Cognitive rehabilitation, STEM skill acquisition, and coaching games such as chess often require tutoring decision-making strategies. The advancement of AI-driven tutoring systems for facilitating human learning requires an understanding of the impact of evaluative feedback on human decision-making and skill development. To this end, we conduct human experiments using Amazon Mechanical Turk to study the influence of evaluative feedback on human decision-making in sequential tasks. In these experiments, participants solve the Tower of Hanoi puzzle and receive AI-generated feedback while solving it. We examine how this feedback affects their learning and skill transfer to related tasks. We also explore various computational models to understand how people incorporate evaluative feedback into their decision-making processes.

翻译：认知康复、STEM技能习得以及象棋等教练游戏通常需要指导决策策略。为推动人类学习的AI驱动辅导系统的进步，需理解评价性反馈对人类决策和技能发展的影响。为此，我们通过亚马逊土耳其机器人开展人类实验，研究评价性反馈对序贯任务中人类决策的影响。实验中，参与者解决汉诺塔谜题，并在解题过程中接收AI生成的反馈。我们考察了这种反馈如何影响他们的学习以及向相关任务的技能迁移。此外，我们还探索了多种计算模型，以理解人们如何将评价性反馈纳入其决策过程。