IJIET 2026 Vol.16(5): 1406-1417
doi: 10.18178/ijiet.2026.16.5.2607
doi: 10.18178/ijiet.2026.16.5.2607
Integration of SAMR, TPACK, and Artificial Intelligence in Computer Science Teacher Education: Impact on Academic Achievement and Digital Competencies
Elmira Abdrashova1, Zhanar Kemelbekova1, Gulbakhram Beissenova1, Aliya Utеbayeva2,
and Zhanat Umarova3,*
1. Computer Science Department, Natural Sciences Faculty, Auezov South Kazakhstan University, Shymkent, Kazakhstan
2. General Education disciplines Department, Engineering and Technology Faculty, Regional Innovation University, Shymkent, Kazakhstan
3. Information systems and Modeling Department, Information Technologies and Energetics Higher School, Auezov South Kazakhstan University, Shymkent, Kazakhstan
Email: emon_81@mail.ru (E.A.); kemel_zhan@mail.ru (Z.K.); gulia-74-74@mail.ru (G.B.); anara0969@mail.ru (A.U.); zhanat-u@mail.ru (Z.U.)
*Corresponding author
2. General Education disciplines Department, Engineering and Technology Faculty, Regional Innovation University, Shymkent, Kazakhstan
3. Information systems and Modeling Department, Information Technologies and Energetics Higher School, Auezov South Kazakhstan University, Shymkent, Kazakhstan
Email: emon_81@mail.ru (E.A.); kemel_zhan@mail.ru (Z.K.); gulia-74-74@mail.ru (G.B.); anara0969@mail.ru (A.U.); zhanat-u@mail.ru (Z.U.)
*Corresponding author
Manuscript received September 12, 2025; revised November 10, 2025; accepted December 17, 2025; published May 22, 2026
Abstract—The rapid digital transformation of teacher education requires instructional models that integrate established pedagogical frameworks with emerging Artificial Intelligence (AI) tools. This study investigates the effectiveness of a unified Substitution, Augmentation, Modification, Redefinition-Technological Pedagogical Content Knowledge-Artificial Intelligence (SAMR–TPACK–AI) framework for enhancing learning outcomes among pre-service computer science teachers. A quasi-experimental design was implemented with over 150 participants across control and experimental groups, using validated instruments and mixed-methods analysis (Analysis of Covariance (ANCOVA), Linear Mixed Model (LMM)). Findings demonstrate significant gains in the experimental group: academic performance increased by 12.2 percentage points, motivation by 0.52 points, and digital competencies by 14.9 points. Cross-disciplinary outcomes also improved, including critical thinking and teamwork skills. The results provide empirical validation of the integrated SAMR–TPACK–AI framework as an effective digital learning design. The study offers a novel conceptual contribution to AI-enhanced digital pedagogy and provides practical implications for developing sustainable, future-oriented teacher education programs.
Keywords—Substitution Augmentation Modification Redefinition (SAMR), Technological Pedagogical Content Knowledge (TPACK), artificial intelligence, digital competencies, teacher education, computer science
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Keywords—Substitution Augmentation Modification Redefinition (SAMR), Technological Pedagogical Content Knowledge (TPACK), artificial intelligence, digital competencies, teacher education, computer science
Cite: Elmira Abdrashova, Zhanar Kemelbekova, Gulbakhram Beissenova, Aliya Utеbayeva, and Zhanat Umarova, "Integration of SAMR, TPACK, and Artificial Intelligence in Computer Science Teacher Education: Impact on Academic Achievement and Digital Competencies," International Journal of Information and Education Technology, vol. 16, no. 5, pp. 1406-1417, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).