Transforming Computing Education in Africa: A Scoping Review of the Potential of Generative AI to Enhance Higher-Order Thinking Skills in Programming Education
This scoping review examines how Generative AI (GenAI) can support higher-order thinking skills (HOTS) in programming education across African higher education institutions. Guided by Arksey and O’Malley’s scoping review framework and PRISMA-ScR principles, the review synthesised 11 studies published between 2020 and 2025. The findings show that GenAI tools, including ChatGPT, GitHub Copilot, Microsoft Copilot, Google Gemini, Codey, Kwame, AutoGrad, SuaCode, and Brilla AI, can support problem-solving, critical thinking, creativity, abstraction, programming logic, computational thinking, and metacognitive reflection. These benefits were most evident when GenAI was embedded in structured pedagogical designs such as project-based learning, problem-based learning, guided discovery, constructivist learning, prompt engineering, reflective learning, collaborative learning, and assessment-redesign approaches. In evidence from the synthesised studies, GenAI functioned most effectively as a learning scaffold when students were required to question, test, debug, compare, justify and reflect on AI-generated outputs. Conversely, weakly structured use risked surface-level engagement, over-reliance, plagiarism, and limited conceptual understanding. The review also identified contextual barriers to adoption, including unreliable internet access, device constraints, subscription costs, uneven faculty readiness, limited institutional policies, academic integrity concerns, and assessment validity challenges. Culturally and linguistically responsive GenAI integration emerged as a critical need, particularly in multilingual African learning environments where Western-centric datasets, examples, and pedagogical assumptions may not fully reflect local realities. This review suggests that GenAI has significant potential to enhance HOTS in African programming education, but sustainable and equitable integration requires AI-resilient assessment, faculty professional development, institutional policy guidance, locally relevant tools, and further longitudinal, experimental, classroom-based, and faculty-centred research.