Daniel Chang is an Assistant Professor in Distance Education in the Faculty of Humanities and Social Sciences. He currently directs the AI-EDU Research Lab (aiedulab.ca), where he leads a program of research on the use of generative AI in higher education.

Daniel has a particular focus on student AI disclosure practices, self-regulated learning (SRL), academic integrity, and equity in post-secondary contexts. Specifically, his current empirical work examines how undergraduate students decide whether and how to disclose their use of generative AI in coursework. Using mixed methods, he investigates the relationship between students' willingness to disclose their AI use and academic performance, and how dispositional factors such as Need for Cognition shape students' moral judgments about AI use. This work is grounded in self-regulated learning theory, drawing on the Winne–Hadwin and Zimmerman SRL traditions.

He has held concurrent SSHRC funding and collaborates with researchers at Mount Saint Vincent University, Athabasca University, Simon Fraser University, and Fairleigh Dickinson University Vancouver. His broader scholarly commitments center on linguistic justice, equity, and the intersection of educational psychology with emerging questions in human–AI interaction.

Dr. Chang is an educational psychologist studying the ethical use of generative AI in higher education, with a focus on student disclosure practices, self-regulated learning, and academic integrity. His research group at the AI-EDU Research Lab uses mixed-methods approaches to generate evidence that informs institutional policy, classroom practice, and equity for linguistically diverse learners in post-secondary contexts.