AI-TPACK Competence and Pedagogical Knowledge Gaps Among Preservice Luganda Language Teachers: A Baseline Assessment in Ugandan Teacher Education

The integration of artificial intelligence into indigenous language teacher education presents both opportunities and challenges, particularly in resource-constrained contexts that implement competency-based curricula. This study assessed baseline AI-Technological Pedagogical Content Knowledge (AI-TPACK) competence and identified pedagogical knowledge gaps among 26 preservice Luganda teachers at a Ugandan university. Using a convergent mixed-methods design, data were collected through AI-TPACK surveys, semi-structured interviews, focus group discussions, and document analysis. Quantitative findings revealed moderate overall AI-TPACK competence with significant disparities between foundational knowledge domains and integrated competencies. AI-TPACK integration scored lowest, while Content Knowledge was highest. Qualitative analysis identified five major pedagogical knowledge gaps: limited understanding of genre-based pedagogy, a superficial grasp of functional grammar principles, an absence of Focus-on-Form strategies, a lack of systematic assessment approaches for competency-based instruction, and no prior experience with AI integration in language teaching. These compound deficiencies reveal that preservice teachers possess isolated knowledge components but struggle with the integration required for effective technology-enhanced, competency-based indigenous language instruction. The findings indicate an urgent need for comprehensive pedagogical frameworks that simultaneously develop content, pedagogical, and technological knowledge in an integrated rather than isolated fashion. Implications for indigenous language teacher preparation programs and curriculum reform are discussed.