ReflectiveGPT: A General-Purpose Multimodal Artificial Intelligence Framework for Reflective Reasoning, Self-Questioning, Cognitive Restructuring, and Transformative Understanding

Contemporary generative artificial intelligence systems have demonstrated substantial capabilities in information processing, reasoning, content generation, and problem solving. Nevertheless, an important challenge remains: the ability to fundamentally reconsider existing knowledge, question previously established conclusions, reconstruct conceptual frameworks, and transform the way a problem or phenomenon is understood. Although existing large language models can perform self-reflection, iterative refinement, and multi-step reasoning, these capabilities do not necessarily constitute a unified mechanism for sustained cognitive reconsideration and transformative understanding. Human intellectual development frequently involves more than acquiring additional information. It also requires revisiting previous beliefs, identifying limitations in established interpretations, recognizing contradictions, reconstructing mental representations, and transferring newly developed understanding to unfamiliar situations. Inspired by these cognitive processes, this paper proposes ReflectiveGPT, a conceptual framework for a general-purpose artificial intelligence system designed to support reflective reasoning and cognitive transformation. The proposed architecture integrates eight interconnected thinking mechanisms: reflective thinking, self-questioning, cognitive restructuring, paradigm shifting, dialectical thinking, abstract thinking, concrete thinking, and transfer thinking. These mechanisms collectively enable an AI system to examine existing knowledge, challenge its own conclusions, identify hidden assumptions, explore opposing perspectives, extract underlying principles, translate abstract understanding into concrete situations, and apply reconstructed knowledge to new problems. Unlike conventional response-centered systems that primarily optimize the generation of an appropriate answer, ReflectiveGPT treats understanding as a revisable computational structure that can be continuously examined and transformed. Importantly, the framework is designed to support multimodal cognition, allowing textual information to be integrated with visual observations, speech, audio, video, and other available modalities. This capability creates opportunities for reflection based not only on linguistic descriptions but also on observed situations, contextual evidence, and multimodal interactions. The paper presents the conceptual architecture, operational mechanisms, potential implementation strategies, general-purpose applications, and an empirical evaluation framework. No experimental performance or demonstrated superiority is claimed at this conceptual stage. The proposed framework establishes a foundation for investigating whether general-purpose multimodal AI can move beyond answer generation toward systematic self-examination, conceptual reconstruction, and transformative understanding.