The role of AI-powered learning analytics in enhancing EFL curriculum design and learning outcomes in higher education

Abstract

This study explores the role of AI-powered learning analytics in improving EFL curriculum design and student learning outcomes in higher education. Adopting a quasi-experimental mixed-methods design, the research engaged 120 undergraduate EFL students and 10 faculty members over a 16-week semester, integrating quantitative data—such as AI-generated engagement metrics and pre/post-test scores—with qualitative insights from student focus groups and faculty interviews. Results revealed a significant increase in academic performance, with post-test scores rising substantially. Engagement indicators, including time-on-task and online participation, were strong predictors of success, allowing for the early identification of at-risk learners. EFL students reported that personalized, real-time feedback enhanced motivation, accountability, and self-regulated learning, while faculty used analytics to adjust teaching strategies and revise curriculum elements, such as embedding targeted workshops. Despite these benefits, challenges emerged in the form of privacy concerns, the psychological burden of continuous monitoring, and faculty difficulties in interpreting complex data due to limited data literacy. The study concludes that AI-powered analytics has transformative potential, provided institutions invest in professional development, ethical data frameworks, and balanced integration with human judgment.