Artificial intelligence dependency in college students: a critical narrative review

Abstract

The rapid diffusion of generative artificial intelligence (GenAI), particularly large language model (LLM) applications, has fundamentally reshaped university students’ learning, problem-solving, and socio-emotional practices. Alongside clear benefits in efficiency and personalized support, concerns are emerging about AI dependency, understood here as an emerging, non-diagnostic educational-psychological construct rather than a formal clinical disorder. This critical narrative review synthesizes evidence from the past decade on AI dependency among college students, focusing on conceptualizations, theoretical frameworks, prevalence and demographic variations, measurement tools, determinants, intervention strategies, and research gaps. Across studies, an “ask-AI-first” pattern appears increasingly common, although frequency of use should not be equated with maladaptive dependency. The central concern is not AI use itself but patterns of cognitive delegation, emotional reliance, and reduced self-regulation that may be associated with weaker independent engagement, academic-integrity risks, and distress when AI is unavailable. The literature also reveals substantial fragmentation in assessment, although several emerging tools demonstrate promising psychometric properties. Determinants appear multi-level, involving individual vulnerabilities, academic pressure, contextual norms, and technological affordances. Current recommendations emphasize promoting augmentation over automation, strengthening AI literacy and critical evaluation skills, redesigning assessments to preserve cognitive engagement, and implementing supportive interventions for at-risk students. Future research should prioritize longitudinal, cross-cultural, and multi-method designs to clarify developmental trajectories, thresholds for maladaptation, and evidence-based intervention pathways.