2025 ASEE Annual Conference & Exposition

Next-Gen Education: Enhancing AI for Microlearning

Presented at DSAI Technical Session 7: Natural Language Processing and LLM Applications

This paper explores integrating microlearning strategies into university curricula, particularly in computer science education, to counteract the decline in class attendance and engagement in U.S. universities post-COVID. As students increasingly opt for remote learning and recorded lectures, traditional educational approaches need help maintaining student engagement and effectiveness. Microlearning, which breaks complex subjects into manageable units, is proposed to cater to contemporary students’ shortened attention spans and enhance their educational outcomes. This method caters to students’ learning preferences and enhances their engagement and motivation. It employs interactive formats such as videos, quizzes, flashcards, and scenario-based exercises, particularly beneficial for intricate topics like algorithms and programming logic requiring profound understanding and continuous practice.

However, the adoption of microlearning is often limited by the extensive effort required from educators to create these materials. This paper proposes leveraging advanced AI technologies, specifically ChatGPT, to alleviate the burden on educators by automating the development of supplementary educational materials. While AI can automate specific tasks, the role of educators in guiding and shaping the learning process remains crucial. This AI-driven approach not only diminishes the workload for educators but also ensures that course content is continuously updated with the latest research and technological innovations, with educators providing the necessary context and insights. By examining the capabilities of AI tools like ChatGPT, this study highlights the potential of AI-enhanced microlearning to transform educational practices and
outcomes in computer science, offering a viable model for integrating cutting-edge technology with established teaching methods.

Our ChatGPT-powered tool automatically converts video lectures and slide content into microlearning formats. It has been implemented in two courses, Discrete Math and Programming Language Principles, for junior students at Pennsylvania State University, USA, with class sizes ranging from 300 to 400 students. The readability scores for each microlearning category ensure that the material appropriately aligns with the student's reading capabilities, promoting effective content delivery and better learning outcomes. We will comprehensively evaluate student performance compared to previous semesters without microlearning. This evaluation will also assess the tool's impact on office-hour attendance, gathering feedback from students and instructors to measure its effectiveness. The thoroughness of our evaluation process will provide valid and reliable results.

Authors

Dr. Suman Saha Pennsylvania State University [biography]

Suman Saha received a Ph.D. degree in computer science from Pierre and Marie Curie University, Paris, France, in 2013.

He is an Assistant Teaching Professor at Pennsylvania State University, University Park, PA, USA. Since 2016, he has been involved in academia. Before his current role with Pennsylvania State University, he taught at Illinois Institute of Technology, Chicago, IL, USA, and Illinois State University, Normal, IL, USA.

Dr. Saha received the distinguished William C. Carter Award for his substantial contributions to dependable and secure computing during his doctoral studies. He has held positions at several prestigious National and International Research Labs, including Microsoft, Cambridge, U.K., Harvard University, Cambridge, USA, and the National Institute for Research in Digital Science and Technology (Inria), Paris, France.
Fatemeh Rahbari The Pennsylvania State University [biography]

Fatemeh Rahbari is currently pursuing an MSc in Computer Science and Engineering (CSE) at The Pennsylvania State University (Penn State).

With a research background spanning image processing and data privacy, Fatemeh's recent work focused on leveraging artificial intelligence (AI) for generating microlearning materials, including quizzes, flashcards, etc, from lecture videos using large language models (LLMs).

Fatemeh also serves as a graduate teaching assistant for the CSE department's Data Structures and Algorithms course. She is passionate about advancing her expertise in machine learning and data analytics and is eager to further contribute to these fields in her future career.
Farhan Sadique Kansas State University [biography]

Farhan Sadique is an Assistant Professor of Higher Education and Leadership at Kansas State University, specializing in microlearning, human resource development, and organizational leadership. He earned his Ph.D. in Workforce Education and Development from Pennsylvania State University. Farhan’s research focuses on innovative teaching strategies, including simulations and gamification, to enhance learner engagement. A recognized thought leader, he holds certifications from SHRM and NCDA and has been honored with the Emerging Practitioner Award by the Organization Development Network. Farhan fosters inclusive learning environments and advances educational practices through technology and research.
Sri Krishna Chaitanya Velamakanni Pennsylvania State University [biography]

Sri Krishna Chaitanya is a Software Engineer at Walmart, where he focuses on enterprise backend payment applications. He holds a Master’s degree in Computer Science from The Pennsylvania State University. His research interests lie at the intersection of Natural Language Processing (NLP), financial technology (fintech), and the applications of Large Language Models (LLMs).
Dr. Mahfuza Farooque http://orcid.org/https://0009-0006-1754-4524 Pennsylvania State University [biography]

Mahfuza Farooque is an Associate Teaching Professor in the School of Electrical Engineering and Computer Science at Pennsylvania State University. She earned her Ph.D. in Computer Science from École Polytechnique, France, specializing in proof search and automated reasoning. Her research interests include formal methods, large language models, and the application of machine learning in education and mental health.
Dr. Farooque has authored a textbook on discrete mathematics. She also serves as Director of the Master of Engineering program in Computer Science and Engineering. She regularly mentors undergraduate and graduate research projects that have received institutional recognition and funding.
William J. Rothwell Penn State University [biography]

William J. Rothwell, PhD, DBA, SPHR, SHRM-SCP, RODC, FLMI, CPTD Fellow is a Distinguished Professor at Penn State, and is a leading expert in workforce development. With 300+ publications, including 170 books, and a legacy of top-ranked programs, he has profoundly shaped the future of vocational education and development.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025

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