How Does AI Literacy Relate to Pre-Service STEM Teachers’ AI Readiness? Insights from Mediational Analysis

Abstract

Efforts to achieve widespread applications of AI in education depend, to a large extent, on teachers’ level of AI literacy (AIL), AI self-efficacy (AISE), Artificial Intelligence Technological Pedagogical Content Knowledge (AI-TPACK), and above all, the teachers’ readiness for AI-based learning. This study, therefore, examined AI literacy’s links with pre-service STEM teachers’ AI readiness (AIR), focusing majorly on the mediational roles of AISE and AI-TPACK. A quantitative research approach, specifically a cross-sectional correlational survey, was employed and was led by six null hypotheses. This study involved 310 final-year pre-service STEM teachers who participated using a volunteer sampling approach from five public universities in southeastern Nigeria. Four adapted structured scales featuring AIL, AIR, AISE, and AI-TPACK served as the data collection tools. The scales showed adequate evidence of construct validity and reliability (Cronbach’s alpha ≥ 0.79, and AVE ≥ 0.50) for all the scales. “Statistical Package for the Social Sciences (SPSS) PROCESS version 5.0” was used for the data analysis. The findings indicated that AIL, AISE, and AI-TPACK have significant direct relationships with AIR. Additionally, AISE and AI-TPACK play significant complementary partial mediation roles in the link between AIL and AIR. The findings highlight the need to redesign Teacher Education Programs (TEPs), focusing mainly on hands-on activities where pre-service STEM teachers practiceusing specific AI tools to solve STEM problems, such as using machine-learning algorithms to model climate data in a physics lesson.