Challenges, Prospects and Readiness for AI-Powered Personalised Learning in Science Teacher Education: Evidence from Jigawa State College of Education, Gumel

Abstract

This study examined the challenges, prospects and readiness for AI-powered personalised learning in science teacher education at Jigawa State College of Education, Gumel. A cross-sectional descriptive survey was conducted among 50 science lecturers and 201 final-year science student-teachers, supported by an institutional ICT audit and frequency analysis of short open-ended responses. Internal consistency was good to excellent across the ten construct scales (Cronbach’s α = .85–.93). Mean scores for awareness, readiness and perceived barriers were above the scale midpoint in both groups, whereas institutional support among lecturers (M = 2.30, SD = 0.86) and institutional access among students (M = 2.34, SD = 0.84) were below it. Awareness was not significantly associated with readiness among lecturers, r(48) = .135, p = .350, or student-teachers, r(199) = −.002, p = .979. The ICT audit yielded 35 of 54 points (64.8%), with notable deficiencies in internet and Wi-Fi access, staff computers and e-learning provision; no responsible AI policy was identified. Open-ended responses indicated interest in AI for access to scientific information, simulations and personalised learning, alongside concerns about cost, infrastructure, technical support and training. Overall, the findings suggest that interest in AI is present, but its responsible integration is constrained by institutional readiness.