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

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.

Predicting challenge moments from students' discourse: A comparison of GPT-4 to two traditional natural language processing approaches

Effective collaboration requires groups to strategically regulate themselves to overcome challenges. Research has shown that groups may fail to regulate due to differences in members' perceptions of challenges which may benefit from external support. In this study, we investigated the potential of leveraging three distinct natural language processing models: an expert knowledge rule-based model, a supervised machine learning (ML) model and a Large Language model (LLM), in challenge detection and challenge dimension identification (cognitive, metacognitive, emotional and technical/other challenges) from student discourse, was investigated. The results show that the supervised ML and the LLM approaches performed considerably well in both tasks, in contrast to the rule-based approach, whose efficacy heavily relies on the engineered features by experts. The paper provides an extensive discussion of the three approaches' performance for automated detection and support of students' challenge moments in collaborative learning activities. It argues that, although LLMs provide many advantages, they are unlikely to be the panacea to issues of the detection and feedback provision of socially shared regulation of learning due to their lack of reliability, as well as issues of validity evaluation, privacy and confabulation. We conclude the paper with a discussion on additional considerations, including model transparency to explore feasible and meaningful analytical feedback for students and educators using LLMs.