Blockchain-Powered Trust Framework for Securing Online Examination Integrity and Authenticity Through Smart Validation

Abstract

Mass adoption of online learning and remote assessment has created significant challenges to maintaining exam integrity, verifying examinees, and ensuring veracity in scores. Existing solutions are frequently not based on consistent and robust validation mechanisms, and it is easy to commit impersonation, cheating and manipulation of the results. This research proposes deployment of a blockchain-based trust architecture to improve security in online examination systems by using smart validation. The primary objective is to create an all-inclusive system that ensures the veracity of exam takers and the validity of exam records when integrating biometric, behavioral, and the use of smart contract automation. We created a dataset of 50 scenarios from exams that included real world elements (face match confidence, keystroke patters, gaze tracking difference, device information and time logs). Deep learning algorithms were used on the dataset for session classification while dynamic validation criteria were applied by smart contracts and a private Ethereum blockchain for validated results maintained. Four main methods were applied to review the proposed model: rule-based logic, random forest, logistic regression and support vector machine (SVM) models. The outcome showed that the proposed technique gave an accuracy of 92%, an F1-score of 0.92 and a ROC-AUC of 0.95 which is lower than the other approaches. By examining confusion matrices and performing statistical tests, it was proved that the suggested model is both robust and generalizable and it has an unbelievably low p-value of 1.95 × 10−20in comparison with the weakest baseline. This research offers a scalable framework for handling e-proctored high stakes assessments, which is secure and auditable to overcome the current limitations and advances reliable digital educational platforms.