DEVELOPING DIGITAL COMPETENCES IN ENGINEERING STUDENTS THROUGH ARTIFICIAL INTELLIGENCE AND AUTOMATION: FRAMEWORK DESIGN AND PRELIMINARY IMPLEMENTATIONautomation: framework design and preliminary implementation

T. S. Zhylkybayev, Alma F. Dautova, T. A. Ustinova, Dana Zhumasheva, Umidjon Z. Djumayozov

2026

Full text Open access: Hybrid Metadata (CSL-JSON)
DOI 10.53360/3135-8160-2026-3(7)-2
Data confidence 70 medium
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Abstract

This article presents a practice-oriented framework for developing digital competences among engineering students through the integration of artificial intelligence and automation technologies into the educational process. The relevance of the study is determined by the digital transformation of higher engineering education, the rapid development of intelligent technologies, and the growing demand for specialists capable of applying digital tools to professional tasks in automation and control. The study aimed to design and conduct a preliminary classroom implementation of a framework that combines artificial intelligence, automation tools, practice-oriented learning activities, and project-based learning. The study was conducted within the 6B07104 Automation and Control educational programme and involved 51 students from the AU-2402, AU-2401, and AU-301 groups. The methods included a review of academic and methodological literature, pedagogical observation, pedagogical modelling, analysis of learning activities, and criterion-referenced assessment. A staged framework was developed comprising diagnostic, motivational and theoretical, practice-oriented, project-based, and reflective and evaluative stages. Five criteria were proposed for assessing students’ digital competences: information and analytical, instrumental and technological, project-based and practical, ethical and legal, and reflective. The preliminary implementation demonstrated the practical applicability of the proposed framework in engineering education and helped identify appropriate learning activities, assessment criteria, and recurring student difficulties. The framework may be applied in modules related to automation, control, programming, data analysis, digital modelling, and artificial intelligence.