Principles of GenAI-assisted microlearning: perspectives of pre-service language teachers

Abstract This qualitative research explored pre-service language teachers’ perceptions of generative artificial intelligence (GenAI)-assisted microlearning in an ELT technology integration class. In semi-structured interviews, 14 Hong Kong pre-service teachers reflected on their engagement with adaptive, multimodal microlearning activities designed to minimize cognitive load. They valued GenAI’s personalized, real-time adaptability, interactivity, and instant feedback. However, they expressed concerns regarding learner autonomy, over-reliance on AI scaffolding, and content accuracy and transparency. They emphasized the critical role of human teachers in mediating and contextualizing GenAI use. Four pedagogical principles for effective AI-supported microlearning were identified: real-time adaptability, multimodal interactivity, learner customization, and reliability and accuracy. These principles reflect the unique affordances of the microlearning format—namely, that it is short, focused, and repeatable—rather than general GenAI features. This study provides suggestions for educators and content creators wishing to integrate GenAI thoughtfully into microlearning-based ELT.