An anti-corruption system for academic achievement verification in kazakhstani higher education using blockchain and artificial intelligence - Razaque, Abdul; Amanzholova, Saule; Mutanov, Galimkair; Ussatova, Olga; Farhad, Mohsin Ali; Kistaubayev, Yerlan; Kamilla, Smagulova; Adilzhan, Kopebayev (Frontiers in Blockchain, 2025)
This article focuses on developing an anti-corruption system for certifying students’ academic achievements in Kazakhstani higher education institutions by utilizing blockchain and artificial intelligence AI technologies. We specifically propose the Academic Integrity Verification System (AIVS), a revolutionary system that combines blockchain’s tamper-proof storage with AI’s anomaly detection capabilities. The system reduces major risks in traditional academic record management while ensuring transparency, precision, and proactive fraud detection. The simulation was conducted at the International Information Technology University (IITU) using Ethereum-based blockchain and AI models. In simulated testnet experiments, AIVS achieved an 85% reduction in verification time compared to traditional processes and delivered a 95% overall model accuracy in record validation. These results demonstrate the potential of blockchain and AI integration for improving efficiency and integrity in academic verification workflows. These findings demonstrate that our proposed AIVS enhances academic transparency, reduces corruption, and provides a scalable framework for secure academic record management. The proposed strategy marks a significant step forward in the governance of digital education in Kazakhstan and abroad.