Institutional Readiness for Integrating Generative Artificial Intelligence into Teacher Education: A Qualitative Study of Marymount Teachers College, Zimbabwe

Generative Artificial Intelligence (GenAI) is rapidly reshaping teacher education by enhancing instructional design, personalized learning, and professional development while simultaneously raising ethical and pedagogical concerns (Holmes et al., 2022; Luckin, 2021). Despite its transformative potential, there is limited empirical evidence on how teacher training institutions in Zimbabwe are integrating GenAI into their programmes. This qualitative study explored the integration of Generative Artificial Intelligence in teacher education at Marymount Teachers College, Mutare, Zimbabwe. Guided by the interpretivist paradigm, the study employed a qualitative research approach to examine participants' experiences and perceptions. Purposive sampling was used to select 8 lecturers, 15 final-year student teachers, and 2 college administrators. Data were collected through semi-structured interviews, focus group discussions, and document analysis and were analysed thematically following Braun and Clarke’s (2006) six-step framework. The findings revealed that GenAI tools are currently used informally, primarily to support lesson planning, content generation, and assignment preparation. Participants acknowledged the potential of GenAI to enhance pedagogical innovation, classroom simulations, and personalised feedback. However, concerns relating to academic integrity, data privacy, algorithmic bias, and inadequate institutional policies were identified as major barriers to effective adoption. The study concludes that while Marymount Teachers College demonstrates readiness to embrace AI-enhanced teacher education, successful integration requires institutional policies, curriculum reform, lecturer capacity building, ethical guidelines, and investment in digital infrastructure. The study contributes context-specific evidence to the growing discourse on AI in teacher education within developing countries and provides practical recommendations for the ethical and sustainable integration of GenAI into teacher preparation programmes.