A SYSTEMATIC REVIEW: EMPLOYING AI IN ADAPTIVE LEARNING RECOMMENDATION SYSTEM FOR VOCATIONAL EDUCATION

The integration of AI in vocational education, especially in adaptive learning, highlights the importance of automatically detecting individual learning styles. Traditional methods such as questionnaires, though reliable, face limitations like student reluctance and lack of self-awareness. This reveals a research gap in learning style detection, particularly in AI-based adaptive systems, requiring further exploration of effective computational techniques in real-world educational contexts. These challenges are especially relevant to the development of adaptive learning-based recommendation systems for career selection. Accurate learning style detection is essential not only for personalized learning but also for aligning educational content with potential career paths, thereby enhancing both academic and career outcomes according to talent, interests, and major. To address these gaps, this study presents a systematic review of 40 selected articles published between 2014 and 2025. The review examines techniques, approaches, and computational strategies used in automatic learning style detection and their implementation in various vocational educational settings. Findings show that AI, particularly data-driven approaches, significantly supports learning adaptation. The Felder–Silverman model and classification techniques like K-Means and Naive Bayesian are commonly applied due to their adaptability across contexts. Moodle also emerges as a frequently used platform for data collection and experimentation. These insights are fundamental for designing intelligent recommendation systems that adapt to student’s learning styles and support personalized career guidance. Integrating such systems can enhance educational relevance, improve learning outcomes, and foster long-term career readiness.