In this study, we aim to examine how students’ awareness of the feedback provider’s identity might influence their evaluation of feedback content, particularly in the context of algorithm aversion and preference for human expertise. With these goals in mind, the study seeks to address the following research questions:

1. First, can students distinguish between AI-generated and humancreated feedback (simplified Turing Test), and what factors influence their ability to make this distinction (RQ1)?
2. Second, how do students’ perceptions of the same feedback content change after revealing the feedback provider’s identity (RQ2)?
3. And third, do students hold a negative bias towards AI as a feedback provider (RQ3)?

To summarize, our study analyzing 457 student responses in actual learning contexts gave us a detailed and accurate understanding of student responses to human and AI-generated feedback, a depth that synthetic scenarios might not achieve. We found that students’ feedback evaluations were influenced by their knowledge of the feedback provider’s identity. Students tended to rate human feedback slightly higher after being informed about the provider, whereas AI-generated feedback was rated lower, especially regarding Genuineness where the decrease was significant. Furthermore, the results of the Turing Test had a notable correlation with feedback perception. Students who failed the Turing Test rated AI-generated feedback higher than human feedback, while those who passed the test preferred human-generated feedback. A significant finding of the study was the influence of feedback provider identity on the perceived credibility of the feedback. Humans as feedback providers were consistently rated as more credible compared to AI. This underscores the prevailing preference for human feedback in educational settings and highlights the complexities of integrating AI tools into educational environments.

Feedback plays a crucial role in learning by helping individuals understand and improve their performance. Yet, providing timely, personalized feedback in higher education presents a challenge due to the large and diverse student population, often resulting in delayed and generic feedback. Recent advances in generative Artificial Intelligence (AI) offer a solution for delivering timely and scalable feedback. However, little is known about students’ perceptions of AI feedback. In this paper, we investigate how the identity of the feedback provider affects students’ perception, focusing on the comparison between AI-generated and human-created feedback. Our approach involves students evaluating feedback in authentic educational settings both before and after disclosing the feedback provider’s identity, aiming to assess the influence of this knowledge on their perception. Our study with 457 students across diverse academic programs and levels reveals that students’ ability to differentiate between AI and human feedback depends on the task at hand. Disclosing the identity of the feedback provider affects students’ preferences, leading to a greater preference for human-created feedback and a decreased evaluation of AI-generated feedback. Moreover, students who failed to identify the feedback provider correctly tended to rate AI feedback higher, whereas those who succeeded preferred human feedback. These tendencies are similar across academic levels, genders, and fields of study. Our results highlight the complexity of integrating AI into educational feedback systems and underline the importance of considering student perceptions in AI-generated feedback adoption in higher education.