adaptive-learning

Introduces AdaJEPA, an adaptive world model that continuously learns and updates its latent representation during deployment, enabling agents to adjust their plans based on real-world observations without retraining or memory tricks.

Introduces AdaJEPA, an adaptive world model that continuously learns from perception, planning, and action in a closed loop.

This paper proposes an adaptive, subject-aware prompt routing framework for LLM-based high-school tutoring, using 14 pedagogical features to switch strategies. A/B testing with 359 students shows improved efficiency and conversion rates over static baselines.

Paradigm turns any goal into a personalized, adaptive learning path.

LectūraAgents is a multi-agent framework for adaptive personalized learning that mimics professor-student interactions and generates embodied teaching actions aligned with learner profiles. It introduces a hierarchical architecture, an adaptive embodied teaching mechanism, and a Teaching Action-Speech Alignment algorithm, showing consistent improvements over existing approaches.

This paper proposes a thinking-learning interaction model for autonomous robots, enabling them to adaptively discover new features, expand output categories, update learning models, and reconstruct action routines in open environments. Experimental results demonstrate significant improvements in recognition accuracy, category formation, and action efficiency.

This paper proposes a learner model-based rubric to evaluate the adaptivity of Vision Language Models (VLMs) in mathematics education. Experiments show measurable differences in adaptivity across models and reveal that current VLMs struggle to produce consistent learner-adaptive instructional responses.

This article introduces an open-source AI tutoring system called 'Bloom-one-vs-one-study' that implements Benjamin Bloom's educational theory using Claude Code to provide personalized, adaptive one-on-one learning experiences.

Researchers from Arizona State University present a framework for evaluating adaptive personalization of educational reading materials using theory-grounded simulated learners, incorporating memory models, misconception revision, and Bayesian Knowledge Tracing. Experiments across three subjects show adaptive reading significantly improved outcomes in computer science but had mixed results in chemistry and biology.