Evaluating Trust in AI, Human, and Co-produced Feedback Among Undergraduate Students

• URL: http://arxiv.org/abs/2504.10961v1
• Date: Tue, 15 Apr 2025 08:06:36 GMT
• Title: Evaluating Trust in AI, Human, and Co-produced Feedback Among Undergraduate Students
• Authors: Audrey Zhang, Yifei Gao, Wannapon Suraworachet, Tanya Nazaretsky, Mutlu Cukurova,
• Abstract summary: Students generally preferred AI and co-produced feedback over human feedback in terms of perceived usefulness and objectivity.<n>Male students consistently rated all feedback types as less valuable than their female and non-binary counterparts.<n>These insights inform evidence-based guidelines for integrating AI into higher education feedback systems.
• Score: 2.935250567679577
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As generative AI transforms educational feedback practices, understanding students' perceptions of different feedback providers becomes crucial for effective implementation. This study addresses a critical gap by comparing undergraduate students' trust in AI-generated, human-created, and human-AI co-produced feedback, informing how institutions can adapt feedback practices in this new era. Through a within-subject experiment with 91 participants, we investigated factors predicting students' ability to distinguish between feedback types, perception of feedback quality, and potential biases to AI involvement. Findings revealed that students generally preferred AI and co-produced feedback over human feedback in terms of perceived usefulness and objectivity. Only AI feedback suffered a decline in perceived genuineness when feedback sources were revealed, while co-produced feedback maintained its positive perception. Educational AI experience improved students' ability to identify AI feedback and increased their trust in all feedback types, while general AI experience decreased perceived usefulness and credibility. Male students consistently rated all feedback types as less valuable than their female and non-binary counterparts. These insights inform evidence-based guidelines for integrating AI into higher education feedback systems while addressing trust concerns and fostering AI literacy among students.

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