Publication Date

2-17-2026

Document Type

Article

Publication Title

SIGCSE TS 2026 Proceedings of the 57th ACM Technical Symposium on Computer Science Education V 1

DOI

10.1145/3770762.3772632

First Page

1075

Last Page

1081

Abstract

While global interest in K–12 AI and ML education grows, many African education systems lack foundational computing education beyond basic computer literacy. This creates unique challenges for AI integration in countries where computer science isn’t part of the K–12 curriculum. Teachers are central to this effort, but little is known about what motivates them to engage with these technologies or how they use them. This study examined what motivates K–12 teachers to engage with AI and ML in Botswana. Using a mixed-methods approach, we surveyed 59 teachers using an adapted version of the Motivation to Teach Computer Science (MTCS) scale and open-ended questions. We used Self-Determination Theory (SDT) as a lens to interpret the findings. Results showed that intrinsic motivation and identified regulation were primary drivers. Context-specific, extrinsic factors were also observed, including a desire to improve educational systems and concerns about infrastructure in schools. Access disparities in teachers’ use of AI emerged: secondary and computing teachers with better infrastructure used AI tools more frequently than primary or non-computing teachers. The results show that while teachers’ engagement with AI stems from perceived teaching and learning value, sociocultural factors like infrastructure determine how motivation translates into practice. These findings have implications for professional development, infrastructure planning, and inclusive AI adoption in resource-constrained education systems.

Keywords

artificial intelligence, K-12 education, teacher motivation

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Computer Science

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