AccreditNet: An Explainable Data-Adaptive AI Framework for Accreditation Quality Analytics and Decision Support in the Training Sector

Accreditation and quality assurance in technical and vocational education and training (TVET) remain largely dependent on manual review and expert judgment. This study presents AccreditNet, an explainable tabular AI model embedded in a broader accreditation decision support framework. The experimentally evaluated component combines per-feature tokenization, multi-head self-attention, hierarchical criterion fusion, class-balanced focal loss, integrated gradients, and descriptive performance gap scoring. Evaluation is conducted against a structured NAAC benchmark that includes criterion scores and institutional attributes. Because the official NAAC grade is derived from criterion-level scoring, the benchmark is interpreted as a test of how well the model represents the criterion-to-grade mapping rather than as evidence of an independent predictive relationship. On the reported held-out split, AccreditNet achieved 93.72% accuracy, 92.84% macro F1-score, and 0.9706 AUC-ROC. OCR-based ingestion, NLP/LLM-assisted evidence mapping, automated reporting, and Saudi deployment are proposed operational extensions and were not evaluated in the present experiments. The results support further study of explainable accreditation analytics and decision support, subject to validation on local data.