arXiv:2604. 26962v3 Announce Type: replace-cross Abstract: Education is one of the most promising real-world applications for Large Language Models (LLMs).
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arXiv:2606. 16428v1 Announce Type: cross Abstract: Effective personalized AI-assisted learning demands systems that can not only generate accurate learner-specific educational materials, but also dynamically adapt their instruction to diverse learners.
arXiv:2608. 03206v1 Announce Type: cross Abstract: Large language models (LLMs) power educational applications from tutoring to essay scoring, but each is a point solution to a single task, and only recently have these point solutions been integrated into agents operating over a learning management system (LMS).
arXiv:2607. 16851v1 Announce Type: new Abstract: Deploying LLM agents typically requires a compact test-time student, even if a stronger teacher is available during training.
DeepEdu‑v1 is an AI‑tutoring system tailored for Vietnamese education that addresses data‑sovereignty and local curriculum alignment issues. It uses a long‑context inference engine to reduce retrieval calls and prefill latency by about 35%, and a self‑improving agentic layer that curates verified local knowledge without fine‑tuning. In deployment, DeepEdu achieves nearly twice the speed of standard vLLM serving and raises agentic accuracy from 70.0% to 79.5% on complex tasks, especially in financial reasoning and interactive‑agent benchmarks.
arXiv:2606. 15225v1 Announce Type: cross Abstract: Large-scale learner-task interaction data are crucial for intelligent educational systems but are costly to collect and constrained by privacy and learner engagement.
CoLearn is an interactive, agentic tutoring system that learns about each learner through a persistent memory of mastery and misconceptions, updated with a Bayesian Knowledge Tracing model that uses a large language model as an observation function. It generates personalized questions targeting the learner’s weakest topics and recurring misconceptions, and provides a live evidence view for progress visualization and blind A/B comparison. In blind A/B tests, learners preferred questions conditioned on this memory 68‑69% of the time, and simulations show the agent’s belief converges toward the learner’s true mastery.