GENERATIVE ARTIFICIAL INTELLIGENCE FOR MICROLEARNING CONTENT DEVELOPMENT: OPPORTUNITIES, CHALLENGES, AND PEDAGOGICAL FRAMEWORKS

This narrative literature review explores how Generative Artificial Intelligence (Gen AI) is used in microlearning content development, the opportunities and risks emerging from this convergence, and the pedagogical frameworks that support instructional quality and ethics. Literature was identified through structured and iterative Google Scholar searches (2018–2025) using keywords spanning Gen AI, microlearning, and content development. After two-stage screening (title–abstract and full-text), 20 core studies published in 2023–2025 were analyzed in depth, with earlier sources used only for conceptual background. The core evidence was synthesized using thematic analysis with deductive (opportunities, challenges, frameworks) and inductive coding. Findings indicate that Gen AI supports key development tasks, including learning objective formulation, outlining and storyboarding, multimodal content production, and micro-assessment generation. Opportunities include faster and more scalable production, expanded multimodal formats, and stronger support for personalization and accessibility. Key risks include inaccuracies and hallucinations, “false authority” effects in concise units, oversimplification and fragmented learning, tool/workflow fragmentation, and ethical concerns related to bias, copyright, transparency, and data privacy. The reviewed literature points to an eclectic, theory-informed approach (grounded in cognitive and multimedia learning principles, instructional design models, microlearning-specific frameworks, and inclusive design) to guide responsible Gen AI use.