Global interest in integrating Artificial Intelligence (AI) and Machine Learning (ML) into K–12 education is rising, yet many education systems, particularly in Africa—face foundational gaps in computing education. In countries like [Country], where instruction is limited to basic digital literacy (e.g., Microsoft Office) and excludes coding or programming, introducing AI and ML presents unique challenges and opportunities. Teachers are central to this effort, yet little is known about what motivates them to engage with these technologies or how they use them.

This study investigates K–12 teachers’ motivation to teach and learn about AI and ML in [Country]. Using a mixed-methods approach that combined an adapted Motivation to Teach Computer Science (MTCS) scale with open-ended questions, we surveyed 59 teachers across educational levels. Quantitative results show that intrinsic motivation and perceived student benefit are key drivers, while external pressure plays a minimal role. Qualitative findings further highlight motivations such as gaining new knowledge, staying current with technology, and enhancing students’ digital literacy. Drawing on motivation theory, we found that teachers’ engagement with AI was driven by perceived value for teaching and learning, shaped by social and structural factors.

Overall, 73% of teachers reported using AI tools outside formal instruction, and 63% used them in educational settings. However, access disparities remained: secondary and computing teachers, with better infrastructure, used AI more frequently than those in under-resourced primary schools. We discuss implications for professional development, infrastructure planning, and equitable AI integration in low-resource systems.