Intelligent Tutoring Systems for Adaptive and Personalized Learning in Vocational Education: A Systematic Literature Review
DOI:
https://doi.org/10.12928/biste.v8i4.16270Keywords:
Systematic Literature Review, PRISMA, Intelligent Tutoring System, Artificial Intelligence in Education, Learning Analytics, Educational Data Mining, Vocational EducationAbstract
Rapid advances in Artificial Intelligence (AI) have driven the growth of Intelligent Learning Systems (ITS) that support adaptive and personalized learning, however, the implementation of ITS in vocational education remains limited and has not been fully integrated into real-world, competency-based training environments. The research contribution of this study is threefold: a comprehensive synthesis of recent AI-based ITS research, a quantified account of the persistent research gap in vocational-education applications, and a conceptual framework for implementing ITS in vocational settings that integrates competency-based content, dual cognitive-psychomotor student modelling, and simulation-based pedagogy. This study applies a Systematic Literature Review guided by the PRISMA protocol. From 300 articles identified across major scientific databases, a staged process of screening, eligibility assessment, and quality assessment yielded 30 articles published between 2019 and 2025 for final analysis. The findings show a sharp increase in ITS publications, with 46.7% published in 2025 alone, driven largely by the adoption of generative AI and large language models. By research type, 50% were systematic reviews, 23.3% empirical studies, 13.3% technology-development studies, and 13.3% conceptual studies. Most studies (63.3%) were conducted in general education contexts, while only 6.7% specifically addressed vocational or workforce-based education, confirming a clear and persistent research gap. ITS was found to effectively support adaptive learning through dynamic content adjustment, personalized learning pathways, real-time feedback, and data-driven performance prediction enabled by the integration of Learning Analytics and Educational Data Mining. AI-based ITS holds substantial potential to enhance learning effectiveness, but further empirical research is needed to validates its implementation in vocational education, particularly through the conceptual framework proposed in this study.
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