again, it was my interaction with one of the teams. But I also think this applies to all the other participants and groups as well. A lot of the interaction was them actually being curious and asking us instead of assuming. I think what stood out to me was that the participants were actually learning from us as well. The reaction I got from one group about NotebookLM made me think, “Oh, even faculty don’t fully understand every tool yet. They’re not experts on every single tool.” So, I think it’s important to ask the students. Ask us how we’re using AI instead of assuming how we’re using it. 

Matthew Cornick:The most interesting part to me was during the initial segment of time during the seminar when we were working on the research questions themselves. In thinking about the conceptual ideation each participant brought, it was fascinating to see the different viewpoints that people are approaching at these problems from. There’s one particular interaction that I remember pretty clearly, and it was when I was having a casual conversation with one of the participants. They mentioned something along the lines of how students are using generative AI every single day. And I’m thinking to myself in retrospect, that’s a very interesting assumption! Like, if you take it from my point of view, I don’t use chatbots or generative AI every single day. And I was not under the impression that every other student is using generative AI every single day, which is interesting twofold. One, that’s an assumption on their part, and that’s an assumption on my part, and they are very different. Secondly, as if my assumptions were not being challenged enough, what if I was wrong and I am using it every day? I wondered then, even if I’m not actively searching out and using generative AI every single day, am I accidentally using it? And then I wonder how active use of GenAI versus inactive use affects academic approaches from students. Interactions and the ensuing contemplation is what makes this seminar so eye-opening. Being able to utilize such a diverse room of perspectives to zoom out to the bigger picture truly make the experience. 

Advice for Faculty Teaching With GenAI

Aaron Trocki: In reflecting on your experience in July’s research seminar, what advice would you give to higher education faculty seeking to understand how to fold GenAI into high-quality teaching and learning practices? 

Matthew Cornick: My take on this one is for those who may have been avoiding addressing GenAI for one reason or another. If we’re being honest, generative AI is here, it has been here, and if we haven’t started trying to put steps in the right direction towards addressing this and understanding how to fold it in, we are behind. Fortunately, the technology itself makes catching up faster than ever. My advice is that I wouldn’t start looking for a “right answer” because the perfect solution for you is unlikely to fall from the literature gods. I say there is no bad first step. No matter what, from that first step you’re going to learn something valuable within your GenAI niche that no other person could have told you. And so, just try something, you know. Maybe attempt to weave GenAI into lesson planning or put out a student-led GenAI-enabled task to see where things go. How do the students approach it? How can you adapt to support or redirect their thinking? Again, if you’re afraid, don’t know where to begin, or otherwise, your best friend is iteration. Find a baby step you are comfortable with and develop it in conjunction with your students from there. 

Chiebuka Tor: So I would say I would encourage faculty to clearly communicate their expectations and what they consider to be appropriate for the use of AI to the students early in the course. I think there are still a lot of questions about how to best use it in the classroom. But I think one thing that we can all agree on is the idea that different disciplines need different approaches. So therefore, I would say [we need] a faculty member who understands the capabilities of AI, the limitations of AI. But showing students which tools would be helpful in their class and which they think they should stay away from would help enrich the learning of their students and expose them to using AI in the academic and potentially professional workplace in the future. 

Mariama Jalloh: I agree. I think another thing is that faculty should at least try to understand or ask why students are even using AI in the first place. You have certain classes where you don’t even think about using AI. Why is that? Is the class more interesting? Is the professor more clear in how they communicate their expectations for the tasks they’re giving out? Or why do you use AI in certain classes? Is the communication unclear, or is the task just hard to understand? I feel like professors and faculty should really try to understand that “why.” I also don’t think they should generalize one AI rule for the whole class. I think if they’re more clear about their expectations for different tasks, like can you use AI for brainstorming? Can you use AI for other things? They should be more clear about that. Ask the students why they’re using AI first, then think about what you can change or adjust in the way you’re teaching or how the class is structured. 

Aaron Trocki: Excellent responses and valuable information. Thank you for participating. 

Takeaways from the Conversation

After the interview, Aaron developed a list of key takeaways regarding GenAI in higher education teaching and learning practices. 

• University faculty may be unaware of the various capabilities of GenAI and ways students use this technology in their academics. 
• Assumptions about how and when students use GenAI are often misaligned with students’ actual practice. 
• Defining appropriate student use of GenAI in their academics is a moving target and depends on the evolving capabilities of GenAI, the disciplinary context, and instructional goals. 
• Approaching students as partners and listening to students to understand their perspectives will assist faculty with addressing the use of GenAI in academics. 
• Students desire clear expectations from faculty regarding GenAI in their coursework.  

These students’ perspectives may assist faculty with approaching GenAI in higher education towards educative outcomes. Consider the messaging you give about the use of this technology and the teaching and learning practices you intend to foster in your own classroom.  

About the Authors

Aaron Trocki is an associate professor of mathematics at Elon University and co-leader of the 2026–2028 CEL Research Seminar, Rethinking Engaged Learning in the Age of GenAI. A 2023–2025 CEL Scholar, his research focuses on mathematics education, technology for teaching and learning, and assessment practices beyond traditional grading. His recent work explores the role of generative AI in higher education, including AI-supported assessments for learning and the pedagogical redesign of courses to foster meaningful student engagement.   

Mariama Kindie Jalloh, 2026–2028 CEL Student Scholar, is a public health major with a minor in biology.

Chiebuka Tor, 2026–2028 CEL Student Scholar, is a finance major with a minor in communications. He is an Odyssey Scholar, S.M.A.R.T. program mentor, Vice President of the African Diaspora of Elon, and SGA senator for the Love School of Business.

Matthew Cornick, 2026–2029 CEL Student Scholar, is a computer science major with minors in cybersecurity and game design.

How to Cite This Post 

Trocki, Aaron, Mariama Jalloh, Matthew Cornick, and Chiebuka Tor. 2026. “Students’ Perspectives on GenAI in Higher Education: Research Seminar Reflections, Part 12” Center for Engaged Learning (blog), Elon University. October 13, 2026. https://www.centerforengagedlearning.org/students-perspectives-on-genai-in-higher-education-research-seminar-reflections-part-2/.