A Blockchain–Artificial Intelligence Hybrid Framework for Secure Assessment, Quality Assurance, and Student Trust in Distance Education

Abstract: The rapid expansion of distance education has increased access to learning but has also intensified challenges related to assessment security, academic integrity, quality assurance, and student trust. Conventional Learning Management Systems (LMS) often rely on centralized architectures that are vulnerable to data tampering, unauthorized access, delayed verification, and limited transparency in grading and credential management. This study proposes a Blockchain–Artificial Intelligence (AI) Hybrid Framework designed to enhance secure assessment, institutional quality assurance, and student trust in distance education through decentralized verification and intelligent analytics. A mixed-methods research design integrating quantitative and qualitative approaches was employed. Quantitative evaluation was conducted using assessment records, blockchain transaction logs, AI prediction outputs, and system performance metrics collected from a distance learning environment. Qualitative data were obtained through interviews and structured questionnaires involving students, instructors, and quality assurance experts. The proposed framework integrates blockchain-based immutable assessment records with AI-driven automated grading, anomaly detection, plagiarism identification, and predictive quality analytics. Performance was compared with conventional cloud-based e-learning systems using assessment integrity, grading accuracy, fraud detection rate, transaction verification time, system latency, user trust, and overall quality assurance effectiveness as evaluation parameters. Experimental results demonstrated that the proposed framework achieved 99.8% assessment data integrity, 98.6% AI grading accuracy, 97.9% academic misconduct detection accuracy, 95.8% quality assurance compliance, and 96.7% student trust satisfaction, while reducing verification time by 64% and assessment processing latency by 42% compared with traditional centralized systems. Qualitative findings further confirmed improved transparency, fairness, accountability, and confidence in online assessment processes. The study concludes that integrating blockchain and artificial intelligence provides a scalable, secure, and trustworthy solution for sustainable distance education, strengthening assessment reliability, institutional quality assurance, and learner confidence while supporting evidence-based educational decision-making. Keywords: artificial intelligence; Quality Assurance; Keywords: Blockchain; Distance Education; Secure Assessment.