Gender-Sensitive Predictive Modelling of AI Adoption in Kenya’s Junior Secondary Schools Under the CBC Framework

Abstract

Kenya’s Competency-Based Curriculum (CBC) emphasizes creativity, critical thinking and learner-centered pedagogy but implementation disparities persist across rural and urban schools due to unequal access to infrastructure, trained educators and digital tools. This study proposes a gender-sensitive and Artificial Intelligence (AI)-driven education model platform to support equitable CBC delivery at the junior secondary level. The model platform incorporates AI tools for adaptive learning and interactive instruction. Using a mixed-methods approach we assess how contextual factors such as digital access, ICT infrastructure, teacher preparedness and location affect AI adoption. Quantitative analysis was conducted using ordinal logistic regression and gender-disaggregated comparisons to evaluate adoption patterns and usability perceptions. Data were drawn from two purposively selected junior secondary schools in contrasting settings. This study revealed that digital access and teacher preparedness are significant predictors of AI adoption (p < 0.05), location shows a marginal effect (p ≈ 0.06) while ICT infrastructure and gender were not statistically significant. These insights suggest that strengthening teacher capacity and improving digital access are critical to advancing AI integration under the CBC framework. These results inform inclusive EdTech design and policy strategies aimed at closing regional and gender-related gaps in AI-driven learning.