Adaptive Learning

Adaptive learning studies how models and instructional systems can change their behavior as they encounter new data, shifting environments, or learners with evolving knowledge. Educational systems use learner-state models such as Bayesian Knowledge Tracing, knowledge graphs, mastery-based curricula, contextual bandits, and reinforcement learning to choose questions, feedback, or pacing. In other settings, online and continual-learning methods detect drift and update or select predictors using Bayesian updates, ensembles, few-shot adaptation, fast memory, or structural changes, with evaluations spanning tutoring, streaming forecasts, robotics, and dynamic graphs. A central challenge is balancing responsiveness to new conditions with retention, robustness, and computational cost, while demonstrating gains through longitudinal or shift-aware evaluation rather than short-term accuracy alone.