When Can AI Models Explain Learning? Validity Criteria for AI as Cognitive Models in Education

Abstract

Traditional verbal theories in educational psychology often remain underspecified at the mechanistic level. While they offer rich descriptive constructs and conceptual insights, they provide limited accounts of how learning processes unfold dynamically and causally. Here, “verbal” denotes theories stated in prose and qualitative relations rather than as formal, computational process models. This lack of mechanistic precision constrains rigorous theory testing, limits integration across levels of analysis, and reduces the potential to design interventions grounded in explanatory understanding of learning processes. In this Review, we synthesize an emerging paradigm that treats artificial intelligence (AI) systems not merely as predictive tools or instructional technologies, but as cognitive models of learners, explicit, runnable instantiations of theoretical assumptions about cognition and learning. Our central question is not whether AI systems can serve as cognitive models, but when they should be allowed to count as such. We therefore organize the Review around explicit validity criteria, theoretical grounding, construct validity, mechanistic transparency, alignment with human learning trajectories, error-signature matching, causal-intervention tests, ecological validity, and instructional usefulness, that an AI system must satisfy before its cognitive-model status is granted rather than assumed. We examine how major families of AI models, including neural networks, reinforcement learning agents, cognitive architectures, and large language models, have been used to operationalize core educational constructs such as memory, strategy use, motivation, self-regulation, and social learning. Across these approaches, we highlight how mechanistic transparency, interpretability, and alignment with human learning trajectories and error patterns are essential for explanatory validity. We further discuss methodological tools, such as representation analysis, ablation, and trajectory-level comparison, that enable causal inference about learning mechanisms within models. Finally, we outline key challenges and future directions, including construct validity, ecological realism, individual differences, and ethical accountability. By positioning AI as a theoretical instrument rather than solely an engineering solution, this Review argues that AI-based cognitive models can, when they satisfy these criteria, help transform abstract learning theories into precise, testable, and educationally actionable accounts of how students learn.