Agentic education · Learning science

Actionable companion · Feedback & scaffolding

Learning with agency: L2 students’ academic emotions in teacher feedback and GAI-generated feedback on argumentative writing

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 With the rise of generative artificial intelligence (GAI) in the field of second (foreign) language writing, GAI-generated feedback has garnered increasing scholarly attention. While previous research has compared the efficacy of GAI and teacher feedback, the academic emotions elicited by GAI during the revision process remain under-explored. To fill this lacuna, this study employed a mixed-methods research design to investigate the emotional experiences and corresponding regulation strategies of L2 students in both teacher and GAI-generated feedback contexts. Eighty-six Chinese master’s students participated in the study, with qualitative data gathered from written retrospective reflections, essays with teacher feedback, screenshots documenting the GAI-assisted revision process, and semi-structured interviews. The results revealed that students experienced higher levels of positive emotions in teacher-feedback contexts, whereas GAI-generated feedback was associated with more negative emotions. Furthermore, the findings identified distinct occurrences, types, and developmental trajectories of emotions across the two feedback conditions. This study offers important pedagogical implications, encouraging teachers to adopt hybrid feedback strategies and suggesting ways to help students manage negative emotions by fostering a growth mindset and enhancing feedback literacy. Keywords: academic emotions, argumentative writing, emotion-regulation strategies, GAI-generated feedback, teacher feedback

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

This study was conducted in a 16-week postgraduate English course at a Chinese university, featuring two lectures per week. Argumentative writing instruction served as a core component of the curriculum and an essential means of preparing students for academic and dissertation writing. To collect rich and relevant data, a convenience sampling technique was employed for participant selection. Selection criteria included English proficiency, prior experience with GAI tools, and the frequency of receiving written assignments. Based on these criteria, 86 participants (37 males and 49 females, all Chinese nationals) were enrolled from two classes taught by the same instructor, who also served as the second researcher. All participants were first-semester accounting majors in a three-year academic program. They had achieved average scores of 70 and 68 (out of 100), respectively, on the National Postgraduate Entrance Examination (NPEE). As a high-stakes nationwide test taken by undergraduates seeking admission to postgraduate programs in China, these scores indicated that the participants possessed intermediate to upper-intermediate English proficiency. Prior to the commencement of the study, all participants were informed of the research objectives, assured of data confidentiality, and provided with written consent forms. To alleviate concerns regarding potential negative repercussions, participants were assured that their contributions would not affect their academic assessments for the course. This measure was intended to establish an open, secure, and honest atmosphere for sharing.

Limitations and uncertainty · source passage

This study examines master’s students’ academic emotions and their corresponding emotion-regulation strategies in response to teacher and GAI-generated feedback on argumentative writing. It identifies 23 distinct emotions (categorized by valence, activation, and object focus) and 16 emotion-regulation sub-strategies across five categories. By incorporating L2 students’ discrete academic emotions and their evolving responses to GAI feedback, this research bridges a significant gap and confirms the highly context-dependent, nuanced, and dynamic nature of academic emotions (Pekrun, 2006; Pekrun and Linnenbrink-Garcia, 2012; Harley et al., 2019). The findings reveal that while teacher feedback elicits a blend of positive and negative emotions, notably social emotions such as gratitude, trust, and awe, GAI-generated feedback initially triggered a prevalence of negative emotions, including anxiety, frustration, and confusion, due to barriers in accessing useful input. However, by employing effective strategies—primarily situation modification and cognitive change—students experienced an emotional reversal that ultimately enhanced their creativity and confidence in GAI-assisted learning.

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.

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

Lian Wang; Mei An. L2 students’ academic emotions in teacher feedback and GAI-generated feedback on argumentative writing. Frontiers in Psychology (2026-06-11). https://doi.org/10.3389/fpsyg.2026.1816731

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.