A recent mixed-methods study by Yilmaz and colleagues examined whether feedback generated by artificial intelligence can help university students develop self-regulated learning skills (SRLs) in an online course, and how students’ perceptions of the feedback source shape their attitudes. The study was conducted within a nine-week distance-learning “Basic Statistics” module and involved 46 higher education students who were randomly and blindly assigned to either a GenAI feedback group (n = 23) or a tutor feedback group (n = 23), with participants unaware of which type of feedback they were receiving during the intervention itself.
The researchers drew on Nazaretsky and colleagues’ (2024) four-dimension feedback perception framework, covering objectivity, usefulness, genuineness, and provider credibility, together with Barnard and colleagues’ (2009) six-dimension SRL model, comprising goal setting, task strategies, environment structuring, time management, help-seeking, and self-evaluation. SRL was measured using trace-based indicators adapted from Ye and Pennisi’s (2022) framework, derived from roughly 48,000 MOODLE log records that were mined, standardised, and mapped onto SRL proxy scores before and after two feedback interventions. GenAI feedback was produced using GPT-4 through structured prompts fed with each student’s individual proxy z-scores, while tutor feedback was generated using a purpose-built support tool named RefleXED, based on the same underlying data.
The results showed that students rated GenAI-generated feedback more favourably than tutor-generated feedback across every perception dimension, with a statistically significant advantage found specifically for Genuineness (p = .036, r = .36). In terms of SRL development, the GenAI feedback group demonstrated a significant improvement in the Task Strategies dimension (p = .044, r = .35), and a near-significant trend emerged for Time Management (p = .057, r = .33), while no significant between-group differences were found for the remaining SRL dimensions. Qualitative analysis of open-ended responses from 17 treatment-group students revealed considerable variation in awareness of the feedback source: students who recognised the feedback as AI-generated generally reported no change in attitude, prioritising content quality over provenance, whereas a smaller number of unaware students indicated their views might have shifted had they known the source in advance.
The findings suggest that carefully designed, learning-analytics-informed GenAI feedback holds real potential to scale personalised support for self-regulation in online higher education, but the researchers caution against viewing GenAI as a replacement for tutors. Instead, they argue for a complementary model in which GenAI’s scalability and adaptability work alongside tutors’ pedagogical judgement, while institutions remain attentive to how students’ awareness and perceptions of the feedback source can influence its ultimate impact. The authors note that the modest sample size (n = 46) drawn from a single course limits generalisability, and they call for future research involving larger, more diverse cohorts, no-feedback control conditions, and longer-term tracking of SRL outcomes.
Source (Open Access): Yilmaz, M., Temur, H. B., Emmungil, L., Çelik, E., Gauthier, A., & Cukurova, M. (2026). Supporting self-regulated learning through generative AI feedback in online higher education: the importance of student perceptions of the source of feedback. International Journal of Educational Technology in Higher Education, 23(1), 16.