新颖性
机器人
心理学
感知
数学教育
知识获取
机器人学
人工智能
计算机科学
社会心理学
神经科学
作者
Liuqing Chen,Yu Cai,Yuyang Fang,Ziqi Yang,Duowei Xia,Jiaxiang You,Shuhong Xiao,Yaxuan Song,Zhan Liang-tong,Juanjuan Chen,Lingyun Sun
出处
期刊:Science robotics
[American Association for the Advancement of Science (AAAS)]
日期:2025-09-10
卷期号:10 (106): eadu5257-eadu5257
标识
DOI:10.1126/scirobotics.adu5257
摘要
According to productive failure (PF) theory, experiencing failure during problem-solving can enhance students’ knowledge acquisition in subsequent instruction. However, challenging students with problems beyond their current capabilities may strain their skills, prior knowledge, and emotional well-being. To address this, we designed a social robot–assisted teaching activity in which students observed a robot’s unsuccessful problem-solving attempts, offering a PF-like preparatory effect without requiring direct failure. We conducted two classroom-based studies in a middle school setting to evaluate the method’s effectiveness. In study 1 ( N = 135), we compared three instructional methods—observing robot failure (RF), individual problem-solving failure, and direct instruction—in an eighth-grade mathematics lesson. Students in the RF condition showed the greatest gains in conceptual understanding and reported lower social pressure, although no significant differences were found in procedural knowledge or knowledge transfer. Follow-up study 2 ( N = 110) further validated the method’s effectiveness in supporting knowledge acquisition after a 2-week robot-involved adaptation phase, when the novelty effect had largely subsided. Students confirmed their perception of the robot as a peer, and they offered positive evaluations of its intelligence and neutral views of its anthropomorphism. Our findings suggest that observing the robot’s failure has a comparable, or even greater, effect on knowledge acquisition than experiencing failure firsthand. These results underscore the value of social robots as peers in science, technology, engineering, and mathematics education and highlight the potential of integrating robotics with evidence-based teaching strategies to enhance learning outcomes.
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