A Knowledge–Scenario–Learner Framework for Target-Oriented Adaptive Sequencing: A Simulation-Based Proof-of-Concept Study

Adaptive learning systems need to sequence learning activities according to individual progress while respecting the prerequisite structure of the learning domain. This study proposes the Triadic Educational Knowledge Space (TEKS) framework for target-oriented adaptive sequencing in structured domains. TEKS integrates a prerequisite graph, a task-specific target set, and a recursively updated predictive distribution over prerequisite-consistent latent knowledge states. At each decision step, candidate items are evaluated using their predictive one-step acquisition probability, adjusted by graph-based target relevance. A simulation-based proof-of-concept study evaluated TEKS on an eight-node symmetric branching graph with two isomorphic target conditions, a six-node asymmetric converging graph, and a six-node linear prerequisite chain. Each condition involved 200 virtual learners, 90 interaction steps, and 30 independent repetitions. TEKS was compared with Prerequisite-Weighted Mastery Gap (PW-MG), a deterministic static schedule, and random selection under matched simulation conditions. In the branching structures, TEKS yielded higher final target mastery, mean target mastery over time, and final scope mastery than all three comparison strategies. In the linear prerequisite chain, TEKS and PW-MG showed similar target-level outcomes, whereas TEKS retained higher final scope mastery. Within the examined conditions, the difference between TEKS and PW-MG was most evident when multiple prerequisite-related candidates competed for selection. These results provide controlled proof-of-concept evidence for target-adjusted one-step acquisition scoring. Further evaluation is needed on larger and more varied prerequisite structures, under broader forms of model misspecification, and with authentic learner interaction data.