A Neutrosophic Triplet-Plithogenic Model for Evaluating Talent Training Quality in IoT Application Technology Based on Artificial Intelligence

Evaluating the quality of Internet of Things (IoT) talent training in vocational colleges is critical yet challenging due to the diverse, interdependent skills required and the complexities of blended learning environments. This study proposes a novel neutrosophic triplet-plithogenic model to assess training effectiveness, integrating neutrosophic triplet sets to capture truth, indeterminacy, and falsehood in student performance and plithogenic sets to model skill interdependencies via contradiction degrees. The model aggregates evaluations from instructors, AI systems, and students, adjusted for reliability, to generate granular proficiency profiles and class-level quality indices. Applied to scenarios like network security, smart agriculture, and industrial IoT, and culminating in a comprehensive smart city training program evaluation, the model provides actionable insights for curriculum optimization. Results demonstrate its ability to identify skill gaps, address uncertainties, and align training with industry needs. Limitations, such as parameter sensitivity, and future directions, including empirical validation, are discussed, positioning the model as a transformative tool for IoT education.