Neutrosophic–Plithogenic Quantum State Modeling for Big Data and Artificial Intelligence–Driven Teaching Effectiveness in Higher Education

Educational analytics frequently operates with datasets that are incomplete, noisy, and even contradictory. Conventional probabilistic models often fail to account for the simultaneous presence of truth, indeterminacy, and falsity in the same observation. This paper proposes a Neutrosophic–Plithogenic Quantum State Modeling (NP-QSM) framework that integrates neutrosophic statistics, plithogenic probability, and quantum inspired state evolution to address these challenges. Each educational performance indicator is represented as a neutrosophic triple, enabling explicit modeling of uncertainty. Plithogenic aggregation is then applied, where the degree of contradiction between indicators dynamically adjusts their influence in the fusion process. The aggregated state initializes a quantum-inspired probabilistic system governed by a real, column-stochastic generator, ensuring probability conservation while allowing smooth temporal transitions. The NP-QSM approach is mathematically rigorous, adaptable to multiple data modalities, and designed for real-time educational decision-making. Worked examples with full calculations demonstrate its application, showing how contradiction-aware modeling improves robustness and interpretability in uncertain environments.