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Summarise, elaborate, try again: exploring the effect of feedback literacy on AI-enhanced essay writing

● high · 2026-08-15 · feedback, ai-feedback-quality, self-regulated-learning, formative-assessment, writing-education, ai-literacy, higher-ed

Summary

Hawkins, Taylor-Griffiths & Lodge (2026) investigate how university students use generative AI for feedback during essay writing, using a novel simulated assessment task methodology. Thirty-two psychology students at The University of Queensland completed a screen-recorded, 25-minute essay writing task with unrestricted access to ChatGPT (and optionally other tools such as Quillbot, Co-pilot, and DeepL), then watched the recording back in a video-stimulated interview. A thematic analysis (Braun & Clarke 2006) of the transcribed interviews identified four distinct, temporally ordered themes: feedforward (initial content and idea requests), feedback (requesting assessments of one's own work, including criteria-based and line-level language improvements), feedback evaluation (making decisions based on AI output, enacted through 'expand', 'summarise', 'elaborate', and 'try again' prompts, and providing feedback to AI), and AI avoidance (deliberately not using AI, often citing academic integrity or a desire that the essay sound 'in my own words'). A standard multiple regression found feedback literacy to be the only significant positive predictor of essay grade (β = 0.46, p = 0.017), supporting the study's working theory. The paper interprets feedback literacy (Carless & Boud 2018) within the broader scope of self-regulated learning (Pintrich 2000) and is one of the first to deploy Dawson et al.'s (2024) validated Feedback Literacy Behaviour Scale.

Key Findings

  • Feedback literacy predicts essay task performance. Feedback literacy was the only significant (positive) predictor of essay grade in a standard multiple regression (β = 0.46, t(25) = 2.56, p = 0.017, sr² = .17); feedback literacy and essay grade were positively and significantly correlated (p = 0.019). The overall model accounted for a non-significant 35% of variance (F(6, 25) = 2.26, p = .071), and the analysis was underpowered.
  • Four themes of student AI use for feedback. Feedforward (initial content requests, e.g. idea generation, exemplar essays, comprehension support); feedback (requesting assessments of own work, mostly 'line-level' language improvements rather than overall feedback); feedback evaluation (evaluating AI output via prompts and comparing it to other sources — fewer than a third compared AI output to another internet source); and AI avoidance (expressed by half the participants, motivated by academic integrity or wanting the essay to be their own words).
  • Simulated assessment task as a methodological bridge. The novel screen-recorded task plus video-stimulated interview bridged the gap between self-reported AI use and observed practice, gathering evidence of students' metacognitive awareness of their AI interactions.
  • Feedback literacy within self-regulated learning. AI co-regulated cognition (outsourcing idea generation and comprehension), motivation (corroborating task understanding and self-efficacy), behaviour (varying help-seeking sophistication), and context. Without SRL skills grounded in self-efficacy and a motivation to learn, the authors argue AI operates "more like a student than a student tool."
  • Task-level feedback dominance. Students typically requested task-level feedback, which Hattie & Timperley (2007) argue is difficult to transfer to other tasks and limited in effect on long-term learning.

Implications

  • Institutions should foster feedback literacy skills that support thoughtful, considered use of generative AI, applicable across domains, so students can evaluate and co-regulate their learning regardless of technology.
  • Highlights that the value of AI feedback depends on the learner's evaluative judgement and self-regulatory skill, not just the tool — a shared thread across the wiki's AI Feedback Quality and Self Regulated Learning research.
  • Students largely wanted to use AI without contravening academic standards, but more advanced feedback literacy would help them adapt and thrive; many regulated towards performance and avoidance goals rather than learning goals.
  • Limitations: underpowered regression, single-university psychology sample, and task-fidelity constraints (performance did not affect grades and AI use was not penalised, likely shifting motivation away from learning goals).

Connected Concepts

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Citation

Hawkins, B., Taylor-Griffiths, D., & Lodge, J. M. (2026). Summarise, elaborate, try again: exploring the effect of feedback literacy on AI-enhanced essay writing. Assessment & Evaluation in Higher Education, 51(5), 879–891.