The Impact of an Ai-Based Dynamic Feedback System on Student Academic Performance in Higher Education

Abstract

This study has delved into exploring the impact of an AI-based dynamic feedback system on student academic performance in higher education, particularly in the context of China. Accordingly, this research is carried out with the integration of “perceived ease of use”, “perceived usefulness”, “feedback”, and “motivation”, on “student academic performance”, which are grounded in the Technology Acceptance Model (TAM), Self-Regulated Learning Theory, and Constructivist Learning Theory. The integration of these constructs has encouraged the progress of the research in a systematic manner by developing relevant objectives, questions, and hypotheses employing the same. A quantitative research design has been adopted, with data collected from 420 undergraduate and postgraduate students in Guangdong, China, using a structured questionnaire based on a five-point Likert scale. The findings underscore that AI-based feedback systems positively influence student academic performance by providing timely, personalised, and actionable insights that foster engagement, motivation, and self-regulated learning, ultimately enhancing their academic performance. The outcomes from the descriptive analysis revealed a higher degree of agreement regarding the significance of the constructs on perceptions of usefulness, ease of use, feedback, and motivation. Reliability and validity tests have further added to the conformity of strong internal consistency and significant positive correlations between all independent variables and student academic performance.