"Everyone's using it, but no one is allowed to talk about it": College Students' Experiences Navigating the Higher Education Environment in a Generative AI World
Yue Fu, Yifan Lin, Yessica Wang, Sarah Tran, Alexis Hiniker
TL;DR
This study investigates how environmental factors on college campuses shape student engagement with generative AI, addressing a gap in qualitative insight beyond individual motivations. Through 23 semi-structured interviews across undergraduate, master's, and PhD students at a large public university, the authors analyze data with reflexive thematic analysis to reveal how academic pressures, social norms, policy clarity, and personal values interact to influence AI use and self-regulation. The work contributes a nuanced map of drivers (deadlines, grading emphasis, peer norms, and AI shame) and offers concrete recommendations: co-created, course- and department-specific AI policies; increased in-person assessments; and structured AI literacy initiatives, supported by system design that aligns with learning goals. The findings have practical significance for institutions, instructors, and tool designers seeking to integrate AI into learning while maintaining rigor, equity, and student learning outcomes in an AI-enabled educational environment.
Abstract
Higher education students are increasingly using generative AI in their academic work. However, existing institutional practices have not yet adapted to this shift. Through semi-structured interviews with 23 college students, our study examines the environmental and social factors that influence students' use of AI. Findings show that institutional pressure factors like deadlines, exam cycles, and grading lead students to engage with AI even when they think it undermines their learning. Social influences, particularly peer micro-communities, establish de-facto AI norms regardless of official AI policies. Campus-wide ``AI shame'' is prevalent, often pushing AI use underground. Current institutional AI policies are perceived as generic, inconsistent, and confusing, resulting in routine noncompliance. Additionally, students develop value-based self-regulation strategies, but environmental pressures create a gap between students' intentions and their behaviors. Our findings show student AI use to be a situated practice, and we discuss implications for institutions, instructors, and system tool designers to effectively support student learning with AI.