AI-Powered Framework for Enhancing Examination Integrity Through Intelligent Question Paper Scrutinization

Ensuring the integrity and security of exams is a matter of serious concern for educational institutions, particularly in the wake of increased instances of question paper leaks and cheating. This paper suggests an AI-driven system to improve examination integrity through intelligent question paper analysis. The system uses AI/ML methods for automated question inspection and AI-driven anomaly detection like isolation forests to detect inconsistencies in question patterns. Machine learning algorithms like decision trees and support vector machines (SVM) enable adaptive question selection to ensure proper difficulty levels and track student performance. An Intelligent Question Bank based on Natural Language Processing (NLP) guarantees the production of non-repetitive, personalized test papers. Moreover, blockchain technology is used to secure and decentralize storage of examination data. The system proposed in this paper provides a scalable, secure, and low-interference solution to maintain examination standards and integrity. In addition, the proposed framework aligns with Sustainable Development Goal 4: Quality Education since it significantly advances the process of attaining inclusive and equitable quality education through ensuring examination integrity. Through a robust assessment of question papers concerning fairness, pertinence, and accuracy, the software ensures tests align with learning outcomes, enhancing trust within the education system. The framework also aligns with Sustainable Development Goal 9: Industry, Innovation, and Infrastructure since the design and utilization of a software-based framework represent a technological breakthrough within the education industry. Through digital resources, the framework improves examination procedure infrastructure, in compliance with the requirement to build resilient infrastructure and promoting sustainable industrialization.