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close the gap between current and expected performance (Sadler, 1989). Additionally,
repeated use of the system supports self-regulated learning by encouraging continuous
monitoring and improvement (Nicol & Macfarlane-Dick, 2006).
Students’ responses to negative feedback were more moderate but still
constructive. While motivation after receiving negative feedback was average (mean =
3.51), students generally used it to improve their work (mean = 3.81). This suggests that
AI feedback, although not always perceived as strongly as instructor feedback, remains
effective in supporting revision and learning. The findings also indicate that students were
able to interpret feedback positively, consistent with growth mindset principles (Dweck,
2006) and Feedback Intervention Theory (Kluger & DeNisi, 1996), which emphasise task-
focused improvement. By guiding rather than correcting, the system encourages active
engagement and deeper learning. Overall, the integration of AI feedback in ODL
demonstrates its potential to enhance learner autonomy, engagement, and continuous
improvement at scale.
8. Conclusion
In conclusion, the findings show that the AI-assisted feedback system enhanced
student satisfaction and usage intention while promoting constructive engagement with
feedback. Students generally accepted AI--assisted feedback, valuing its timely and
actionable insights. However, its effectiveness depends on ensuring feedback is
contextually relevant to maintain trust. The study demonstrates the effective use of AI-
assisted feedback in supporting formative assessment.

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