From Knowledge to Intention: Developing and Validating a Scale for K-12 Teachers' Readiness to Teach AI

The growing integration of artificial intelligence (AI) into society has increased demand for K-12 AI education, placing information technology (IT) teachers at the forefront of curriculum delivery. However, validated instruments assessing IT teachers' readiness and motivation toward teaching AI remain scarce. This study developed and validated the Readiness to Teach AI and Behavioral Intention Scale (RTAI-BIS), grounded in the Technological Pedagogical Content Knowledge (TPACK) framework and the Theory of Planned Behavior (TPB). An initial pool of 294 items was refined through literature review, expert panel evaluation (n = 6), and pilot testing, yielding a 45-item candidate scale. Exploratory Factor Analysis (EFA) with 392 IT teachers in Turkish schools yielded a five-factor structure explaining 73.79% of variance. Confirmatory Factor Analysis (CFA) with an independent sample (n = 258) validated a refined four-factor, 26-item model with acceptable fit (Comparative Fit Index [CFI] = .943, root mean square error of approximation [RMSEA] = .079, standardized root mean square residual [SRMR] = .061). The final four factors are Technological, Pedagogical, and Content Knowledge for Teaching AI (TPACK-TAI), Attitude Toward AI (ATA), Technological Knowledge for Teaching AI (TK-TAI), and Disposition Toward Teaching AI (DTAI), with two knowledge factors capturing related but distinguishable aspects of instructional readiness. Reliability analyses demonstrated strong internal consistency (Cronbach's α = .921–.974). The RTAI-BIS offers a psychometrically sound tool for assessing IT teachers' readiness to teach AI and their behavioral intentions, with applications in teacher training and curriculum implementation.