Agentic education · Learning science

Actionable companion · Feedback & scaffolding

Learning with agency: Exploring English for academic purposes learners’ engagement with ChatGPT-4 in academic writing revision: a case study from a private language institute in China

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Age / stage not established. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Abstract While a growing body of research has examined the affordances and effects of generative artificial intelligence (GenAI) in language learning, less attention has been paid to how learners engage with these tools during the writing revision process across behavioural, cognitive, and affective dimensions. This qualitative multiple-case study investigates how eight English for Academic Purposes (EAP) learners engage with ChatGPT-4.0 while revising academic writing. Data included screen recordings of learner–AI interactions, interaction transcripts, stimulated recall sessions, and semi-structured interviews. The findings reveal that learner engagement is heterogeneous and multidimensional. Behavioural and cognitive engagement ranged from deliberate, evaluative processing to more mechanical uptake, including instances of overreliance on AI-generated feedback. Affective engagement was similarly mixed: while participants valued the immediacy and accessibility of feedback, some reported a lack of social and contextual support compared to teacher guidance. These findings highlight the importance of teacher mediation and critical AI literacy in supporting meaningful engagement with GenAI. The study contributes to emerging research on AI-assisted language learning by specifying the conditions under which GenAI feedback may foster, rather than undermine, reflective and critical engagement in EAP writing. Keywords: ChatGPT, EAP, EAP writing, generative artificial intelligence, L2 learner engagement, L2 writing

What kind of evidence is this?

Supporting AI learning science · Qualitative / mixed methods

Stage: Age / stage not established. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

At the outset of the study, all participants (see Table 1) attended an internal seminar entitled “Generative AI Has Revolutionised and Transformed EAP Writing”, organised by a private school. Eight participants were included in this qualitative multiple-case study. To further protect participant confidentiality, detailed individual demographic profiles are not reported. Participants were learners enrolled in the institute’s EAP programme and represented a range of disciplinary backgrounds. Their ages ranged from 19 to 29 years, and the sample included both undergraduate- and postgraduate-level learners. English proficiency ranged from upper-intermediate to advanced, based on institutional placement records. Several participants reported prior experience with AI-supported writing or search tools, while others had little or no previous experience. The seminar was delivered during participants’ EAP writing course and aimed to introduce the core writing-related functions of ChatGPT, its basic operational features, and potential pedagogical applications for EAP writing tasks. The session was designed to familiarise students with the tool in preparation for EAP writing assessments and to support their L2 academic writing development.

Limitations and uncertainty · source passage

Several interpretive boundaries should be acknowledged when considering the findings of this qualitative multiple-case study. First, the data collection was preceded by a one-hour training seminar that introduced participants to ChatGPT’s functionalities and effective prompting strategies. This created a lightly scaffolded instructional context. Consequently, the observed engagement patterns may partly reflect this pedagogical framing, influencing participants’ expectations of what ChatGPT should do, their willingness to trust AI-generated feedback, and the specific uptake patterns later observed, rather than capturing purely unprompted or natural engagement.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
  • Limit simultaneous instructions and unnecessary dialogue. Compare task performance and a suitable mental-effort measure; easier-feeling work is not automatically better learning.
  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
  • Co-design accommodations with learners and specialists. Check access and error burden across groups without inferring subgroup effectiveness from small or unreported samples.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Jinming Lawrence Du; Yiran Chen. Exploring English for academic purposes learners’ engagement with ChatGPT-4 in academic writing revision: a case study from a private language institute in China. Frontiers in Artificial Intelligence (2026-06-01). https://doi.org/10.3389/frai.2026.1802007

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.