The dependency trap: a systematic review of the contributing factors and consequences of student over-reliance on generative AI in higher education

Student over-reliance on generative artificial intelligence (GenAI) poses a critical challenge to higher education, yet existing literature remains fragmented and has not fully explained how reported contributing factors and associated consequences may be conceptually connected. To address this gap, the current systematic review synthesized empirical studies published between 2022 and 2026 ( n = 54) to examine the reported contributing factors and associated negative consequences of student over-reliance on GenAI, and to conceptually synthesize how these negative consequences may, under certain conditions, feed back into the conditions related to over-reliance. The findings identify four major categories of contributing factors, including psychological factors, capability gaps, environmental stress, and technological pull, together with four major consequence domains, namely cognitive impairment, learning efficacy decline, academic integrity risks, and psychosocial impairment. From the perspective of Lewin’s Field Theory, student over-reliance on GenAI can be understood as emerging from person–environment interaction. Building on this foundation, a system dynamics perspective provides a conceptual lens for interpreting student over-reliance on GenAI as a potentially self-reinforcing dependency trap, in which associated negative consequences may help sustain the conditions under which over-reliance becomes more likely. Finally, we argue that higher education responses to GenAI should shift from restrictive control toward active critical engagement through a multi-dimensional strategy that aligns pedagogical innovation, process-oriented assessment, and holistic psychosocial support.