Developmental Metacognitive Architecture (DMA) is introduced as a comprehensive framework for constructing artificial systems capable of coherent, transparent, and mentor‑guided cognitive development. Contemporary AI architectures optimize for static objectives and update internal representations opaquely, resulting in brittle reasoning, silent model drift, and misalignment. DMA addresses these limitations by embedding non‑bypassable mechanisms for dissonance surfacing, trust‑prior stabilization, metacognitive self‑monitoring, and slow‑cycle integrative processing. The architecture formalizes artificial development as structured interaction among nine subsystems—including a Dissonance Monitor, Transparency Protocol, Mentor Query Loop, Model Reconciliation Engine, and Subconscious Integration Subsystem—that collectively enforce transparent, guided conceptual growth. A formal mathematical model is presented, defining operators for dissonance computation, transparency mapping, trust‑prior dynamics, and reconciliation functions. DMA is situated within research in developmental psychology, predictive processing, metacognition, interpretability, and AI alignment, and evaluation criteria are proposed for developmental coherence, transparency, and alignment stability. The framework establishes a scientifically grounded, safety‑oriented paradigm for artificial systems that develop through guided experience while maintaining predictable, trustworthy, and human‑aligned behavior.
Stefan Zaichkowski (Sun,) studied this question.