TPACK-based AI integration in Kazakh language teacher education: evidence from a controlled pedagogical experiment

Abstract

Pre-service teacher education in Kazakhstan faces a persistent gap between digital infrastructure investment and teachers’ practical ability to integrate artificial intelligence (AI) and digital tools into Kazakh language instruction. This study examined whether a technological, pedagogical, and content knowledge (TPACK)-aligned specialist course could simultaneously improve TPACK among pre-service primary school teachers. A quasi-experimental pretest–posttest control-group design was employed with 123 third-year pre-service teachers (experimental: n=62; control: n=61) at two universities in Shymkent, Kazakhstan. The Dubovitskaya diagnostics of educational motivation orientation scale and a researcher-developed digital competence survey were used to assess outcomes. Between-group differences were analyzed using the independent-samples student’s t-test (α=0.05), with effect sizes reported via Cohen’s d. The intervention produced statistically significant, large-effect improvements in professional motivation (t=6.84, p<0.001, d=1.24), research activity (t=7.21, p<0.001, d=1.31), and digital competence application (40%→80%, d=0.97). Structured, TPACK-integrated practice-based learning — rather than digital exposure alone — meaningfully enhances pre-service teachers’ readiness for AI-integrated Kazakh language instruction. A context-specific AI integration model is proposed for curriculum designers and policymakers.