Date
Publisher
arXiv
This study investigates how undergraduate students engage with ChatGPT in
self directed learning contexts. Analyzing naturalistic interaction logs, we
identify five dominant use categories of ChatGPT information seeking, content
generation, language refinement, meta cognitive engagement, and conversational
repair. Behavioral modeling reveals that structured, goal driven tasks like
coding, multiple choice solving, and job application writing are strong
predictors of continued use. Drawing on Self-Directed Learning (SDL) and the
Uses and Gratifications Theory (UGT), we show how students actively manage
ChatGPTs affordances and limitations through prompt adaptation, follow-ups, and
emotional regulation. Rather than disengaging after breakdowns, students often
persist through clarification and repair, treating the assistant as both tool
and learning partner. We also offer design and policy recommendations to
support transparent, responsive, and pedagogically grounded integration of
generative AI in higher education.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
