A decade of reflection and thematic review on artificial intelligence's impact on educational measurement

Abstract

Educational measurement stands at a transformative juncture as artificial intelligence (AI) reshapes assessment practices, however, no comprehensive synthesis has examined this evolution systematically. This thematic review integrates five major international conference trends with peer-reviewed literature to map AI's decade-long impact on educational measurement and to derive heuristic insights for learning and instruction. Through systematic content analysis, we identify three distinct evolutionary eras, the Formative Era (2015-2018), the Expansion Era (2019-2022), and the ongoing Generative Era (2023-present). Our analysis traces AI's progression from an operational tool to a co-designer for assessment and learning, organized through an Efficiency–Enhancement–Transformation framework. Within this framework, we highlight four key themes: (a) AI on scoring and item generation, (b) AI on psychometric modeling, (c) AI on assessment innovation and process data, and (d) AI on fairness, ethics, and equity. Across these themes, the literature reveals both opportunities, such as scalability and richer diagnostic inference, and challenges, including interpretability, algorithmic bias, and construct validity. The evidence also points to a new paradigm that integrates measurement theory with AI methods. The review concludes future directions for the responsible adoption of AI in educational measurement and discusses how AI-integrated measurement can inform learning and instruction, with emphasis on transparency, equity, and the reconceptualization of educational constructs in the context of human–AI interaction.