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AI-Based Tutoring Systems in Education: A Systematic Literature Review on Personalized Learning, Intelligent Agents, and Learning Analytics
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Author(s): Abdulla Sultan Binhareb Almheiri (Zayed University, UAE), Humaid Albastaki (Zayed University, UAE)and Hanadi Alrashdan (Faculty of Education, Jadara University, Joran)
Copyright: 2026
Pages: 26
Source title:
Generators, Bots, and Tutors: Creative Approaches to Human-AI Synergy in Classroom Instruction
Source Author(s)/Editor(s): Bosede Iyiade Edwards (Universiti Sains Malaysia, Malaysia), Hassan Abuhassna (Sunway University, Malaysia), Damola Olugbade (University of South Africa, Pretoria, South Africa), Olayinka Anthony Ojo (University of Bolton, United Kingdom)and Wan Ahmad Jaafar Wan Yahaya (Universiti Sains Malaysia, Malaysia)
DOI: 10.4018/979-8-3373-0847-0.ch007
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Abstract
This systematic literature review examines the current state of AI-based tutoring systems in education, focusing on their roles in personalized learning, intelligent agent integration, and learning analytics within classroom instruction. A thorough analysis of 30 relevant studies reveals that AI-based tutoring systems significantly enhance educational outcomes by adapting learning experiences to individual needs through tailored feedback and customized learning trajectories, leading to improved student engagement and performance. Intelligent agents are central to these systems, providing social-emotional support, interactive feedback, and fostering motivation and deeper understanding. Learning analytics further support educators by enabling real-time monitoring of student progress, facilitating data-driven instructional adjustments, and ensuring timely, personalized support. Despite the progress, the study identifies ongoing challenges, particularly concerning ethical data use, scalability, and the need for integrating socio-emotional learning components.
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