Assessing student perceptions and use of instructor versus AI ‐generated feedback
Open accessAbstract
Despite the growing research interest in the use of large language models for feedback provision, it still remains unknown how students perceive and use AI‐generated feedback compared to instructor feedback in authentic settings. To address this gap, this study compared instructor and AI‐generated feedback in a Java programming course through an experimental research design where students were randomly assigned to either condition. Both feedback providers used the same assessment rubric, and students were asked to improve their work based on the feedback. The feedback perceptions scale and students' laboratory assignment scores were compared in both conditions. Results showed that students perceived instructor feedback as significantly more useful than AI feedback. While instructor feedback was also perceived as more fair, developmental and encouraging, these differences were not statistically significant. Importantly, students receiving instructor feedback showed significantly greater improvements in their lab scores compared to those receiving AI feedback, even after controlling for their initial knowledge levels. Based on the findings, we posit that AI models potentially need to be trained on data specific to educational contexts and hybrid feedback models that combine AI's and instructors' strengths should be considered for effective feedback practices. Practitioner notes: What is already known about this topic Feedback is crucial for student learning in programming education. Providing detailed personalised feedback is challenging for instructors. AI‐powered solutions like ChatGPT can be effective in feedback provision. Existing research is limited and shows mixed results about AI‐generated feedback. What this paper adds The effectiveness of AI‐generated feedback was compared to instructor feedback. Both feedback types received positive perceptions, but instructor feedback was seen as more useful. Instructor feedback led to greater score improvements in the programming task. Implications for practice and/or policy AI should not be the sole source of feedback, as human expertise is crucial. AI models should be trained on context‐specific data to improve feedback actionability. Hybrid feedback models should be considered for a scalable and effective approach.
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Students often prefer instructor feedback over AI-generated feedback, even when human feedback is slower.
“Er et al. show that students often prefer instructor feedback over AI-generated feedback, even when human feedback is slower, suggesting that timeliness does not substitute for perceived credibility, nuance, and relational value in assessment [16].”
From Leveraging Artificial Intelligence in Scholarly Publishing (2025) · cites this paper as [16]
The Discussion (page unavailable) reports that students perceived instructor feedback as significantly more useful than AI feedback, which supports a narrower claim about perceived usefulness. The Experimental design and procedure section (page unavailable) says students did not know which source produced their feedback and both groups used it in the same session. The study therefore does not establish that students preferred known instructor feedback despite receiving it more slowly, or that timeliness was outweighed by credibility or relational value. (LLM, conf=0.90)
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- Authors
- Er, E., Akçapınar, G., Bayazıt, A., Noroozi, O., & Banihashem, S. K.
- Affiliations
- Middle East Technical University (TR)
- Hacettepe University (TR)
- Ankara University (TR)
- Wageningen University & Research (NL)
- Open University of the Netherlands (NL)
- Corresponding author
- Er, Erkan
- Venue
- British Journal of Educational Technology
- Publisher
- Wiley
- Published
- 2024-12-27
- Volume
- 56
- Issue
- 3
- Pages
- 1074-1091
- ISSN
- 0007-1013 (ISSN-L)
- 1467-8535
- OpenAlex
- W4405868861
- Cited by
- 114
- References
- 58
- Citations by year
- 2026: 81 · 2025: 33
- FWCI
- 43.01
- Type
- journal article
- Language
- en
- Open access
- green
- research.wur.nl
- Licence
- Locations
- British Journal of Educational Technology (publishedVersion)
- DSpace (Open University in the Netherlands) (publishedVersion)
- Socio-Environmental Systems Modeling (submittedVersion)
- OpenMETU (Middle East Technical University) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Social Sciences › Education › Student Assessment and Feedback
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- AI-generated feedback, instructor feedback, large language models, student perceptions of feedback, programming education, Java programming, personalized feedback, ChatGPT
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