Generative artificial intelligence as an enabler of student feedback engagement: a framework

Synthesis: A conceptual framework paper from feedback-literacy leaders Boud, Dawson and Yan arguing that GenAI can be an enabler of student feedback engagement — but only when positively aligned with students' feedback literacy. Analysing feedback across three stages (eliciting, processing, enacting), it proposes a cyclical self-regulation feedback model (feedback forethought → feedback control → feedback retrospect) grounded in an ecological perspective, as a pathway for students to productively engage with GenAI feedback while mitigating over-reliance and academic-integrity risks.

The framework

The paper is a theoretical synthesis (deductive reasoning, four steps: literature search, analysis, adaptation, refinement) that adapts Chong's (2021) ecological framework to the GenAI context. Its core argument is ecological: GenAI affordances create opportunities for feedback engagement, but affordances do not guarantee engagement — students must possess the feedback literacy to perceive and act on them (van Lier's "match between the environment and agent").

GenAI as an enabler at each feedback stage

• Eliciting (seeking feedback). GenAI is on-demand, always accessible, and less intimidating than human instructors — helpful in large classes, time-poor contexts and power-hierarchical cultures. But effectiveness hinges on prompt quality; vague prompts yield generic, useless output. Constraints include version/access divides (subscriptions worsen the Digital Divide), English-centric training data, and data-privacy/intellectual-property concerns.
• Processing (making sense). GenAI reduces cognitive barriers (immediate clarification of academic jargon) and emotional barriers (less anxiety than human interaction). But output can be hallucinated, biased or general/overlapping, so students must exercise evaluative judgement and emotional reflexivity rather than blindly trusting GenAI (see AI Feedback Quality).
• Enacting (using feedback). GenAI enables iterative, cyclical feedback loops, self-monitoring toward self-defined goals and personalised non-judgemental suggestions. The risk is uncritical over-reliance and boundary confusion over what constitutes acceptable academic-integrity use.

The cyclical self-regulation feedback model

Adapting Zimmerman's (2000) self-regulation phases, the proposed pathway comprises three interwoven phases that cycle continuously:

1. Feedback forethought — setting feedback goals and planning strategically, based on learning needs and understanding of GenAI's capabilities/limitations.
2. Feedback control — self-observation and self-monitoring of one's prompts and GenAI's responses, assessing prompt and information quality, and refining interaction.
3. Feedback retrospect — self-reflection on the feedback received, interaction quality, strategies used and outcomes, feeding insights back into the next cycle's forethought phase.

These phases are interwoven with the eliciting/processing/enacting stages of the feedback process.

The interaction of GenAI context and feedback literacy

Two contrasting illustrative cases (IELTS writing with ChatGPT 3.5) ground the argument: Student A (low feedback literacy) used a vague prompt, received generic feedback, blindly trusted or over-copied output, and showed superficial engagement; Student B (high feedback literacy) used a specific, criteria-referenced prompt, exercised evaluative judgement (following up, cross-checking sources), monitored revisions and achieved deep engagement. This illustrates that feedback literacy specific to the GenAI context — including prompt engineering, evaluative judgement and self-regulation — must align with the GenAI environment for GenAI to be an effective enabler.

Implications for practice

• Teacher role: help students define individual feedback goals before seeking feedback, model metacognitive planning/monitoring/evaluation strategies, and train prompt engineering and evaluative judgement.
• Scaffolding: the process view enables educators to design stage-specific scaffolds (e.g., prompt templates for eliciting, evaluative-judgement tasks for processing, revision monitoring for enacting).
• Research agenda: the model offers a framework for systematically investigating and monitoring feedback engagement in GenAI contexts, with future work on discipline-specific application and how engagement evolves over time.

Connected Concepts

• Feedback
• Self Regulated Learning
• AI Literacy
• AI Feedback Quality
• Formative Assessment
• Teacher Role
• Higher Ed
• Cognitive Offloading
• Prompt Engineering
• Feedback Literacy

Connected Articles

• Chatgpt Feedback Engagement GenAI — Zhan & Yan's companion empirical study of students' ChatGPT feedback engagement and five feedback-literacy capacities in a GenAI context.
• Care Full Feedback GenAI — Winstone et al.'s related position paper on feedback as "matters of care" in an age of GenAI.
• GenAI Teacher Feedback Comparison — Student perceptions of the usefulness and trustworthiness of GenAI vs teacher feedback.
• Feedback Futures GenAI — Complementary analysis of the limits of human and GenAI feedback capacities.
• AI Internal Feedback Evaluative Judgments — Related work on evaluative judgement as a basis of feedback engagement.

Citation

Zhan, Y., Boud, D., Dawson, P., & Yan, Z. (2025). Generative artificial intelligence as an enabler of student feedback engagement: a framework. Higher Education Research & Development, 44(5), 1289–1304. (CC BY-NC-ND 4.0 — license flagged: non-commercial, no-derivatives.)