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.

翻译：与人工智能（AI）代理进行协同决策既带来机遇也面临挑战。尽管人机协作的表现常优于个体单独行动，但此类技术对人类行为的影响仍未得到充分理解，尤其是在AI代理能够为其建议提供合理解释的情况下。本研究比较了经典解释与伙伴感知解释在实践学习任务中对人类行为与表现的影响。研究涉及三组参与者：一组与计算机交互，一组与人形机器人交互，第三组则在无辅助条件下进行。结果表明，伙伴感知解释对参与者的影响因所涉及的人工代理类型而异。与计算机交互的参与者提升了任务完成效率，而与人形机器人交互的参与者则更倾向于遵循其建议，尽管并未缩短任务用时。值得注意的是，自主完成实践学习任务的参与者比那些获得可解释AI（XAI）辅助的参与者表现出更优的知识掌握水平。这些发现提出了深刻的问题，并对自动化教学及人机协作领域具有重要启示。