From Tool Overload to Instructional Alignment: Developing Pre-Service Teachers’ Digital and AI Teaching Competence

This qualitative case study investigates how EdTech and generative AI were integrated into English microteaching to develop pre-service teachers’ digital and AI teaching competencies, with particular attention to instructional alignment among learning goals, activities, and assessment. The study was conducted in an elective teachereducation course titled “Digital·AI Teaching Competency Development,” redesigned from a conventional microteaching practicum. Nineteen pre-service English teachers designed and delivered English-medium microteaching lessons incorporating EdTech and generative AI, participated in rehearsal-based feedback sessions, and completed reflective journals and semi-structured interviews. Data included lesson plans, teaching artifacts, recorded lessons, reflections, and interview transcripts. Qualitative coding and cross-case comparison identified three findings. First, generative AI functioned primarily as linguistic and structural scaffolding during preparation. Second, tools such as Padlet and Kahoot were selected in the enactment phase because they supported learner participation and formative assessment. Third, rehearsal-based feedback corrected tool-centered designs and restored alignment among learning goals, communicative tasks, and assessment. The findings suggest that the educational value of digital tools depends not on the number of technologies used but on their contribution to coherent instructional design. Implications are discussed for practice-based teacher education that emphasizes alignment-oriented planning and iterative feedback cycles.