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Background <p>While AI-generated feedback has been promoted as a supplement to teacher feedback, recent empirical studies have consistently revealed that students prefer teacher feedback to AI-generated feedback. It remains unclear whether such preferences reflect the inherent quality of the feedback or stem from psychological biases toward the feedback source.</p> Method <p>This study investigates the existence of psychological biases towards teacher and AI-generated feedback. An experiment was conducted with 52 advanced learners of Chinese as a second language (CSL). The subjects were randomly divided into two groups: AI-label group and teacher-label group. All participants received identical AI-generated feedback on an essay, but were led to believe it came from either an AI system (AI-label group) or an experienced teacher (teacher-label group). Data included participants’ perceptions of the feedback measured across seven psychological dimensions (coverage, accuracy, elaboration, utility, cost, interest, and intention), as well as their subsequent textual revisions coded by type and frequency.</p> Results <p>Participants in the teacher-label group reported consistently more positive perceptions of the feedback across all dimensions, although these differences did not reach statistical significance. However, the AI-label group made significantly more textual revisions, particularly replacements, than the teacher-label group (<i>p</i> = .017). This reveals a dissociation between cognitive appraisal of feedback and behavioral engagement with it.</p> Conclusion <p>These findings provide empirical evidence of a psychological bias against AI-generated feedback, wherein the same feedback is perceived less favorably when attributed to AI. However, this negative bias does not translate into reduced behavioral engagement; instead, learners interact more actively with AI-attributed feedback, potentially due to reduced social-evaluative concerns and enhanced autonomy. The study contributes to the psychology of human-AI interaction in educational contexts and highlights the need to consider both cognitive and socio-affective mechanisms in understanding feedback engagement.</p>

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Students’ psychological biases towards teacher and AI-generated feedback: an experimental study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月22日

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  • Haiyan Yu

摘要

Background

While AI-generated feedback has been promoted as a supplement to teacher feedback, recent empirical studies have consistently revealed that students prefer teacher feedback to AI-generated feedback. It remains unclear whether such preferences reflect the inherent quality of the feedback or stem from psychological biases toward the feedback source.

Method

This study investigates the existence of psychological biases towards teacher and AI-generated feedback. An experiment was conducted with 52 advanced learners of Chinese as a second language (CSL). The subjects were randomly divided into two groups: AI-label group and teacher-label group. All participants received identical AI-generated feedback on an essay, but were led to believe it came from either an AI system (AI-label group) or an experienced teacher (teacher-label group). Data included participants’ perceptions of the feedback measured across seven psychological dimensions (coverage, accuracy, elaboration, utility, cost, interest, and intention), as well as their subsequent textual revisions coded by type and frequency.

Results

Participants in the teacher-label group reported consistently more positive perceptions of the feedback across all dimensions, although these differences did not reach statistical significance. However, the AI-label group made significantly more textual revisions, particularly replacements, than the teacher-label group (p = .017). This reveals a dissociation between cognitive appraisal of feedback and behavioral engagement with it.

Conclusion

These findings provide empirical evidence of a psychological bias against AI-generated feedback, wherein the same feedback is perceived less favorably when attributed to AI. However, this negative bias does not translate into reduced behavioral engagement; instead, learners interact more actively with AI-attributed feedback, potentially due to reduced social-evaluative concerns and enhanced autonomy. The study contributes to the psychology of human-AI interaction in educational contexts and highlights the need to consider both cognitive and socio-affective mechanisms in understanding feedback engagement.