AI and the psychology of educational disruption: Historical patterns and cognitive implications

Abstract

Artificial intelligence (AI) and, concomitantly, large language models (LLMs), emerge as an unprecedentedly disruptive force in education. We contend that AI is not just a technological disruption but also a psychological inflection point that reconfigures the evolving contours of cognition, motivation, and agency under technological pressure. Using Disruptive Innovation Theory (DIT), the Technological Pedagogical Content Knowledge (TPACK) framework, and Postdigital Education Theory (PET), AI is located in a series of histories of educational disruptions (printing, radio, Internet, and mobile / cloud). These lenses highlight recurrent psychological themes that transcend technological epochs: novel literacies, evolving learner motivation/autonomy, rising human cognitive load, and the lag in institutional adaptation. Conceptual synthesis demonstrates a crystallization of psychological effects—bifurcated epistemic fluidity, blurred authorship, and identity negotiation/formation—under AI. Theoretical triangulation of the DIT, TPACK, and PET frameworks articulates the entangled, ripple effects of AI-triggered changes transcending institutional, pedagogical, and psychological levels. To support the enactment of these ideas, we delineate staged AI-responsive literacies (autonomy, vigilance, regulation, ethics) and propose operational strategic supports for learners, educators, and institutions. We conclude by reviewing the limitations of the conceptualization and charting future research directions in empirical classrooms, identity shifts, and equity in institutional responses. Overall, we argue that AI as a “disruption” must be repositioned not only as a technical intervention but also as a psychological and educational imperative to nurture autonomy, metacognition, and equity.