Over-reliance on Large Language Models in Higher Education: Ethical and Institutional Challenges in Responsible AI Integration

The integration of Large Language Models (LLMs) such as ChatGPT in higher education presents both transformative opportunities and critical pedagogical challenges. This study examines the motivations, behaviors, and contextual factors that contribute to students’ over-reliance on LLMs, with particular attention to cognitive, social, and institutional dynamics. Adopting a qualitative research design grounded in Root Cause Analysis (RCA), the study draws on guided interviews with undergraduate students across professional disciplines. Five interrelated themes emerged: LLMs as learning substitutes, time pressure and efficiency seeking, normalization through peer influence, ethical ambiguity and misuse, and institutional gaps in support and guidance. While LLMs enhance accessibility and efficiency, findings indicate that excessive reliance encourages cognitive offloading, weakens critical thinking, and reshapes students’ learning practices toward surface-level engagement. The study further reveals limited institutional preparedness, characterized by inadequate faculty development, minimal ethical training, and the absence of clear policies governing AI use. Peer norms and competitive academic cultures further reinforce reliance on LLMs as normalized practice. In response, the study proposes policy, pedagogical, and institutional reforms aimed at promoting ethical AI literacy, critical engagement, and equitable access. By foregrounding students’ lived experiences, this research contributes to the growing discourse on digital transformation in higher education and underscores the need to align LLM integration with responsible learning practices that strengthen intellectual autonomy and academic integrity.