The adaptive learning taxonomy for responsible large language model integration in higher education.

UNLABELLED: Large Language Models (LLMs) are being rapidly deployed in higher education, yet institutions lack integrated, theoretically grounded frameworks to guide responsible adoption. While meta-analytic evidence reports large positive effects on academic performance (g = 0.867; Wang and Fan 2025), emerging research reveals a "cognitive paradox": performance gains may coincide with diminished metacognitive accuracy and self-regulation-a gap no existing framework comprehensively addresses across adaptation design, data governance, pedagogical targeting, and learner agency simultaneously.

METHODOLOGY: Through a targeted narrative synthesis of 35 empirical studies published between January 2024 and February 2025, supplemented by selected post-window studies incorporated during peer review, and grounded in four learning theories-Connectivism, Distributed Cognition, Cognitive Load Theory, and Self-Regulated Learning-this paper develops the Adaptive Learning Taxonomy for Educational Decision-Making (ALT-ED). The framework structures institutional decision-making across four operational dimensions: Adaptation Trigger, Data Granularity, Pedagogical Locus, and Agency.

KEY FINDINGS: Three central findings emerge: (1) the cognitive paradox requires shifting the pedagogical locus from cognitive scaffolding to metacognitive development; (2) high-resolution data collection amplifies algorithmic bias, supporting a default of progressive data minimisation; and (3) the policy-practice chasm demands transparent, auditable design frameworks rather than prohibition-based approaches.

RECOMMENDATIONS: Institutions should prioritise a metacognitive pedagogical locus to foster "learning to learn" skills, employ progressive data granularity to balance personalisation with student privacy, and default to learner-negotiated agency as a structural safeguard against algorithmic determinism.

LIMITATIONS: The framework has not yet been empirically validated through prospective institutional implementation. Additional limitations include reliance on predominantly short-term quasi-experimental evidence, the absence of standardised metrics for real-time metacognitive efficiency, and a literature base that is overwhelmingly English-language and Western-centric. Future research should focus on cross-institutional pilot validations, longitudinal studies of cognitive entrainment, and the development of standardised instruments for measuring metacognitive efficiency in AI-augmented environments.