Enhancing Learner Engagement through Artificial Intelligence Integration in Ugandan Rural Primary Schools’ Science Classroom Instructional Practices

Purpose: This study examined the integration of Artificial Intelligence (AI) tools in science education in selected primary schools in Bududa District, Uganda. It focused on implications for teacher education and professional development within resource-limited contexts. Guided by constructivist and cognitive load theories, the research investigated how AI affects learner engagement, instructional practices, and classroom dynamics. Methodology: A mixed-methods approach was used, incorporating pupil interviews, teacher questionnaires, and classroom observations. This provided a comprehensive understanding of how AI tools were being adopted and experienced in real classroom settings. Findings: Artificial Intelligence (AI) tools such as educational simulations and interactive quizzes improved learner motivation, collaboration, and conceptual understanding. However, implementation was inconsistent due to inadequate digital infrastructure and limited teacher training. While many teachers expressed a willingness to adopt AI, they lacked the necessary digital skills and support systems to use these tools effectively. Unique Contribution to Theory, Practice and Policy: Theoretically, the study links AI-enhanced learning to constructivist and cognitive load principles in low-resource environments. In practice, it highlights the need for teacher education programs that develop AI literacy, pedagogical adaptability, and context-sensitive strategies. On a policy level, the study recommends revising teacher training curricula to include AI integration, alongside increased investment in digital infrastructure and professional development. These measures are critical for advancing equitable and effective science education in rural Ugandan schools.