From One-Size-Fits-All to Every Learner: Leveraging AI for Inclusive and Adaptive Lesson Planning

Despite widespread recognition that differentiated instruction benefits all learners, practical realities of teaching often lead to "teaching to the middle." AI tools offer potential to address this persistent challenge by enabling more efficient creation of differentiated materials. This qualitative study examines how student-teachers at an Australian university utilize AI tools to develop inclusive lesson plans during their initial teacher education. Using semi-structured interviews with 20 student-teachers following their teaching placements, the study examines how they conceptualize and utilize AI for differentiation, the meanings they attribute to this technology within their developing practice, and the factors that facilitate or constrain their engagement. The research employs reflexive thematic analysis to understand participants’ experiences transforming AI-generated baseline content into differentiated materials for students across the ability spectrum. This ongoing research is expected to generate insights into the pedagogical and practical factors that shape AI use, contribute indicators for future research on AI engagement in educational settings, and inform policy discussions surrounding teacher preparation. By capturing the perspectives of student-teachers during this formative period, the research addresses a critical gap in understanding how emerging technologies can support beginning teachers in transitioning from one-size-fits-all instruction to inclusive teaching practices.