解释性AI助手机器人能影响学习行为，但自主学习效果更好
Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks
- 对比了不同解释方式对人类学习的影响
- 机器人助手提升服从率，但未加快任务速度
- 无辅助自主学习者知识掌握更优，适合教学设计参考
与人工智能（AI）代理协作决策带来机遇与挑战。尽管人机协同表现常优于个体，但此类技术对人类行为的影响仍不明确，尤其是在AI能提供可解释建议的情况下。本研究比较了经典解释与伙伴感知解释对学习型任务中人类行为和表现的影响。三组参与者分别与计算机、人形机器人交互，或独立完成任务。结果显示，伙伴感知解释对参与者的影响因代理类型而异：与计算机交互时，参与者任务完成时间缩短；与人形机器人交互时，更倾向于采纳其建议，但任务时间未减少。有趣的是，自主完成任务的参与者在知识获取上优于接受可解释AI辅助者。这些发现引发深层思考，对自动辅导和人机协作具有重要启示。
原文摘要 · Abstract (English)
Collaborative decision-making with artificial intelligence (AI) agents presents opportunities and challenges. While human-AI performance often surpasses that of individuals, the impact of such technology on human behavior remains insufficiently understood, primarily when AI agents can provide justifiable explanations for their suggestions. This study compares the effects of classic vs. partner-aware explanations on human behavior and performance during a learning-by-doing task. Three participant groups were involved: one interacting with a computer, another with a humanoid robot, and a third one without assistance. Results indicated that partner-aware explanations influenced participants differently based on the type of artificial agents involved. With the computer, participants enhanced their task completion times. At the same time, those interacting with the humanoid robot were more inclined to follow its suggestions, although they did not reduce their timing. Interestingly, participants autonomously performing the learning-by-doing task demonstrated superior knowledge acquisition than those assisted by explainable AI (XAI). These findings raise profound questions and have significant implications for automated tutoring and human-AI collaboration.
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