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Kylychbekovich, Arkabaev Nurkasym
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Computer Science & IT Education

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A Conceptual Model for Integrating Machine Learning Competencies into Technical Higher Education: Development and Pilot Validation in a Central Asian Context Kylychbekovich, Arkabaev Nurkasym
Journal of Applied Artificial Intelligence in Education Vol 2, No 2 (2027): January 2027
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i2.429

Abstract

The integration of machine learning (ML) competencies into undergraduate technical education remains methodologically underdeveloped, particularly in higher education systems of developing economies undergoing digital transformation. This study aimed to develop and empirically validate a conceptual model for integrating ML competencies into technical higher education programmes. The model was constructed through the synthesis of systemic, competency-based, and activity-based pedagogical approaches and comprises four interrelated components: target, content, procedural, and evaluative-outcome. A quasi-experimental pilot study was conducted at Osh State University (Kyrgyz Republic) involving 54 second-year students of specialty 710100 "Computer Science and Engineering," assigned to an experimental group (n=28) and a control group (n=26). Group differences were assessed using an independent-samples t-test. Students in the experimental group achieved significantly higher scores in theoretical knowledge (82.4% vs. 71.3%; p<0.05), practical skills (85.7% vs. 68.9%), and reported substantially higher learning motivation (89% vs. 54%). These findings suggest that embedding ML components within a structured, modular instructional model improves both subject-matter competency and student engagement, indicating the model's potential for broader adoption in technically oriented higher education systems pursuing digital transformation
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