AI社交助手学会自我解释，提升学习社区透明度与信任。
Self-Explanation in Social AI Agents
- 用自模型+思维链反思生成自我解释
- 在真实课堂部署中验证了解释的完整性和正确性
- 适合教育AI、可信智能系统研究者参考
社交AI代理与社区成员互动，从而改变社区行为。例如，在在线学习中，AI社交助手机能连接学习者并增强社交互动。此类代理需具备自我解释能力以提升学习者的透明度与信任感。本文提出一种基于自模型内省的自我解释方法：自模型以功能形式描述代理如何运用知识完成任务；通过思维链（Chain of Thought）反思自模型，并利用ChatGPT生成解释。我们评估了该方法在完整性与正确性方面的表现，并在真实课堂中进行了部署验证。
原文摘要 · Abstract (English)
Social AI agents interact with members of a community, thereby changing the behavior of the community. For example, in online learning, an AI social assistant may connect learners and thereby enhance social interaction. These social AI assistants too need to explain themselves in order to enhance transparency and trust with the learners. We present a method of self-explanation that uses introspection over a self-model of an AI social assistant. The self-model is captured as a functional model that specifies how the methods of the agent use knowledge to achieve its tasks. The process of generating self-explanations uses Chain of Thought to reflect on the self-model and ChatGPT to provide explanations about its functioning. We evaluate the self-explanation of the AI social assistant for completeness and correctness. We also report on its deployment in a live class.
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