Microlearning and Generative AI in Online Education: Content Fragmentation, Sequencing, and Adaptation for Student-Centered Learning

• Paulo J. A. Gimenez Federal University of the State of Rio de Janeiro http://orcid.org/0000-0003-3136-271X
• Sean W. M. Siqueira Federal University of the State of Rio de Janeiro https://orcid.org/0000-0002-0864-2396

DOI: https://doi.org/10.5753/cbie_estendido.2024.244295

Abstract

This study proposes a framework for applying microlearning in online educational environments using generative AI. We explore a gap in the literature regarding the optimization of educational content in concise and focused formats. We propose a solution that fragments, organizes, sequences, and adapts educational content to meet individual student needs, considering cognitive load and topic complexity. The research methodology will be Design Science Research (DSR), involving the development and evaluation of the framework application through experiments with students. It is expected that the results will advance online education, providing insights for designing more effective and efficient learning experiences.

Keywords: Microlearning, Generative AI, Content Fragmentation, Pedagogical Sequencing, Online Education

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