Abstract:
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.
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