Blockchain-Powered Academic Integrity with AI-Based Plagiarism Detection

Abstract

Academic institutions face intensifying challenges to uphold integrity in an era of abundant digital content, AI-assisted writing tools, remote assessment, and rapidly scaling enrollments. Traditional plagiarism detection pipelines— based largely on centralized databases and surface-level similarity measures—struggle with paraphrase obfuscation, cross-lingual borrowing, ghostwriting, and evolving AI-generated text. This manuscript proposes a reference architecture that fuses permissioned blockchain with advanced AI-based plagiarism detection to create verifiable, privacy-preserving, and scalable integrity services for higher education. The approach records tamper-evident submission events, content fingerprints, and adjudication trails on-chain while storing full artifacts off-chain. It deploys modern NLP and program-analysis models (transformers, stylometry, AST- and token-based code analysis) for robust semantic similarity and Date of Publication: 12-06-2026 authorship profiling, augmented by privacy-preserving techniques such as federated learning and differential privacy. The design extends to decentralized identifiers (DIDs) and verifiable credentials (VCs) for identity assurance, and to zero-knowledge proofs (ZKPs) for selective disclosure of evidence during disputes. We present a methodological plan and an illustrative statistical analysis that compares a baseline detector with the proposed AI+blockchain system, showing higher recall with maintained precision, lower false-positive rates, and auditable decisions. We discuss governance, interoperability, and cost-performance trade-offs, and we delineate scope and limitations—including model drift, paraphrase arms races, throughput constraints, and crossinstitution adoption. The resulting architecture reframes plagiarism detection as a transparent, rights-respecting, and institutionally portable service, enabling universities to deter misconduct, accelerate fair resolution, and cultivate trust across the academic ecosystem.