Personalisation versus Equity in AI-Driven Formative Feedback: Evidence from Heterogeneous Learner Populations - Jarudin; Dedi; Edy Tekad Bronto Waluyo (Journal of Pedagogi, 2026)
This study examines whether AI-driven personalised formative feedback improves learning while maintaining equity across heterogeneous learner populations. Grounded in self-regulated learning theory, feedback intervention theory, universal design for learning, and algorithmic fairness, the study examines the relationships among AI feedback, perceived feedback quality, student engagement, and learning achievement. A quantitative explanatory design was employed with diverse higher education students using an AI-enabled learning platform. Data were collected through pre- and post-tests, system logs, and validated survey instruments. Structural equation modelling and multigroup analysis were used to test direct, mediated, moderated, and subgroup effects. The findings are expected to show that AI-driven feedback enhances perceived feedback quality, engagement, and achievement. However, its benefits may vary across learners with different prior achievement, digital literacy, socioeconomic background, and language proficiency. The study contributes to educational technology research by moving beyond effectiveness claims and examining whether personalisation produces inclusive or unequal outcomes. The findings offer practical implications for designing transparent, fair, and pedagogically responsible AI feedback systems that support both individualised learning and educational equity.