ARTIFICIAL INTELLIGENCE IN EDUCATIONAL MEASUREMENT: CHALLENGES AND OPPORTUNITIES

Abstract

Artificial Intelligence (AI) has emerged as a transformative technology with significant implications for educational measurement. The integration of AI into assessment processes has expanded the capabilities of traditional measurement practices through innovations such as automated scoring, computerized adaptive testing, automated item generation, learning analytics, predictive assessment, and personalized feedback systems. This paper examines the opportunities and challenges associated with the application of AI in educational measurement. Drawing on contemporary literature, the paper highlights how AI can enhance assessment efficiency, improve measurement precision, support data-driven decision-making, and facilitate individualized learning experiences. Despite these benefits, several concerns persist regarding the validity, reliability, fairness, transparency, data privacy, and security of AI-driven assessment systems. The study further discusses the risks of algorithmic bias, the lack of explainability in complex AI models, and emerging threats to academic integrity arising from generative AI technologies. The paper argues that the successful adoption of AI in educational measurement requires adherence to established psychometric principles, ethical guidelines, and regulatory frameworks. It concludes that while AI offers considerable potential for improving educational assessment, continuous validation, monitoring, interdisciplinary collaboration, and responsible governance are essential to ensure that AI-based measurement systems remain accurate, equitable, transparent, and beneficial to learners and educational stakeholders.