Summary
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, contextualised (95.2% of contextualisation 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, contextualisation) 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).Implications
Students already self-initiate GenAI feedback at scale, so institutions should actively support how students engage with it (feedback literacy, evaluative judgement — cf. AI Literacy).The trust gap (90.5% vs 60.1%) is not simply an accuracy verdict; it partly reflects source-credibility heuristics (Lipnevich & Smith 2008; Nazaretsky et al. 2024) and may be context-dependent (language-form advice vs deeply contextual course guidance).The "less risky" property makes GenAI feedback valuable for feedback seeking, especially for anxious students — complementing Chatgpt Feedback Engagement GenAI's "calm, stress-free" affective finding — while teacher feedback retains contextual expertise and relational recognition.Directly challenges replacement narratives: GenAI is an additional source, not a substitute for the teacher-learner relationship (cf. AI Generated Feedback Higher Ed, which found equivalent outcomes under strong assessment architecture — outcomes and perceptions can diverge).Connected Concepts
AI LiteracyHigher EdStudent ExperienceGenerative AIConnected Articles
AI Generated Feedback Higher Ed — Artificial intelligence and feedback in university education: effectiveness and student perceptionsChatgpt Feedback Engagement GenAI — Students' engagement with ChatGPT feedback: implications for student feedback literacy in the context of generative a...Feedback Futures GenAI — Feedback futures: beyond the limits of human and GenAI capacitiesLearner Centered Feedback AI — Enhancing learner-centered feedback with AI: teachers' practices and perceptionsRepeated AI Writing Feedback Semester — Student Evaluation of Repeated AI Feedback Across a Semester of WritingA4l Analytics Pipeline — Generalizing a Highly Configurable Analytics Pipeline to Replicate and Support Educational Research Across Multiple D...Aaai2026 Prompting Literacy K12 — Learning to Use AI for Learning: Teaching Responsible Use of AI Chatbot to K-12 Students Through an AI Literacy ModuleAcademiclaw Student Agent Benchmark — AcademiClaw: When Students Set Challenges for AI AgentsAccess Not Enough AI Tutoring 2026 — Access is Not Enough: Human Support Improves Engagement with AI TutoringAdapt Adaptive Lesson Plan Transformer — AdaPT: Adaptive Lesson Plan Transformer for Cross-Regional and Differentiated InstructionAdaptive Pretesting Retention — Do Gains from Generative AI-Enabled Adaptive Pretesting Persist? Evidence from a Retention StudyAffective Text Wearable Student Health — A Formative Study of Brief Affective Text as a Complement to Wearable Sensing for Longitudinal Student Health MonitoringAgency Gap AI Writing — The agency gap in AI-supported writing: how reactive and proactive agent designs shape multimodal reasoningAgent Voice Accents K12 Group Learning — Exploring How Agent Voice Accents Shape Human-AI Collaboration in K-12 Group LearningAgentic AI Education Scoping Review — Agentic AI in Education: A Scoping Review of Research Landscape, Capabilities, and the Frontier Agent ParadigmAgentic Education Coding — Agentic Education with AI Coding AssistantsAgentic Literacy Debt — Agentic Literacy Debt: A Structural Problem the AI Literacy Field Has Not Yet NamedAgents That Teach Incidental Learning — Agents That Teach: Designing Incidental Learning Back into AI-Assisted Software DevelopmentAI Adoption Training Public Sector — The Main Barrier to AI Adoption in the Public Sector is Lack of TrainingAI Adult Learning Guidelines Dis2026 — Guidelines for Designing AI Technologies to Support Adult LearningAI Agents Constructive Conflict Design Education 2026 — Enacting Constructive Conflicts with AI Agents to Enhance Reconsideration among Novice Interaction DesignersAI Agents Peer Learning Discourse — When AI Agents Teach Each Other: Discourse Patterns Resembling Peer Learning in the Moltbook CommunityAI Assessment Human Tutors — AI-Driven Assessment of Human Tutors: Linking Training Performance to Real-Life PracticeAI Assessment Scale Reform — A bit of chaos and madness": The AI Assessment Scale and the work of assessment reformAI Assistance Discretionary Feedback — AI Assistance for Discretionary Work: Increasing Feedback Provision in Higher EducationCitation
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