Agentic artificial intelligence-driven tutoring: A multi-agent cognitive architecture for personalized adaptive learning in Education
Abstract
The one-size-fits all model of education has not been sufficient with regard to meeting the various learning needs of the students in present day education setup. This research paper outlines a review of the literature which discusses the transformational possibilities of agentic artificial intelligence-based tutoring systems which realize multi-agent cognitive architectures to personalized adaptive learning. This paper explores how autonomous AI agents, integrated in complex cognitive models, will help transform the way education is delivered through dynamical adaptation of instructional models, teaching complexity, and pedagogic quality depending on the distinctive features of individual learners, educational performance, and neuroscience involved. This review summarizes the current nexus of intelligent tutoring system, multi-agent architecture, cognitive computing and adaptive learning technologies by conducting a systematically done review based on PRISMA methodology. The results indicate that multi-agent AI systems have better behavior modeling, system organization of individualized learning processes, real-time feedback, and metacognitive enhancement capacities over traditional learning technologies. Some of the main concerns that are discovered are ethical issues regarding the privacy of learner data, transparency in algorithms, lack of scalability, and the requirement to implement powerful evaluation systems. The study points out the new possibilities in neuroadaptive learning interface, emotion-sensitive tutoring agent, and socially-intelligent interactive learning learning settings. This analytic review has added to the existing literature as it provides a mapping of the state of underlying agentic AI tutoring systems and offers important research areas that researchers need to focus on to construct more successful and equitable and human education technologies.
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References
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Copyright (c) 2026 Neha Gupta, Shreeshail Heggond (Author)
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