Who Gives Feedback Matters: Student Biases Towards Human and AI-Generated Formative Feedback

Abstract

ABSTRACT Background Feedback is essential for learning, helping individuals understand and improve their performance. However, providing timely, personalised feedback in higher education is challenging. Generative AI offers a scalable solution, yet little is known about students' biases towards AI-generated feedback. Objectives This study aims to investigate how the identity of the feedback provider (human vs. AI) affects students' perceptions of feedback quality and credibility. Methods The study involved 472 students across diverse academic programmes and levels in authentic educational environments and employed a within-subject experimental design with a priming effect. A mixed-methods approach combined quantitative analysis of feedback evaluations with qualitative insights into students' perceptions to deepen understanding of the observed biases. Results and Conclusions Students perceived AI as a significantly less credible feedback provider and tended to associate lower feedback quality with AI. Disclosing the feedback provider's identity led to decreased evaluations of AI-generated feedback and an increased preference for human-crafted feedback. These patterns were consistent across academic levels, genders, and fields of study. These insights highlight the need for targeted interventions, such as improving AI literacy and building human-in-the-loop systems, to mitigate biases and enhance the effectiveness of AI in educational feedback systems.