Microlearning and generative AI for pre-service teacher education: a qualitative case study
Abstract
The rapid emergence of generative artificial intelligence (GenAI) tools has underscored the urgent need for pre-service teachers to develop technological pedagogical content knowledge (TPACK) and self-regulated learning (SRL) strategies – both critical for integrating AI into classrooms. However, existing teacher education programmes lack structured approaches to equip pre-service teachers with AI literacy and pedagogical adaptation skills. Traditional training models remain too generalised and fail to provide incremental, hands-on experiences for AI integration. This qualitative case study addresses these gaps by investigating the use of microlearning modules – bite-sized, multimodal instructional units – to enhance pre-service teachers’ TPACK and SRL in English language teaching (ELT). Over 13 weeks, 19 participants engaged with GenAI-focused microlearning modules that progressively developed their ability to adapt AI tools such as ChatGPT, Twee, Mizou, Perplexity and MagicSchool for differentiated instruction, formative assessment and culturally responsive teaching. Thematic analysis of participants reflective journals and semi-structured interviews revealed three key findings: (1) microlearning facilitated a structured, low-cognitive-load approach to developing GenAI competencies, (2) participants gained confidence and autonomy in using AI for lesson planning, and (3) SRL strategies such as goal setting and iterative refinement were essential for AI integration. Participants mitigated challenges such as GenAI tool limitations and initial AI anxiety by refining prompt engineering techniques and cross-validating AI outputs. These findings highlight microlearning’s potential to bridge AI literacy and pedagogical applications of AI, offering a scalable model for teacher education.
Link to publication in Springer Nature
