Assessment of Physics Lecturers’ Readiness to Adopt Artificial Intelligence in Teaching Practices in Colleges of Education in North-East Nigeria

Through intelligent tutoring systems, adaptive learning technologies, automated assessment platforms, predictive analytics, and virtual learning environments, artificial intelligence (AI) is revolutionizing educational systems around the world. The degree to which educators are prepared to incorporate new technologies into their teaching and learning processes will determine how well AI integration works in the classroom. This study evaluated the preparedness of physics instructors in North-East Nigerian colleges of education to incorporate AI into their lesson plans. The study had three main goals: to find out how prepared physics lecturers were to accept AI, to identify the elements that influence lecturers’ readiness for AI integration, and to look at obstacles to AI adoption in physics teaching practices. A descriptive survey research design was used in the study. Lecturers in physics from North-East Nigerian colleges of education made up the population. Using stratified random sampling procedures, a sample of 120 physics instructors was chosen. The Physics Lecturers’ Artificial Intelligence Readiness Questionnaire (PLAIRQ), a tool created by the researcher, was used to gather data. Using Cronbach Alpha, the instrument produced a reliability coefficient of 0.86. Data analysis was done using the mean and standard deviation. The results showed that physics instructors were moderately prepared to adopt AI (Grand Mean = 3.12). Readiness levels were strongly impacted by professional development, digital competency, and institutional support (Grand Mean = 3.34).Inadequate infrastructure, a lack of AI training, and a lack of institutional support were the main obstacles to adoption (Grand Mean = 3.41). The study found that while infrastructure constraints and insufficient capacity development continue to be obstacles to implementation, physics instructors show a positive willingness toward AI integration. The study suggested institutional AI policy development, enhanced technology infrastructure, and ongoing AI training programs.