人は失敗すべき！説明可能な仮想・ロボットエージェントが体験学習に与える影響の探究
Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks
体験学習タスクにおいて、説明可能なAI（コンピュータまたはヒューマノイドロボット）が人間の行動と成績に与える影響を比較し、AIの説明が必ずしも学習を促進せず、自律的な失敗経験の方が知識獲得に優れることを示した研究。
著者: Marco Matarese, Francesco Rea, Katharina J. Rohlfing, Alessandra Sciutti
分類: cs.AI, cs.HC, cs.RO
原文アブストラクト
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.
