A Decade of Reflection and Thematic Review on Artificial Intelligence's Impact on Educational Measurement

In brief: Xiong and Li map a decade (2015–present) of AI's impact on educational measurement through a thematic review that integrates five major international conference trends with peer-reviewed literature. They identify three evolutionary eras — the Formative Era (2015–2018), the Expansion Era (2019–2022), and the ongoing Generative Era (2023–present) — and organize AI's progression from an operational tool to a co-designer of assessment and learning through an Efficiency–Enhancement–Transformation framework, spanning four themes: AI on scoring and item generation, psychometric modeling, assessment innovation and process data, and fairness/ethics/equity.

This review traces how educational measurement has undergone a "computational turn" over the past decade, moving from its historical grounding in Classical Test Theory (CTT) and Item Response Theory (IRT) toward AI-integrated methods. The authors analyze conference trends (NCME, AERA, IAFOR, ASCILITE, and others) alongside the peer-reviewed corpus to quantify the rising proportion of AI-related measurement papers and to assign topics via Latent Dirichlet Allocation topic modeling. Their Efficiency–Enhancement–Transformation framework captures AI's evolving role: from efficiency gains in scoring and item generation, to enhancement of psychometric modeling, to transformation of assessment through process data and new paradigms that integrate measurement theory with AI methods.

The review surfaces both opportunities and challenges. Opportunities include scalability and richer diagnostic inference; challenges include interpretability, algorithmic bias, and construct validity. A key forward-looking conclusion is the need to reconceptualize educational constructs in the context of human–AI interaction and to adopt AI in Educational Measurement responsibly, with emphasis on transparency and equity.

Key Findings

• Three evolutionary eras: Formative (2015–2018), Expansion (2019–2022), and Generative (2023–present).
• Efficiency–Enhancement–Transformation framework organizes AI's progression from an operational tool to a co-designer of assessment and learning.
• 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.
• Opportunities: scalability and richer diagnostic inference from AI-integrated measurement.
• Challenges: interpretability, algorithmic bias, and construct validity.
• Argues for a new paradigm integrating measurement theory with AI methods and for reconceptualizing constructs in the context of human–AI interaction.

Connected Concepts

• Educational Measurement — the domain under review
• AI Ed Evaluation — evaluation of AI-enabled assessment
• Assessment — the practice AI is reshaping
• Generative AI — the driving technology of the Generative Era
• Assessment Validity — construct validity is a central challenge
• Automated Assessment — AI on scoring and item generation

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• Liu AI Literacy Interventions Meta Analysis 2026 — companion on AI literacy interventions and assessment
• Can AI Evaluate Assessment LLM Meta Assessment 2026 — LLM-based assessment evaluation
• GenAI Oop Programming Assessments 2026 — GenAI on authentic programming exams

Citation

Xiong, J., & Li, F. (2026). A decade of reflection and thematic review on artificial intelligence's impact on educational measurement. Educational Research Review, 51, 100789.