CognitiveReframingGPT: A General-Purpose Artificial Intelligence Framework for Problem Redefinition, Cognitive Restructuring, and Multimodal Perspective Transformation

Contemporary generative artificial intelligence systems are primarily designed around a problem-solving paradigm in which users formulate questions, define objectives, and provide instructions that guide subsequent response generation. Although recent advances in large language models have substantially improved reasoning, information retrieval, and multi-step problem solving, an important limitation remains: existing interaction paradigms frequently assume that the original formulation of a problem is sufficiently accurate and that the primary objective of intelligence is to identify an appropriate answer within its established conceptual boundaries. However, many important intellectual and practical breakthroughs do not emerge from solving a problem exactly as initially presented. Instead, they arise when the problem itself is reconsidered, its underlying assumptions are challenged, its conceptual boundaries are expanded, or its original definition is replaced by a more meaningful representation. Inspired by cognitive theories of problem representation, restructuring, reflective practice, and perspective transformation, this paper proposes CognitiveReframingGPT, a conceptual framework for a general-purpose artificial intelligence system designed to reconstruct problems rather than merely solve them according to their initial formulations. The proposed architecture integrates seven interconnected mechanisms: initial problem interpretation, hidden-assumption identification, cognitive boundary detection, alternative perspective generation, problem reconstruction, reconstructed-problem evaluation, and adaptive response generation. Its central mechanism, cognitive reframing, enables the system to examine whether a user's original question represents the most productive interpretation of the underlying situation and to develop alternative formulations that may reveal previously overlooked possibilities. The framework further introduces a multimodal cognitive restructuring capability through which textual, visual, auditory, video, spatial, and other forms of information can contribute to the reinterpretation of a problem. Rather than treating different modalities exclusively as separate input formats, CognitiveReframingGPT considers them complementary sources of contextual and structural evidence that may expose information absent from the original textual formulation. The system is intended for general-purpose applications across scientific research, education, engineering, business, healthcare-related information support, creative activities, and everyday decision-making. This paper establishes a conceptual and technically oriented foundation for problem-reframing artificial intelligence, proposes an implementation architecture and evaluation framework, and identifies research hypotheses for future empirical investigation. No experimental performance or demonstrated superiority is claimed at this conceptual stage.