Co-CoT: A Prompt-Based Framework for Collaborative Chain-of-Thought Reasoning

• URL: http://arxiv.org/abs/2504.17091v1
• Date: Wed, 23 Apr 2025 20:48:09 GMT
• Title: Co-CoT: A Prompt-Based Framework for Collaborative Chain-of-Thought Reasoning
• Authors: Seunghyun Yoo,
• Abstract summary: We propose an Interactive Chain-of-Thought (CoT) Framework that enhances human-centered explainability and responsible AI usage.<n>The framework decomposes reasoning into clearly defined blocks that users can inspect, modify, and re-execute.<n>Ethical transparency is ensured through explicit metadata disclosure, built-in bias checkpoint functionality, and privacy-preserving safeguards.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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