Artificial intelligence is increasingly encountered not as background software but as an interlocutor, adviser and institutional gatekeeper. Yet claims that an AI system is 'human-like' often collapse distinct comparison targets, measurements and inferences into a single label. This Review proposes a structured framework in which every resemblance claim specifies the human reference class, the AI system and version, the task and context, the property compared, the measurement relation, the perturbations considered, the uncertainty of the estimate and the inference that the evidence permits. Four recurring evidence families concern behavioural outputs, representational organization, process dynamics and neural or physiological correspondence. Perceived resemblance is treated separately as an observer attribution that can mediate trust, reliance, disclosure, delegation and moral concern. We distinguish exact from approximate resemblance, symmetric from directional measures, static from dynamical correspondence and observational from interventional evidence. Worked examples show how to code missing or conflicting indicators without forcing them into a single score. Convergence supports broader conclusions only when measures with different assumptions exclude distinct alternatives; otherwise, agreement may reflect shared data, stimuli, objectives or mappings. The framework is intended both for exploratory science and for application-matched evaluation, while preserving strict limits on claims about cognition and consciousness.
Peng Wang, E. Law, Li-Ye Zou et al.· Physics of Life Reviews· 0 citations
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.
Peng Wang, Olga Viberg, E. Law et al.· Educational Psychology Revie...· 2 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
