A qualitative study of 16 undergraduates at a Hong Kong teacher-education university who used ChatGPT 3.5 to obtain feedback on IELTS writing tasks. Data came from unobtrusive screen-recorded observations plus stimulated-recall interviews. The study extends the traditional tripartite model of feedback engagement (cognitive, affective, behavioural) to a four-dimensional model adding metacognitive engagement, and asks what feedback literacy students need in a GenAI context.
Key findings by engagement dimension
Cognitive engagement — active, comparison-driven
Three strategies: selective attention (n = 8, focusing on areas of self-assessed weakness), extracting key information (n = 4, filtering lengthy ChatGPT output), and comparison (n = 10 — comparing original vs revised versions, ChatGPT comments across time, against IELTS criteria, and against teachers' previous comments).Comparison is "a hub" for internal feedback (Nicol 2021): in GenAI contexts students can instantaneously compare multiple sources and versions — something hard in traditional feedback environments.Metacognitive engagement — weaker, signalling "metacognitive laziness"
Only 5 of 16 monitored their revision process; 6 reflected after finishing; none used goal-setting or planning strategies.Echoes Fan et al.'s (2025) metacognitive laziness: ChatGPT may boost short-term task performance while impeding self-regulation and knowledge transfer. Most revisions targeted local aspects of L2 writing (grammar, vocabulary, sentence structure), which may not transfer to future writing tasks.Affective engagement — calm, but selectively trusting
Majority (n = 13) described interactions as calm, relaxed, stress-free — minimal emotional resistance (unlike typical teacher-feedback contexts).Trust split: trusted ChatGPT on language forms (n = 11) but doubted its examples/evidence (n = 12); some doubted its legitimacy as an IELTS examiner.Negative affect arose when ChatGPT misunderstood prompts or repeated similar suggestions.Behavioural engagement — superficial patterns
308 prompts total (9–40 per student); more prompts on local aspects (n = 104) than global (n = 55); 11.3% were direct copies of the draft; only 1.6% probed ChatGPT's credibility.Interactions were mostly one-off (n = 133 one-round vs 34 two-round vs 18 three-round).High uptake: 56.3% of ChatGPT comments honestly followed, 27.6% adapted, 16.1% rejected (83.9% overall uptake) — but revisions were superficial and local, and some students avoided directly using AI content due to academic-integrity beliefs.Five capacities for student feedback literacy in a GenAI context
1. Prompt engineering — the quality of GenAI feedback is largely determined by prompt clarity (e.g. the CLEAR framework; Lo 2023).
2. Evaluative judgement — discerning useful feedback from plausible-but-unreliable output.
3. Emotional reflexivity — balancing trust and doubt by understanding GenAI's capabilities and limits (Bearman & Ajjawi 2023).
4. Ethical decision-making — deciding how, when, and why to use GenAI feedback so work remains authentic (academic integrity).
5. Metacognitive skills — setting feedback goals, planning prompts, self-monitoring interactions, and reflecting on the whole process.
Implications
Feedback literacy and engagement are bidirectional and mutually reinforcing; the model proposed here (Figure 2) shows the four engagement dimensions interplaying with these five literacy capacities.GenAI feedback can lower the emotional barrier to feedback seeking (important in power-hierarchical cultures — see GenAI Teacher Feedback Comparison's "less risky" finding), but without metacognitive scaffolding students drift toward superficial, high-uptake, low-transfer use — a core Over Reliance risk.Connected Concepts
Over RelianceConnected Articles
GenAI Teacher Feedback ComparisonCitation
Zhan, Y., & Yan, Z. (2026). Students' engagement with ChatGPT feedback: Implications for student feedback literacy in the context of generative artificial intelligence. Assessment & Evaluation in Higher Education, 51(5), 821–834