Developing pre-service EFL teachers’ professional competences for digital adaptive learning: a quasi-experimental validation of a training model

Ainur Zhorabekova, Moldir Kurmasheva, Assel Yesnazar, Artyk Nuralieva

Frontiers in Education, 2026

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DOI 10.3389/feduc.2026.1902394
Data confidence 70 medium
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Abstract

The growing integration of artificial intelligence into language education necessitates a corresponding evolution in teacher preparation, yet existing competence models remain largely unvalidated within the specific context of digital adaptive learning. This study designed and experimentally validated a training model for developing professional competence among pre-service EFL teachers, comprising four interrelated dimensions: conceptual, digital, methodical, and organizational. Using a quasi-experimental pre-test/post-test control group design, 106 third-year pre-service EFL teachers (experimental, n = 54; control, n = 52) participated in a 15-week intervention. The experimental group engaged in an elective course structured around the proposed model, while the control group followed conventional methodology training. Competence was assessed across motivational, cognitive, and performance domains using validated instruments. Mixed ANOVA revealed significant Time × Group interaction effects favouring the experimental group across all dimensions: motivation [F(1,104) = 24.82, p < .001, η 2 = .19, d = 0.97], cognitive competence [F(1,104) = 111.34, p < .001, η 2 = .52, d = 2.05], and instructional performance [F(1,104) = 228.34, p < .001, η 2 = .69, d = 2.94]. These findings provide empirical validation for a training model, demonstrating that structured, competence-based preparation is associated with significantly enhanced pre-service teachers’ readiness to integrate adaptive technologies into language instruction. The study contributes both a replicable pedagogical framework and evidence of its effectiveness for teacher education programmes responding to AI-mediated educational contexts.