DEVELOPMENT OF DIGITAL COMPETENCE OF FUTURE INFORMATICS TEACHERS THROUGH ARTIFICIAL INTELLIGENCE

Abstract

Artificial intelligence (AI) has been rapidly adopted in the educational system, which has transformed the digital competence standards for future informatics teachers. The concept of digital competence in this study is approached as a multidimensional construct, consisting of six dimensions from the European Framework for the Digital Competence of Educators (DigCompEdu) such as professional engagement, digital resources, teaching and learning, assessment, empowering learner and facilitating learner's competence. The effectiveness of the AI Integration Methodology – Five Stages (AIM-5) Model is assessed in the development of two dimensions of digital competence of future informatics teachers in Kazakhstan: AI-TPACK knowledge (cognitive-pedagogical dimension corresponding to the areas of DigCompEdu: digital resources and teaching and learning) and AI acceptance / behavioural intention to integrate AI in teaching (attitudinal-behavioural dimension, corresponding to the area of DigCompEdu: professional engagement, grounded on the Technology Acceptance Model). A quasi-experimental pre-test/post-test non-equivalent control group design with 31 first-year students of Zhetysu University named after

I. Zhansugurov was employed. The experimental group (EG, n = 15) participated in a fifteen-week AIM-5 programme delivered within the course «Introduction to Programming» while the control group (CG, n = 16) followed the standard curriculum. AI-TPACK knowledge was measured with a 20-item adapted scale (Cronbach’s α = 0.83) and AI acceptance with a validated 5-point Likert scale (α = 0.81) based on the Technology Acceptance Model. Post-test results indicate that the EG outperformed the CG in AI-TPACK knowledge (EG: M = 53.1, SD = 7.8; CG: M = 49.4, SD = 6.2; gain +10.3 vs. +6.2 points; t(29) = 1.47, p = 0.153, Cohen’s d = 0.53) and demonstrated a statistically significant advantage in AI acceptance (EG: M = 4.18, SD = 0.51; CG: M = 3.62, SD = 0.60; t(29) = 2.81, p = 0.009, Cohen’s d = 1.00). These findings indicate that the AIM-5 Model makes a meaningful contribution to the development of two core dimensions of the digital competence of future informatics teachers within the DigCompEdu architecture.