A Research Analysis Of Intelligent Tutoring Systems Based On Generative Artificial Intelligence And Adaptive Learning Approaches
DOI:
https://doi.org/10.63665/fab54378Keywords:
Intelligent Tutoring Systems, Generative Artificial Intelligence, Adaptive Learning, Large Language Models, Personalized EducationAbstract
The integration of Generative Artificial Intelligence (GenAI) into Intelligent Tutoring Systems (ITS) has opened a transformative pathway for personalized, adaptive education. This study examines how GenAI-powered ITS influence learning outcomes across educational levels, focusing on cognitive performance improvement and embedded adaptive mechanisms. The research objectives are: (1) to evaluate GenAI-ITS effectiveness on student learning outcomes, and (2) to analyze adaptive learning mechanisms embedded within GenAI-driven platforms. A secondary research design was employed, synthesizing meta-analyses, randomized controlled trials, and systematic reviews published between 2016 and 2025. The hypothesis posits that GenAI-integrated ITS yield significantly superior academic outcomes compared to conventional instruction. Findings from five verified data tables reveal effect sizes ranging from d = 0.66 to g = 0.76, statistically significant pre/post-test gains (p < 0.001) in problem-solving and critical thinking, and GenAI academic adoption exceeding 60% among higher education students globally in 2023–2024. The study affirms that adaptive feedback, LLM-based interactivity, and personalized learning pathways drive these outcomes, while underscoring ethical and equitable implementation imperatives for responsible deployment.
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