Agentic artificial intelligence in education: A review on personalized adaptive learning through autonomous tutoring systems
Abstract
The accelerated development of artificial intelligence has triggered disruptive shifts in the educational settings of all sizes, but conventional learning models remain into the inability of providing really personalized, adaptive experiences in large scale. This literature analysis focuses on how agentic artificial intelligence has come to existence in education and specifically a personalized adaptive learning autonomous tutoring system. The problem statement deals with the life-and-death situation of standardized education provision in comparison to the needs of individual learners that the current technologies have addressed half-heartedly. This review synthesizes the current literature on autonomous AI agents that are intended to act as intelligent tutors and discuss their architectures, decision-making processes as well as adaptive learning processes through systematic analysis using the PRISMA methodology. The paper explores the way in which these systems utilize reinforcement learning, natural language processing, affective computing and cognitive modeling as a means of providing dynamic, learner-focused educational experiences. Findings indicate that the research made a major step forward in terms of multi agent tutoring models, real-time knowledge tracking, emotion sensitive pedagogy, and optimal sequencing of the curriculum. According to the review, such persistent issues are ethical issues, algorithmic-based decision-making transparency, data privacy, and equitable access. Results indicate that the agentic AI tutoring systems can have significant potential in overcoming learning gaps, differentiation of educational requirements, and business model of personalized training. Nevertheless, there is significant implementation impediments on technological infrastructure, integration of teachers and regulatory frameworks.
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References
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