Developing Students' Practical Skills in Artificial Intelligence Education Through a Competency-Based Approach

The rapid expansion of artificial intelligence (AI) across professional fields has intensified the need for educational approaches that move beyond the transmission of theoretical concepts toward demonstrable practical competence. This narrative review examines how competency-based education can be applied to the teaching of artificial intelligence in higher education, with particular attention to problem formulation, data literacy, algorithm selection, model evaluation, ethical reasoning, and collaborative project work. Drawing on research in competency-based education, experiential learning, computing education, and AI literacy, the article develops a structured instructional framework linking intended learning outcomes, authentic tasks, formative feedback, and performance-based assessment. The review argues that effective AI teaching requires the coordinated development of technical, methodological, and socio-ethical competencies. It also identifies implementation constraints, including unequal prior programming experience, limited computing resources, assessment validity, and the risk of excessive dependence on generative AI systems. The proposed framework is conceptual and requires empirical validation in specific institutional settings.