Empowering pre-service teachers with generative artificial intelligence and microlearning: pathways to self-directed growth

Abstract

This study explores the impact of generative artificial intelligence (GenAI) tools and microlearning strategies on the professional development of pre-service language teachers in Hong Kong. Grounded in self-regulated learning (SRL) and self-directed professional development (SP-PD), the research explores how teachers develop technological and pedagogical competencies through flexible, informal learning. Through semi-structured interviews with 14 pre-service teachers, the study identifies four key themes: navigating GenAI with curiosity, microlearning as a practical scaffold, integrating SRL strategies, and reimagining teacher identity and agency. The findings reveal that participants valued the accessibility and responsiveness of GenAI and microlearning but faced cognitive and emotional challenges, particularly without institutional support. Notably, GenAI tools functioned not only as instructional aids but also as co-regulators of learning, facilitating goal-setting, feedback, and reflection. By positioning GenAI as an active agent in teacher learning, this study contributes to the field and advocates for integrating AI literacy, SRL, and ethical frameworks into teacher education. The study proposes reimagining professional development to strike a balance between autonomy and support and between innovation and pedagogical integrity in AI-enhanced educational contexts.