Modeling Changes in Student AI-Engagement Profiles

Project Team

Faculty Lead: Melissa R. Hunte, Assistant Professor, Department of Language & Literacy Education, Faculty of Education 

Staff Lead: Warren Code, Associate Director, Skylight (Science Centre for Learning and Teaching), Faculty of Science 

Graduate Student Researcher: 

• Alexis Webster, Ph.D. Student, Measurement, Evaluation, and Research Methodology, Faculty of Education 

Understanding how students really use GenAI at UBC

Generative artificial intelligence (GenAI) is rapidly reshaping how students learn, engage with academic work and disciplinary knowledge, and seek support. Its growing presence in higher education has prompted critical discussions among educators, administrators, and policymakers. However, many of these conversations and interventions treat the student population as largely homogeneous. Since learning is also a social, relational, and motivational process, it is important to examine the heterogeneity of students’ GenAI use, and its social and relational antecedents. While there have been preliminary efforts to understand student attitudes and GenAI use, UBC has not yet undertaken a comprehensive campus-wide study examining whether students’ AI use is shaped by academic belonging, beliefs in their ability, motivation, identity, language background, and perceived support, or the extent to which existing AI-literacy workshops and courses are effective in supporting  learning-oriented AI engagement. It also remains unclear whether additional or alternative forms of interventions are needed for different groups of students. This project addresses that gap by positioning UBC Vancouver as a living lab for responsible, equitable, and learning-oriented AI engagement. 

Profiling student GenAI use and testing what shifts it toward learning 

The Modelling Changes in Student AI-Engagement Profiles project will examine student GenAI use across faculties, departments, and program levels, with attention to students’ ethnic and linguistic backgrounds, academic belonging, and other motivational and demographic factors. Using a person-centered research design, the study will identify distinct profiles of AI engagement based on students’ reported frequency of use, purposes, timing, motivations, autonomy, competence, social connectedness, perceived AI literacy, ethical use, academic ownership, academic self-efficacy, belonging, and perceived impact on learning. This approach will identify what kinds of AI-use patterns exist and which student groups may benefit from differential supports. 

The project will also partner with existing courses and workshops on campus that offer AI awareness, training, and/or practice to examine their effectiveness in shifting students’ AI-use behaviours, beliefs, and learning orientations. Students will complete surveys before and after participating in these learning opportunities. Statistical modelling techniques will be used to identify AI-engagement profiles and examine whether students move from learning-displacing AI engagement profiles toward more strategic, ethical, and learning-oriented patterns of use. 

Turning evidence into action for responsible GenAI at UBC

Expected outcomes include a validated AI-use and motivation survey, evidence-based student AI-use profiles, intervention findings, and recommendations to support student and instructors, and shape academic policy. Knowledge sharing will include campus reports, presentations to teaching and learning units, student-facing summaries, and scholarly outputs. By generating actionable evidence from real campus contexts, this project will support UBC’s leadership in responsible AI education and contribute to broader regional, national, and global conversations about the social, relational, and motivational factors that inform students’ GenAI use in higher education. 

Related Publications 

_{Dickinson, J., & Marshall, M. (2026). Trained to stop learning: How students are experiencing assessment and learning in an age of AI. Wonkhe.}

_{Bewersdorff, A., Hornberger, M., Nerdel, C., & Schiff, D. S. (2025). AI advocates and cautious critics: How AI attitudes, AI interest, use of AI, and AI literacy build university students' AI self-efficacy. Computers and Education: Artificial Intelligence, 8, Article 100340.}

_{Wang, K., Cui, W., & Yuan, X. (2025). Artificial Intelligence in Higher Education: The Impact of Need Satisfaction on Artificial Intelligence Literacy Mediated by Self-Regulated Learning Strategies. Behavioral Sciences, 15(2), 165.}

_{How UBC Students Are Using Generative AI: A 2026 Update. UBC: A.I. in Teaching and Learning.}

_{Navigating AI in the Classroom: A Student Survey. UBC: Artificial Intelligence, Academic Integrity, and Writing.}