Review of: "Rethink Your Mental Model in the Age of Generative AI: A Triadic Framework for Human-AI Collaboration"

This manuscript offers an ambitious, carefully researched, and timely theoretical synthesis addressing one of the most pressing challenges in the era of generative AI: how humans should conceptualize, collaborate with, and regulate their own cognition when working with probabilistic, opaque, and rapidly evolving AI systems.The proposed Triadic Framework-integrating system-level properties, collaboration practices, and metacognitive re ection-provides a coherent and analytically useful lens for understanding why miscalibration, overtrust, and performance paradoxes persist despite technical advances.Overall, the paper represents a strong conceptual contribution and is suitable for publication following minor re nements focused primarily on positioning and theoretical integration. StrengthsThe manuscript's primary strength lies in its depth and breadth of synthesis.The authors demonstrate impressive command of recent empirical and conceptual literature across AI research, HCI, organizational studies, cognitive science, and ethics.The diagnosis of "jagged intelligence" and capability drift is persuasive and well supported, and the framing of AI as operating at a pre-cognitive "System 0" level is particularly compelling in explaining why traditional explainability and training interventions often fail.The Triadic Framework is clearly articulated and logically structured.By distinguishing the System Layer, Collaboration Layer, and Metacognitive Layer, the authors avoid reductionist explanations and show convincingly that failures in human-AI collaboration rarely originate from a single source.The seven propositions derived from this framework are concrete, actionable, and well grounded in the Qeios qeios.com