As AI systems increasingly take on instructional roles - providing feedback, guiding practice, evaluating work - a fundamental question emerges: does it matter to learners who they believe is on the other side? We investigated this using a three-condition experiment (N=148) in which participants completed a creative coding tutorial and received feedback generated by the same large language model, attributed to either an AI system (with instant or delayed delivery) or a human teaching assistant (with matched delayed delivery). This three-condition design separates the effect of source attribution from the confound of delivery timing, which prior studies have not controlled. Source attribution and timing had distinct effects on different outcomes: participants who believed the human attribution spent more time on task than those receiving equivalently timed AI-attributed feedback (d=0.61, p=.013, uncorrected), while the delivery delay independently increased output complexity without affecting time measures. An exploratory analysis revealed that 46% of participants in the human-attributed condition did not believe the attribution, and these participants showed worse outcomes than those receiving transparent AI feedback (code complexity d=0.77, p=.003; time on task d=0.70, p=.007). These findings suggest that believed human presence may carry motivational value, but that this value depends on credibility. For computing educators, transparent AI attribution may be the lower-risk default in contexts where human attribution would not be credible.

翻译：随着AI系统越来越多地承担教学角色——提供反馈、指导实践、评估作业——一个基本问题浮现出来:学习者认为另一方是谁,对他们而言重要吗?我们通过一项三条件实验(N=148)对此进行了研究,参与者完成创意编程教程并收到由相同大语言模型生成的反馈,该反馈被归因于AI系统(即时或延迟交付)或人类助教(匹配延迟交付)。该三条件设计将来源归因效应与交付时间这一混淆因素分离,而以往研究未对此加以控制。来源归因和时间对不同结果有不同影响:相信人类归因的参与者在任务上花费的时间多于接收等时AI归因反馈的参与者(d=0.61,p=.013,未校正),而交付延迟独立增加了输出复杂度但不影响时间指标。一项探索性分析显示,人类归因条件下46%的参与者不相信该归因,这些参与者表现劣于接收透明AI反馈的参与者(代码复杂度d=0.77,p=.003;任务时间d=0.70,p=.007)。这些发现表明,相信的人类存在可能具有激励价值,但该价值取决于可信度。对计算教育工作者而言,在人类归因不可信的情境下,透明AI归因可能是风险更低的默认选择。