Deconstructing the Dual Black Box:A Plug-and-Play Cognitive Framework for Human-AI Collaborative Enhancement and Its Implications for AI Governance

Currently, there exists a fundamental divide between the"cognitive black box"(implicit intuition) of human experts and the"computational black box"(untrustworthy decision-making) of artificial intelligence (AI). This paper proposes a new paradigm of"human-AI collaborative cognitive enhancement,"aiming to transform the dual black boxes into a composable, auditable, and extensible"functional white-box"system through structured"meta-interaction."The core breakthrough lies in the"plug-and-play cognitive framework"--a computable knowledge package that can be extracted from expert dialogues and loaded into the Recursive Adversarial Meta-Thinking Network (RAMTN). This enables expert thinking, such as medical diagnostic logic and teaching intuition, to be converted into reusable and scalable public assets, realizing a paradigm shift from"AI as a tool"to"AI as a thinking partner."This work not only provides the first engineering proof for"cognitive equity"but also opens up a new path for AI governance: constructing a verifiable and intervenable governance paradigm through"transparency of interaction protocols"rather than prying into the internal mechanisms of models. The framework is open-sourced to promote technology for good and cognitive inclusion. This paper is an independent exploratory research conducted by the author. All content presented, including the theoretical framework (RAMTN), methodology (meta-interaction), system implementation, and case validation, constitutes the author's individual research achievements.