Ai-Powered Intelligent Tutoring Systems: A Review Of Learner Modelling, Adaptive Instruction And Conversational Tutors

Intelligent Tutoring Systems (ITS) aim to give every student the benefits of one-to-one tutoring by using Artificial Intelligence to understand individual needs, abilities and progress. This paper reviews AI-powered ITS. It explains the classic architecture of domain, learner, pedagogical and interface models, and surveys learner modelling through knowledge tracing with Bayesian and deep recurrent approaches, adaptive selection of content and practice, automated feedback and hints, and conversational tutors from early dialogue systems to tutors built on large language models. Evidence on effectiveness is examined, including meta-analyses comparing ITS with human tutoring and classroom instruction, and recent studies of language-model-generated hints. A five-layer AI tutoring architecture is proposed, comprising a content and domain layer, a learner model, an adaptive engine, an AI chatbot tutor and a teacher and parent dashboard with progress reports, quizzes and practice tests. Research gaps, objectives, a comparison of tutoring approaches and challenges including accuracy of generated explanations, over-reliance, privacy, equity of access and multilingual support are discussed. The review concludes that AI tutors can deliver personalized, interactive and effective learning when designed with sound pedagogy and teacher involvement.