Comparing Generative AI and teacher feedback: student perceptions of usefulness and trustworthiness

Synthesis: The largest study in the AEHE 51(5) special issue: a cross-sectional survey across four Australian universities (≈192,000 invited; 10,132 volunteered; this paper analyses 6,960 students who answered the feedback items). It combines quantitative comparison of perceived helpfulness/trustworthiness of GenAI vs teacher feedback with thematic analysis of 8,642 open-ended responses (11,903 coded instances, 48 codes). Core conclusion: GenAI and teacher feedback serve different needs — complementary but not interchangeable.

Quantitative findings

• Usage split: 49.7% (n = 3,461) of students had sought feedback from GenAI; 50.3% had not.
• Helpfulness: 83.9% rated AI feedback somewhat/very helpful vs 82.2% for teacher feedback — a small but significant teacher advantage (mean diff 0.14, t(3327) = −7.17, p < .001, d = −0.13).
• Trustworthiness: the striking gap — 90.5% rated teacher feedback somewhat/very trustworthy vs 60.1% for AI (mean diff 0.93, t(3327) = −49.30, p < .001, d = −0.89, a large effect). 58.3% rated teacher feedback very trustworthy vs 8.8% for AI.

Qualitative findings — why they differ

When comparing GenAI to teacher feedback, students said GenAI was more: accessible/easy (99.3% of access codes), fast, voluminous, understandable, objective (less biased), and positive in tone — and less risky (99.3% of relational-risk codes: less vulnerable, no loss of social status). It aided sense-making and could be used before submission.

Teacher feedback was more: relevant, contextualized (95.2% of contextualization codes), specific, in-depth, personal/relational, and expert — but also more frequently negative in tone (e.g. dismissive, insulting) and more likely to produce negative feelings (85.3% of negativity codes).

The two most frequent comparison themes were the nature of the feedback information (54.8% of coded instances; quality, reliability, relevance, contextualization) and feedback processes (22.7%; access, timing, effort, sense-making).

Why students did NOT use GenAI for feedback (n = 3,405 comments)

• 28.1% were unaware it was possible or did not know how (a support/equity gap, not a preference).
• 28.7% cited trustworthiness/reliability concerns.
• 22.5% cited values: preference for human connection (4.3%) or no perceived need (6.3%).
• Smaller shares: academic integrity (9.1%), privacy (2.7%), wanting to preserve effortful learning (18 of 24 effort codes).

What this means for practice

• Instructors. Assign GenAI and teacher feedback complementary roles: GenAI for accessible, low-stakes sense-making before submission, and teacher feedback for the contextual, beyond-the-task guidance students say it uniquely provides.
• Instructors. Develop students' evaluative judgment so they can tell whether GenAI comments are trustworthy; only 60.1% rated AI feedback trustworthy versus 90.5% for teacher feedback, and the authors stress this judgment cannot rest on an uninformed impression.
• Instructors. Close the awareness gap: 28.1% of students who did not use GenAI for feedback did not know it was possible or how to do it, even though 83.9% of those who used it found it helpful.
• Administrators. Support how students engage with GenAI feedback, since 49.7% already self-initiate it, and address the negative tone students associate with teacher feedback (85.3% of negativity codes).
• Learners. Use GenAI feedback to make sense of your work before submission while keeping teacher feedback as the source of course-specific expert judgment — the two are complementary but not interchangeable.

Limitations

• Cross-sectional survey with an optional response: roughly 8,000 respondents from over 200,000 invited students, which the authors state cannot claim to be representative; 6,960 students answered the feedback items.
• Helpfulness and trustworthiness are self-reported perceptions, not observed feedback use or measured learning outcomes.
• All respondents came from four Australian universities, and Australian higher education may differ from other contexts.
• The survey ran before most students had experienced GenAI feedback grounded in their curriculum or assignment resources, a development the authors expect could change perceptions significantly.

Citation

Henderson, M., Bearman, M., Chung, J., Fawns, T., Buckingham Shum, S., Matthews, K. E., & de Mello Heredia, J. (2026). Comparing Generative AI and teacher feedback: Student perceptions of usefulness and trustworthiness. Assessment & Evaluation in Higher Education, 51(5), 863–878