When learners receive feedback, what they believe about its source may shape how they engage with it. As AI is used alongside human instructors, understanding these attribution effects is essential for designing effective hybrid AI-human educational systems. We designed a creative coding interface that isolates source attribution while controlling for content: all participants receive identical LLM-generated feedback, but half see it attributed to AI and half to a human teaching assistant (TA). We found two key results. First, perceived feedback source affected engagement: learners in the TA condition spent significantly more time and effort (d = 0.88-1.56) despite receiving identical feedback. Second, perceptions differed: AI-attributed feedback ratings were predicted by prior trust in AI (r = 0.85), while TA-attributed ratings were predicted by perceived genuineness (r = 0.65). These findings suggest that feedback source shapes both engagement and evaluation, with implications for hybrid educational system design.

翻译：当学习者接收反馈时，他们对其来源的认知可能影响其参与方式。随着人工智能与人类教师协同应用于教育场景，理解这种归因效应对于设计有效的人机协同教育系统至关重要。我们设计了一个创意编程界面，在控制反馈内容一致性的前提下分离来源归因：所有参与者接收完全相同的LLM生成反馈，但其中一半被告知反馈来自AI，另一半则被告知来自人类助教。研究发现两个关键结论：首先，感知的反馈来源显著影响参与度——尽管接收相同反馈，助教组学习者在任务中投入的时间与精力显著更高（效应量d = 0.88-1.56）；其次，评价机制存在差异：AI组反馈评分与对AI的事先信任度高度相关（r = 0.85），而助教组评分则主要受感知真诚度影响（r = 0.65）。这些发现表明反馈来源同时塑造学习者的参与行为与评价标准，为人机混合教育系统的设计提供了重要启示。