Next-Gen Education: Enhancing AI for Microlearning
Suman Saha, Fatemeh Rahbari, Farhan Sadique, Sri Krishna Chaitanya Velamakanni, Mahfuza Farooque, William J. Rothwell

TL;DR
This paper investigates integrating AI-powered microlearning strategies into university computer science curricula to improve student engagement and learning outcomes amid declining attendance and remote learning trends.
Contribution
It introduces a novel approach of using AI tools like ChatGPT to automate microlearning material creation, enhancing educational efficiency and effectiveness.
Findings
AI can significantly reduce educator workload in material development
Microlearning improves engagement for complex topics like algorithms
AI-enhanced microlearning has potential to transform computer science education
Abstract
This paper explores integrating microlearning strategies into university curricula, particularly in computer science education, to counteract the decline in class attendance and engagement in US universities after COVID. As students increasingly opt for remote learning and recorded lectures, traditional educational approaches struggle to maintain engagement and effectiveness. Microlearning, which breaks complex subjects into manageable units, is proposed to address shorter attention spans and enhance educational outcomes. It uses interactive formats such as videos, quizzes, flashcards, and scenario-based exercises, which are especially beneficial for topics like algorithms and programming logic requiring deep understanding and ongoing practice. Adoption of microlearning is often limited by the effort needed to create such materials. This paper proposes leveraging AI tools, specifically…
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Taxonomy
TopicsOnline Learning and Analytics · E-Learning and COVID-19 · Information Systems and Technology Applications
