When Do Learners Prefer AI or Human Feedback? Situational Predictors of Feedback Source Preference

Artificial intelligence (AI)–based feedback systems are increasingly used to support learning at scale, particularly in complex performance domains such as writing. While prior research has demonstrated that AI can generate feedback comparable in quality to human feedback, less is known about in which situations learners prefer AI feedback versus human feedback. Under the assumption that learners' preferences in specific feedback situations and their psychological affordances predict engagement with feedback and thereby its effectiveness, understanding these preferences and their determinants is critical for designing scalable feedback systems that learners sustainably engage with and benefit from. In this study, we investigated situation-specific psychological predictors of learners’ preferences for AI versus human feedback using a vignette-based within-subject design in a sample of N = 282 US-college students on Prolific. Participants evaluated six feedback scenarios that systematically varied in perceived effort, salience of motivational needs (autonomy vs. support), and anxiety. After each vignette, participants indicated their preference for receiving feedback from an AI system or a human instructor. Mixed-effects logistic regression models were used to analyze feedback source preferences to account for repeated measures within participants. Results showed that preferences for AI versus human feedback were attributable to the characteristics of the scenarios: when the situation suggested high prior task effort and a salient need for support, scenarios were linked to less AI instead of human feedback preference, whereas high-anxiety scenarios regarding performance evaluation were associated with a substantially higher likelihood of preferring AI feedback. These findings suggest that AI and human feedback may serve complementary roles depending on learners’ psychological needs. We discuss implications for the strategic deployment of AI feedback in hybrid and learning-at-scale educational environments.