Contemporary university examination systems face persistent challenges including inconsistent evaluation, evaluator fatigue, marks tampering, and delayed result publication. This paper presents an AI-driven Examination Controller System that automates the complete examination lifecycle from question generation to secure result publication. Subject PDFs are processed using PyMuPDF, with key concepts extracted through TF-IDF and TextRank algorithms. The T5 Transformer synthesizes extracted concepts into syllabus-aligned examination questions structured into standardized formats. Post-examination, scanned answer sheets undergo OpenCV preprocessing and Tesseract OCR-based text extraction. Student responses are encoded using BERT embeddings and evaluated against reference content through cosine similarity, enabling context-aware, bias-free mark allocation. Evaluated marks are cryptographically secured within a SHA-256 blockchain ledger, rendering tampering immediately detectable. A Controller of Examinations verification workflow ensures institutional oversight before result publication. The system significantly reduces manual workload, improves evaluation consistency, and demonstrates strong scalability for large examination ecosystems including autonomous universities and affiliated institutions.
Purushothaman et al. (Fri,) studied this question.
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