Co-CoT: A Prompt-Based Framework for Collaborative Chain-of-Thought Reasoning
This addresses the issue of reduced critical thinking and engagement for users interacting with AI systems, though it appears incremental as it builds on existing chain-of-thought methods.
The paper tackles the problem of diminishing reflective thinking and lack of transparency in AI-generated outputs by proposing an Interactive Chain-of-Thought Framework that makes model inference transparent, modular, and user-editable, enhancing explainability and engagement.
Due to the proliferation of short-form content and the rapid adoption of AI, opportunities for deep, reflective thinking have significantly diminished, undermining users' critical thinking and reducing engagement with the reasoning behind AI-generated outputs. To address this issue, we propose an Interactive Chain-of-Thought (CoT) Framework that enhances human-centered explainability and responsible AI usage by making the model's inference process transparent, modular, and user-editable. The framework decomposes reasoning into clearly defined blocks that users can inspect, modify, and re-execute, encouraging active cognitive engagement rather than passive consumption. It further integrates a lightweight edit-adaptation mechanism inspired by preference learning, allowing the system to align with diverse cognitive styles and user intentions. Ethical transparency is ensured through explicit metadata disclosure, built-in bias checkpoint functionality, and privacy-preserving safeguards. This work outlines the design principles and architecture necessary to promote critical engagement, responsible interaction, and inclusive adaptation in AI systems aimed at addressing complex societal challenges.