GENERATIVE AI IN HIGHER EDUCATION: PARADOXICAL ROLES IN SELF-REGULATED LEARNING AND ACADEMIC DEPENDENCY

Abstract

opportunities for self-regulation and risks of academic dependency. This narrative review aimed to synthesize how GenAI may support self-regulated learning (SRL), how intensive or uncritical use may promote cognitive offloading, and which contextual conditions appear to distinguish these outcomes. Ten peer-reviewed articles published between 2022 and 2026 were identified through Publish or Perish using Google Scholar and synthesized thematically. The evidence indicates that GenAI can support SRL by functioning as a learning tutor, assisting goal setting and planning, facilitating monitoring and self-evaluation, and improving learning efficiency. At the same time, overreliance on GenAI is associated in the reviewed literature with cognitive offloading, academic dependency, reduced cognitive effort, weaker critical evaluation, and diminished learner autonomy. The synthesis proposes four contextual boundary conditions - purpose of AI use, SRL capability, AI literacy, and intensity of use - that may shape whether GenAI acts primarily as a learning scaffold or as a substitute for cognitive engagement. The framework is conceptual and has not been empirically validated. Higher-education institutions should therefore combine GenAI access with pedagogical practices that strengthen planning, verification, reflection, critical thinking, and responsible AI literacy.