Design and implementation of an AI competency module for prospective teachers: A mixed-methods study

This study explores how a learning module grounded in the AI competency instructional model (Ng et al., 2023) facilitates prospective teachers' AI competencies and their AI literacy. Adopting a complementarity parallel mixed-methods design, the research was conducted over nine weeks with eleven undergraduate prospective teachers enrolled in an Instructional Technologies and Material Development course at a university in Türkiye. Quantitative data were collected via the Meta AI Literacy Scale (MAILS), and qualitative data included pre- and post-opinion forms and AI-integrated final projects. The findings revealed a statistically significant improvement in participants' AI literacy levels, particularly in the sub-domains of understanding, detecting, and creating AI. Although there was no significant change in the domains of AI ethics and apply AI, qualitative data indicated a notable shift in ethical awareness and reflections on AI's societal implications. Lesson plan analysis showed that participants successfully integrated AI into diverse subject areas, aligning the Five Big Ideas in AI and UNESCO's AI competency framework. However, assessment strategies for AI concepts remained limited. This study contributes to the literature by providing empirical evidence on how pedagogically informed modules can build AI competencies and guide meaningful integration of AI into instructional design. Implications for teacher education and recommendations for future research are discussed.