Karjus, A., Leoste, J., & Oun, T. (2026) — arXiv:2607.16115 (cs.CY, cs.HC). 📄 Full text (arXiv)
This short paper provides rare descriptive classroom evidence on what happens when students repeatedly use generative-AI feedback across a full semester of writing coursework. Drawing on 2,988 reflective essay-feedback-appraisal instances from 283 Estonian bachelor students, the authors find that students rated AI feedback as helpful and actionable more often than not, but a growing minority (about one in ten) found it unhelpful toward the end of the term. The work sits squarely in the ai-generated-feedback-higher-ed literature and complements prior ai-feedback-quality studies by tracking feedback appraisal longitudinally rather than in a one-off lab task.
The study surfaces the central tension in over-reliance: generative AI offers a fast, scalable route to immediate writing advice, but it is not a self-contained path to deeper reflection. Using a validated AI-text classifier, the authors estimate the share of essays that look like unaided student writing, linking tool use to the broader question of whether AI assistance erodes learning gains. These findings reinforce concerns echoed in generative-ai-reduced-study-time-math and sequenced-ai-feedback-learning about dosage and critical engagement. The paper argues benefits depend on whether students learn to use AI selectively and critically, a skill squarely within ai-literacy and the student-experience of writing support in writing-education.
Related Pages
- genai-teacher-feedback-comparison — Large-scale perception data complements semester-long evaluation
- ai-generated-feedback-higher-ed — Semester-long student evaluation of repeated GenAI writing feedback (n=283).
- ai-feedback-quality — Longitudinal evidence that AI writing feedback is usually rated helpful but decays in perceived value.
- over-reliance — Shows benefits of AI feedback depend on selective, critical use to avoid over-use harming learning.
- writing-education — Classroom study of AI feedback integration across a semester of writing coursework.
- student-experience — Reports student-perceived helpfulness of self-selected AI feedback tools over a term.
- ai-literacy — Argues learning to use AI selectively and critically is the key mediator of benefit.