同种反馈，不同来源：AI与人类反馈的归因和可信度如何影响学习者在计算教育中的行为
Same Feedback, Different Source: How AI vs. Human Feedback Attribution and Credibility Shape Learner Behavior in Computing Education
AI总结：
研究探讨了AI与人类反馈归因对学习者行为的影响，发现归因和交付时间对不同结果有不同影响，透明的AI归因可能在人类不可信时是更安全的选择。
摘要：
随着AI系统在教学中的角色日益增强，一个基本问题浮现：学习者认为对方是谁是否重要？本研究通过三条件实验（N=148）探讨了这一问题，参与者完成创意编码教程并收到由同一大语言模型生成的反馈，归因于AI系统（即时或延迟交付）或人类助教（匹配延迟交付）。此三条件设计分离了归因效应与交付时间的混淆因素，此前研究未控制此变量。归因和时间对不同结果有不同影响：认为人类归因的参与者比收到等时AI归因反馈的参与者花费更多时间（d=0.61，p=0.013，未校正），而交付延迟独立增加输出复杂性而不影响时间指标。探索性分析显示，46%的人类归因组参与者不相信归因，这些参与者的表现比接受透明AI反馈的参与者更差（代码复杂度d=0.77，p=0.003；时间d=0.70，p=0.007）。这些发现表明，相信的人类存在可能具有激励价值，但这种价值取决于可信度。对于计算教育者，透明的AI归因可能在人类不可信的场合下是更安全的默认选择。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
