What type of study is this?

This is a Mixed-Methods study (also classified as: Qualitative Study).

What question did this study set out to answer?

This research examines the impact of AI-enabled microlearning on student engagement and inclusivity in higher education.

December 9, 2025Open Access

AI-Enabled Microlearning and Case Study Atomisation: ICT Pathways for Inclusive and Sustainable Higher Education

Key Points

This research examines the impact of AI-enabled microlearning on student engagement and inclusivity in higher education.
Applied a mixed-methods design across two cohorts (2023, n = 138; 2024, n = 140)
Data collection included surveys, learning analytics, and qualitative feedback
Quantitative analyses used Wilcoxon signed-rank tests and regressions
Qualitative data underwent thematic analysis
Significant improvements observed in engagement, inclusivity, and academic performance (p < 0.001)
Engagement metrics showed video viewing up by 19%, quiz completion by 21%, and forum participation by 65%
Forum contributions and video engagement were primary predictors of academic grades

Abstract

The integration of Artificial Intelligence (AI) into higher education offers new opportunities for inclusive and sustainable learning. This study investigates the impact of an AI-enabled microlearning cycle—comprising short instructional videos, formative quizzes, and structured discussions—on student engagement, inclusivity, and academic performance in postgraduate management education. A mixed-methods design was applied across two cohorts (2023, n = 138; 2024, n = 140). Data included: (1) survey responses on engagement, accessibility, and confidence (5-point Likert scale); (2) learning analytics (video views, quiz completion, forum activity); (3) academic results; and (4) qualitative feedback from open-ended questions. Quantitative analyses used Wilcoxon signed-rank tests, regressions, and subgroup comparisons; qualitative data underwent thematic analysis. Findings revealed significant improvements across all dimensions (p < 0.001), with large effect sizes (r = 0.35–0.48). Engagement, accessibility, and confidence increased most, supported by behavioural data showing higher video viewing (+19%), quiz completion (+21%), and forum participation (+65%). Regression analysis indicated that forum contributions (β = 0.39) and video engagement (β = 0.31) were the strongest predictors of grades. Subgroup analysis confirmed equitable outcomes, with non-native English speakers reporting slightly higher accessibility gains. Qualitative themes highlighted interactivity, real-world application, and inclusivity, but also noted quiz-related anxiety and a need for industry tools. The AI-enabled microlearning model enhanced engagement, equity, and academic success, aligning with SDG 4 (Quality Education) and SDG 10 (Reduced Inequalities). By combining Cognitive Load Theory, Kolb’s experiential learning, and Universal Design for Learning, it offers a scalable, pedagogically sustainable framework. Future research should explore emotional impacts, AI co-teaching models, and cross-disciplinary applications. By integrating Kolb’s experiential learning, Universal Design for Learning, and Cognitive Load Theory, this model advances both pedagogical and ecological sustainability.

AI-Enabled Microlearning and Case Study Atomisation: ICT Pathways for Inclusive and Sustainable Higher Education

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Abstract

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