Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks
This research addresses the impact of explainable AI on human learning and collaboration, with implications for automated tutoring, but it is incremental as it compares specific explanation types without introducing new methods.
This study investigated how explainable AI agents, specifically computers and humanoid robots with partner-aware explanations, affect human behavior and performance in learning-by-doing tasks, finding that participants with computers improved task completion times, those with robots followed suggestions more but did not reduce timing, and unassisted participants showed better knowledge acquisition than those with XAI assistance.
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.