AI ‐Driven Intelligent Feedback System for Enhancing Self‐Assessment Accuracy in Higher Education Writing

ABSTRACT With the rapid advancement of generative artificial intelligence, large language models (LLMs) have become increasingly integrated into education, particularly for automated formative feedback and writing assessment. This study introduces and evaluates an AI‐driven intelligent feedback system aimed at promoting sustainable and inclusive practices in higher education. It does so by delivering cost‐effective feedback in under‐resourced contexts and adaptive guidance for diverse learners. The system is built on transformer‐based models (BERT and RoBERTa) to support personalised writing evaluation and feedback generation. The system aims to improve students’ self‐assessment accuracy (SAA), a critical factor for self‐regulated learning, while addressing the challenge of delivering high‐quality feedback efficiently in under‐resourced contexts. A quasi‐experimental design was employed to examine the effects of LLM‐generated feedback (LLMF) on students’ SAA and to investigate how these effects vary by initial ability. Results indicated no significant group‐level difference in posttest SAA between the experimental and control groups. More importantly, interaction analysis revealed a significant moderating effect of Initial Self‐Assessment Accuracy (ISAA). Students with lower baseline accuracy benefited substantially from LLMF, while those with higher baseline SAA showed limited change. This compensatory effect highlights the potential of LLMF to reduce inequities in self‐regulated learning. These findings demonstrate the potential of AI‐driven feedback systems to cost‐effectively reduce calibration gaps and foster metacognitive development. By embedding adaptive and personalised mechanisms, such systems advance educational equity and promote scalable personalised learning. They also contribute to the broader agenda of intelligent and sustainable education.