MetacognitiveGPT: A General-Purpose Artificial Intelligence Framework for Self-Monitoring, Self-Evaluation, and Adaptive Regulation of Cognitive Processes

Contemporary generative artificial intelligence systems have demonstrated increasingly sophisticated capabilities in reasoning, problem solving, knowledge integration, and multimodal information processing. Nevertheless, an important challenge remains: although an AI system can generate an answer and subsequently revise it, its ability to systematically monitor, evaluate, and regulate the cognitive processes responsible for generating that answer remains an underdeveloped dimension of general-purpose artificial intelligence. In human cognition, metacognition refers to the capacity to think about one's own thinking, monitor ongoing cognitive activities, evaluate the quality of understanding and reasoning, and regulate cognitive strategies according to the requirements of a particular task. Drawing inspiration from established theories of metacognition and cognitive regulation, this paper proposes MetacognitiveGPT, a conceptual framework for a general-purpose artificial intelligence system designed to incorporate metacognitive capabilities into computational reasoning. Unlike conventional AI systems that primarily concentrate on generating an appropriate response, MetacognitiveGPT introduces a higher-level metacognitive architecture that continuously examines the status, reliability, limitations, and strategic direction of its underlying cognitive operations. The proposed framework integrates six interconnected mechanisms: cognitive process representation, metacognitive monitoring, metacognitive evaluation, adaptive cognitive regulation, uncertainty awareness, and recursive self-reflection. A central principle is that the system should not only determine whether an answer appears satisfactory but also examine how its current reasoning approach is progressing, whether its assumptions remain appropriate, which uncertainties remain unresolved, and whether an alternative cognitive strategy would produce a more reliable result. The framework further introduces a hierarchical relationship between object-level cognition, which performs the primary reasoning task, and meta-level cognition, which monitors and regulates the reasoning process. Importantly, MetacognitiveGPT is designed to support multimodal cognition, allowing metacognitive monitoring and regulation to operate across textual, visual, auditory, video, and other supported information modalities. For example, the system could evaluate whether its interpretation of an image is sufficiently supported, identify ambiguity in spoken information, reconsider its interpretation of a video sequence, and revise its cross-modal reasoning strategy when inconsistencies emerge. The proposed architecture is intended for general-purpose applications, including scientific research, education, engineering, business, decision-making, and everyday problem solving. This paper establishes a conceptual and technically oriented foundation for metacognitive artificial intelligence and proposes implementation mechanisms, evaluation dimensions, research hypotheses, and future research directions. No experimental performance or demonstrated superiority is claimed at this conceptual stage.