Generative AI is rapidly transforming higher education, yet there is limited real-world data on how students actually use these tools in their learning. This project fills that gap, providing data-informed insights that can inform the development of effective instructional strategies.

This project explores students’ perspectives on the benefits and concerns of AI. It also investigates how students interact with AI tools in the classroom—examining the questions they ask, the ways they engage, and the interaction patterns associated with learning outcomes. 

In four semesters of data collection (spring 2024 through fall 2025), we have gathered data from 41 courses, with participation from over 10,000 students. As we expand the AI tools under the study and as additional courses and students participate each semester, this number will continue to grow

Research Goals

  • Understand students’ experiences with generative AI in the classroom.
  • Explore students’ attitudes, beliefs, and expectations regarding AI.
  • Analyze how students interact with AI tools—the questions they ask and the nature of their conversations.
  • Identify the types of interaction patterns associated with better learning outcomes.

How We’re Answering These Questions

  • Analyzing real student and AI interactions: examining actual conversation transcripts for a genuine view of how students use AI tools.
  • Integrating multiple data sources: combining conversation data with surveys and performance measures to connect behaviors with attitudes, context, and outcomes.
  • Using a large, diverse sample: thousands of students across multiple courses and disciplines for robust, generalizable findings.
  • Collaborating across expertise: pairing learning analytics specialists with faculty subject matter experts.
  • Applying innovative methods: blending qualitative and quantitative analysis, from hand-coding to advanced AI models like BERT and novel approaches such as Epistemic Network Analysis.

Get Involved

If you plan to implement generative AI for your students’ use in your classroom, we invite you to join our research group. We support projects involving a wide range of AI tools, from chatbots and tutors to custom GPTs. By collaborating, you’ll:

  • Gain insight into how your students are engaging with AI tools.
  • Help shape the research questions and direction based on your instructional context.
  • Be part of presentations and publications that share findings across the academic community.

OTLT Research and Analytics will handle the IRB process, data collection, and analysis, making participation simple and straightforward. Together, we can advance the understanding of AI’s role in education and support its thoughtful, evidence-based integration into teaching and learning. Contact us for more information.

Participating Faculty Members

We are proud to collaborate with faculty across multiple disciplines.

Accounting

  • Pam Bourjaily, associate professor of instruction
  • Carl Follmer, associate professor of instruction

Biology

  • Erin Irish, associate professor
  • Krista Osadchuk, assistant professor of instruction
  • Brandon Waltz, assistant professor of instruction

Chemistry

  • Ned Bowden, professor
  • Adam Brummett, associate professor of instruction
  • Renee Cole, professor
  • Stephanie Eveland-Parrott, assistant professor of instruction
  • Kefa Onchoke, visiting professor
  • Michael Sinnwell, assistant professor of instruction
  • Amy Strathman, associate professor of instruction

Civil and Environmental Engineering

  • Ibrahim Demir, adjunct associate professor
  • Humberto Vergara Arrieta, assistant professor

Communication Studies

  • David Supp-Montgomerie, associate professor of instruction

Computer Science

  • Erik Krohn, professor of instruction
  • Sriram Pemmaraju, professor
  • Alberto Segre, professor

Economics

  • Yuan Liao, professor
  • Alexandra Nica, professor of instruction

Electrical and Computer Engineering

  • Najeeb W. Najeeb, assistant professor of instruction

Finance

  • Martin Grace, professor

Journalism & Mass Communication

  • Sang Jung, Kim, assistant professor

Languages, Linguistics, Literatures, and Cultures

  • Jan Steyn, associate professor of instruction

Marketing

  • Robert M. Rouwenhorst, associate professor of instruction

Mathematics

  • Wade Bloomquist, assistant professor of instruction
  • Cynthia Farthing, associate professor of instruction
  • Olga Sokratova, assistant professor of instruction

Nursing

  • Nai-Ching (Allison) Chi, associate professor
  • Heather Dunn, clinical associate professor

Physics and Astronomy

  • Jane Nachtman, professor
  • David Nataf, assistant professor

Rhetoric

  • Elizabeth Crawford, visiting assistant professor
  • Deirdre Egan, associate director of the Writing Center

Data Partners

We would like to thank the following teams for providing access to data that supports this research:

  • Macmillan
  • Pearson
  • Cogniti/University of Sydney

Publications

Russell, J.-E., et al. “Unlocking Insights: Investigating Student AI Tutor Interactions in a Large Introductory STEM Course.” LAK '25: Proceedings of the 15th International Learning Analytics and Knowledge Conference, Association for Computing Machinery, 2025, pp. 451–461. https://doi.org/10.1145/3706468.3706524.

Have Questions?

We deliver a holistic view of student learning experiences and provide actionable insights.