ARTIFICIAL INTELLIGENCE AND THE FUTURE OF EDUCATIONAL MEASUREMENT AND EVALUATION: EMERGING TRENDS, ISSUES, AND IMPLICATIONS

Abstract

This paper examines emerging trends, critical issues, and implications associated with the integration of AI into educational measurement and evaluation. Drawing on contemporary literature and policy documents, the paper explores AI-assisted test development, automated scoring, adaptive assessment, generative AI, learning analytics, educational data mining, predictive assessment, competency-based assessment, and AI-supported institutional evaluation. Particular attention is given to the implications of AI for validity, reliability, fairness, transparency, privacy, accountability, assessment security, and professional practice. The paper argues that AI can enhance the efficiency, scalability, personalization, and responsiveness of assessment systems, but technological sophistication does not automatically guarantee valid or equitable measurement. AI-enabled assessments may introduce algorithmic bias, construct-irrelevant variance, data privacy risks, opacity, score drift, and new forms of academic misconduct. Recent scholarship emphasizes that validity and fairness arguments must evolve alongside AI-enabled assessment practices, with continuous monitoring required throughout the assessment lifecycle.The paper concludes that the future of educational measurement should be characterized by human-AI collaboration rather than technological substitution. It recommends AI literacy for measurement professionals, rigorous psychometric validation, transparent algorithmic governance, continuous bias auditing, secure data infrastructures, human oversight, and context-sensitive regulation. Responsible integration of AI can strengthen educational measurement and evaluation while preserving the scientific, ethical, and human-centred foundations of assessment.