METHODOLOGY FOR DEVELOPING STUDENTS' PROFESSIONAL COMPETENCIES THROUGH ARTIFICIAL INTELLIGENCE-BASED ADAPTIVE LEARNING TOOLS
Keywords:
Artificial intelligence; adaptive learning; professional competence; higher education; learning analytics; intelligent tutoring system; formative feedback; human-centred AIAbstract
Artificial intelligence-based adaptive learning tools are increasingly used to diagnose learners' prior knowledge, recommend resources, adjust task difficulty, generate feedback, and analyse learning traces. However, the availability of such functions does not automatically produce professional competence, because competence is demonstrated through integrated performance in authentic situations rather than through isolated test scores. This article develops a methodology for using artificial intelligence-based adaptive learning tools to support the development of university students' professional competencies. The study is theoretical and design-oriented. It combines conceptual analysis, competence mapping, pedagogical modelling, and synthesis of research on adaptive learning, intelligent tutoring systems, learning analytics, formative feedback, self-regulated learning, and human-centred artificial intelligence. The results are presented as an eight-stage adaptive cycle: competency profiling, multisource diagnosis, construction of an explainable learner model, design of an individual pathway, performance of authentic professional tasks, AI-supported feedback, reflection in an electronic portfolio, and competency assessment with subsequent recalibration. Six components of professional competence are operationalised: motivational-value, cognitive-professional, operational-digital, communicative-collaborative, reflective-analytical, and innovative-ethical. The scientific novelty lies in connecting adaptive recommendations with professional tasks, triangulated evidence, teacher orchestration, explainability, data minimisation, and ethical safeguards. The proposed methodology can guide the design of LMS courses, simulations, intelligent tutoring systems, and generative AI assistants, while future studies should validate its effectiveness through discipline-specific pedagogical experiments.
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