New Tool Uses AI to Help Undergraduates Think Critically About Research
For Immediate Release
An interdisciplinary team of researchers has developed and demonstrated a step-by-step framework called Socratic Challenger that uses artificial intelligence to help undergraduate students cultivate critical thinking skills. Specifically, the framework uses AI as a collaborator to develop research questions that can be pursued in laboratory or classroom settings.
“Formulating a good research question is a bottleneck in undergraduate inquiry,” says Aram Mikaelyan, corresponding author of a journal article on the work and an associate professor of entomology at North Carolina State University. “Students can name topics they’re interested in, but struggle to find ways to turn that interest into a meaningful research question that makes sense and can be developed into a research project.
“We also know that undergraduates are often turning to AI for assistance with academic work without having a clear idea of what is expected of them or what they are trying to accomplish, which is not helpful,” Mikaelyan says. “So we developed a detailed, step-by-step workflow that allows students to use AI tools, requires students to think critically, and helps students develop research questions that can be used to enable undergraduate research.”
“Ideally, students could work with mentors to learn how to develop a meaningful research question – but that’s not feasible given the number of students,” says Erin McKenney, co-author of the article and an assistant professor in NC State’s College of Agriculture and Life Sciences. “This framework, which we call Socratic Challenger, provides instructors with a new tool that can help to address that need.”
Socratic Challenger is a workflow consisting of eight steps, five of which are AI-enabled. The steps range from identifying a topic of interest to receiving feedback from (undergraduate) peers and instructors.
“To be clear, the AI is not generating the research question,” says Mikaelyan. “Instead, the AI is essentially being used to interrogate the student’s process and help them develop the habits of mind necessary to think critically about research. Is this really a gap in what we know about the topic? Is this question novel? What resources would I need to address this question, methodologically? And so on.”
To see whether Socratic Challenger was actually useful, the researchers conducted a proof-of-concept study with 45 students in an undergraduate ecology course. The students worked through the Socratic Challenger framework over a nine-week period and ultimately were tasked with submitting an abstract for the proposed research.
“We wanted to know if Socratic Challenger works and whether some steps in the workflow work better than others,” says McKenney. “We also wanted to know whether students feel like it makes a difference and, if so, how.”
“What we found is that the students who made use of the Socratic Challenger framework developed really thoughtful research questions,” says Mikaelyan.
The researchers also found that the step-by-step structure of Socratic Challenger was important, and every step in the workflow was deemed important by the students.
“Technology on its own doesn’t help student understanding,” says Dhvani Toprani, co-author of the paper and assistant director of learning design and support at Elon University. “But using technology as part of an intentional, step-by-step process did benefit the students’ ability to learn and think critically about research question development.”
“Basically, Socratic Challenger gives students the opportunity to have their ideas challenged earlier in the process of developing a research question,” says Olivia Mathieson, co-author of the paper and a Ph.D. student at NC State who participated in the initial testing of the workflow and discussions for classroom adaptation. “Learning how to think critically about their own ideas in a structured way is a valuable skill.”
“Socratic Challenger is iterative and open-ended, which makes it fairly generalizable to any unstructured, messy and complex learning environment,” says Toprani. “However, educational tools and educational research are always context dependent, and additional research is needed to broaden our understanding of how well this tool works and where it could make a positive difference.
“Could it be used in undergraduate courses in other disciplines? Could it be used to help students think critically about things other than how to develop a research question? Those are good questions, but more work would be needed to answer them.”
The paper, “The Socratic Challenger: a structured GenAI-assisted workflow for undergraduate research inquiry,” is published in the open-access journal Frontiers in Education. This research was supported by a Scholarship of Teaching and Learning Institute mini-grant from the NC State University Office for Faculty Excellence.
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Note to Editors: The study abstract follows.
“The Socratic Challenger: a structured GenAI-assisted workflow for undergraduate research inquiry”
Authors: Aram Mikaelyan, Erin A. McKenney and Olivia L. Mathieson, North Carolina State University; Dhvani Toprani, Elon University
Published: Aug. 17, Frontiers in Education
DOI: 10.3389/feduc.2026.1913451
Abstract: Generative artificial intelligence (GenAI) tools are increasingly used during early stages of academic inquiry, yet their role in supporting research-question development remains unclear. This mixed-methods study evaluated an eight-step GenAI-assisted workflow implemented in an undergraduate ecology course. The workflow alternated AI-supported exploration with literature verification, revision, and human feedback. The study population comprised students enrolled in the course, and all 45 students who consented to research use of their coursework were included; therefore, no sample-size formula was applied. Quantitative data consisted of ordinal ratings of the perceived contribution of each workflow step, while qualitative data consisted of students’ written reflections. The rating items corresponded directly to the eight implemented workflow stages, and qualitative themes were independently reviewed and refined by two researchers. Quantitative results showed that perceived usefulness differed modestly across steps (Friedman χ2 = 14.53, p = 0.0426; Kendall’s W = 0.046), although the median rating for every step was “Helped a lot,” and no pairwise comparison remained significant after correction. The ratings showed acceptable internal consistency (Cronbach’s α = 0.745). Qualitative analysis indicated that students viewed AI primarily as a thought partner that helped them narrow and refine ideas, while the broader scaffolded workflow supported progress through literature engagement, structured pacing, and feedback. Together, the findings suggest that GenAI can support undergraduate research inquiry when embedded within a structured process that requires students to verify evidence, evaluate suggestions, and retain responsibility for the final research question.
