Large Language Model Agents for Personalized Education: Adaptive Tutoring and Reinforcement Learning-Based Learning Path Optimization
Year: 2026
Type: article
Abstract: Personalized education seeks to approximate the responsiveness, diagnosis, and pacing of expert human tutoring at scale, yet traditional intelligent tutoring systems often remain constrained by brittle domain models, limited natural language flexibility, and static sequencing logic. Large language model agents introduce a new system-level possibility: they can interpret open-ended learner language, coordinate pedagogical tools, maintain extended interaction histories, and generate context-sensitive explanations while remaining embedded in measurable instructional workflows. This paper examines the architecture, deployment, and governance of large language model agents for adaptive tutoring and reinforcement learning-based learning path optimization. It argues that the central challenge is not merely model capability but the socio-technical integration of learner models, curriculum representations, reinforcement learning controllers, retrieval infrastructure, assessment instruments, and human oversight. The paper analyzes adaptive tutoring architectures, reward design for sequencing, orchestration trade-offs across cloud and edge environments, robustness under hallucination and distribution shift, fairness and privacy obligations, and sustainability concerns associated with large-scale inference. It also considers case illustrations across K-12 mathematics, higher education, and professional learning, and compares educational deployments with other high-stakes service infrastructures. The conclusion emphasizes that durable personalization requires interpretable learner representations, auditable optimization objectives, resilient fallback pathways, and policy frameworks that treat educational agents as accountable public-interest systems rather than isolated software products.
Authors: Heere, Vince, Kalman, Jinthe
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W7222852925