State-conditioned Pedagogical Adaptive Routing for Exercise Recommendation

Exercise recommendation is inherently non-stationary: learner representations are unreliable under sparse interaction histories, the set of pedagogically appropriate exercises changes with knowledge mastery, and the desired balance among consolidation, exploration, and challenge evolves throughout the learning process. However, existing methods often optimize multiple objectives using globally fixed trade-offs and handle representation learning, candidate retrieval, and ranking as separate components, limiting their ability to adapt to both cold-start uncertainty and changing cognitive states. To address these limitations, we propose SPAR, a state-conditioned pedagogical adaptive routing framework that treats exercise recommendation as three interdependent decisions. First, a prototype-guided alignment module regularizes sparse learner representations using behavioral priors derived from similar active learners. Second, a ZPD (Zone of Proximal Development)-inspired retrieval module adapts the target challenge level according to recent performance and constructs a personalized candidate set within an appropriate difficulty range. Third, a state-conditioned dynamic routing network dynamically coordinates relevance, diversity, and difficulty within the retrieved candidate space according to the learner's current cognitive state. Experiments on three public datasets show that SPAR consistently improves ranking performance over strong exercise-recommendation baselines, with particularly pronounced gains for learners with sparse interaction histories.