AI-overdependence and human cognitive decline: Hazards, evidence, and mitigation strategies

The rapid diffusion of artificial intelligence (AI) dialogue systems and generative models across education, professional work, and healthcare brings substantial productivity gains but also raises growing concerns about overreliance, overdependence, and cognitive decline. Despite increasing scholarly attention, existing research remains fragmented across domains and disciplines, with no recent integrative review that systematically consolidates evidence on cognitive effects, associated hazards, and mitigation strategies across different domains. This fragmentation limits cumulative understanding of shared mechanisms, broader hazards, and effective responses, as well as their impacts on individuals’ learning, decision-making, professional competence, overall well-being, and wider societal outcomes. To address this gap, we present a cross-domain review of empirical and conceptual literature on AI-overdependence and its cognitive consequences, based on a systematic mapping of studies. The review focuses on two objectives: (1) synthesize available experimental and observational evidence on cognitive decline and task-specific deskilling, and (2) systematically organize theoretical arguments and emerging concerns where empirical evidence remains limited. In addition, the review explicitly identifies reported and hypothesized hazards of AI-overdependence and surveys proposed mitigation strategies across domains. To support integrative analysis, we introduce the P2BEAM taxonomy ( P sychological and Contextual Mechanisms, P opulation-Specific Effects, B roader Hazards and Impacts, E vidence for Cognitive Decline and Deskilling, A ffected Domains and M itigation Strategies). The taxonomy encapsulates the key themes emerging from the review. By consolidating the fragmented literature, distinguishing evidence from hypotheses, and systematically mapping hazards and mitigation approaches, this review provides transferable insights across domains and concludes with a prioritized cross-domain research agenda and practical recommendations for researchers, educators, policymakers, and system designers.