Abstract
GenAI tools have diffused rapidly across higher education and are increasingly embedded in students' everyday learning practices. However, scholarly understanding of how students actually use GenAI for learning remains limited. This project addresses this gap by analyzing students' interactions with an AI-based virtual Teaching Assistant integrated into Canvas, using retrieval-augmented generation (RAG). Two forms of data will be analyzed, including complete chat logs and post-session survey responses. This study contributes a process-oriented understanding of student–AI interactions and their implications for pedagogy and AI system design.
Recommended Citation
Yang, Kai; Zhang, Wei; and Du, Kui, "How Students Learn with Generative AI: Evidence from Student–AI Interactions" (2026). AMCIS 2026 TREOs. 41.
https://aisel.aisnet.org/treos_amcis2026/41