Predictive Computing for AI Education: Anticipating Students' Future Learning Questions for Proactive Personalized Learning Support

The increasing integration of generative artificial intelligence into educational environments has created new opportunities for personalized learning, interactive tutoring, and adaptive knowledge support. Conventional AI-assisted educational systems primarily operate reactively: students submit questions, and the system generates explanations, examples, exercises, or feedback in response. However, student learning is not a sequence of completely independent questions. Learning activities follow partially predictable cognitive and conceptual trajectories. After understanding one concept, students may encounter related questions, misunderstandings, prerequisite knowledge gaps, application difficulties, and more advanced learning requirements. These emerging learning needs may provide opportunities for computational preparation before students explicitly formulate their next questions. This paper introduces Predictive Computing for AI Education (PCAE), a conceptual framework that investigates how artificial intelligence systems can anticipate students’ potential future learning questions and proactively prepare appropriate educational resources. Rather than predicting only the probability of future question occurrence, PCAE considers the relationship between students’ current learning progress, curriculum structures, prerequisite dependencies, historical interaction patterns, knowledge gaps, assessment performance, and conceptual learning trajectories. The framework translates these signals into probabilistic estimates of future learning questions and identifies suitable instructional resources for advance preparation. The proposed architecture integrates seven interconnected functions: (1) student learning-state representation, (2) predictive learning-question generation, (3) knowledge-gap and misconception anticipation, (4) instructional resource preparation, (5) pedagogically constrained response selection, (6) adaptive learning-support scheduling, and (7) prediction-aware educational evaluation. Its computational preparation mechanisms include generating anticipated explanations, preparing alternative conceptual representations, constructing worked examples, selecting practice exercises, organizing prerequisite knowledge, and preparing diagnostic questions. A unified mathematical formulation is developed to evaluate the expected educational benefit of advance preparation while accounting for prediction uncertainty, computational expenditure, instructional relevance, student cognitive readiness, resource freshness, and the risk of inappropriate pedagogical intervention. An important distinction is established between predicting what a student may ask, predicting what the student needs to learn, and deciding what educational support should be prepared. These three operations are related but not equivalent. A predicted question may be statistically likely without representing the student's most important learning need. Consequently, PCAE incorporates pedagogical relevance, curriculum alignment, knowledge prerequisites, student autonomy, and uncertainty-aware resource selection into the preparation process. The framework further distinguishes between reusable educational content and student-specific instructional states, recognizing that identical questions may require different explanations depending on individual learning progress. An experimental methodology is proposed to investigate prediction accuracy, educational resource reuse, learning-support latency, computational expenditure, instructional appropriateness, student learning outcomes, and privacy preservation. Comparisons with reactive AI tutoring, conventional educational recommendation, knowledge tracing, and non-predictive resource caching are established. No empirical performance improvements are assumed. The proposed framework provides a theoretical and experimental foundation for investigating a transition from reactive AI tutoring toward anticipatory educational support, in which artificial intelligence systems prepare relevant learning resources before students explicitly request them.