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

arXiv: https://arxiv.org/abs/2504.10961 · HTML: https://mlsanity.com/paper/2504.10961/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 9 of its 47 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Human values and AI feedback alignment](https://mlsanity.com/alignment/human-values-and-ai-feedback-alignment/)

## Builds on (most cited first, 9 of 9)

- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems](https://mlsanity.com/paper/2001.08298/) (2020)
- [When combinations of humans and AI are useful: A systematic review and meta-analysis](https://mlsanity.com/paper/2405.06087/) (2024)
- [Designing for human-AI complementarity in K-12 education](https://mlsanity.com/paper/2104.01266/) (2021)
- [The Interplay of Learning, Analytics, and Artificial Intelligence in Education: A Vision for Hybrid Intelligence](https://mlsanity.com/paper/2403.16081/) (2024)
- [Students' Perceptions and Preferences of Generative Artificial Intelligence Feedback for Programming](https://mlsanity.com/paper/2312.11567/) (2023)
- [Effects of Human vs. Automatic Feedback on Students' Understanding of AI Concepts and Programming Style](https://mlsanity.com/paper/2011.10653/) (2020)
- [Investigating the Essential of Meaningful Automated Formative Feedback for Programming Assignments](https://mlsanity.com/paper/1906.08937/) (2019)

## Built on by (most cited first, 1 of 1)

- [Towards Synergistic Teacher-AI Interactions with Generative Artificial Intelligence](https://mlsanity.com/paper/2511.19580/) (2025)

## Abstract (arXiv)

As generative AI models, particularly large language models (LLMs), transform educational feedback practices in higher education (HE) contexts, understanding students' perceptions of different sources of feedback becomes crucial for their effective implementation and adoption. This study addresses a critical gap by comparing undergraduate students' trust in LLM, human, and human-AI co-produced feedback in their authentic HE context. More specifically, through a within-subject experimental design involving 91 participants, we investigated factors that predict students' ability to distinguish between feedback types, their perceptions of feedback quality, and potential biases related to the source of feedback. Findings revealed that when the source was blinded, students generally preferred AI and co-produced feedback over human feedback regarding perceived usefulness and objectivity. However, they presented a strong bias against AI when the source of feedback was disclosed. In addition, 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 LLM-generated feedback and increased their trust in all types of feedback. More years of students' experience using AI for general purposes were associated with lower perceived usefulness and credibility of feedback. These insights offer substantial evidence of the importance of source credibility and the need to enhance both feedback literacy and AI literacy to mitigate bias in student perceptions for AI-generated feedback to be adopted and impact education.