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State Education Agencies  \nFebruary 2026  \nWill Lorié & Nathan Dadey  \nNational Center for the Improvement of Educational Assessment, Inc. Dover, New Hampshire  \nARTIFICIAL INTELL IGENCE IN LARGE-SCALE ASSESSMENT PROGRAMS:  \nApplications and Considerations for State Education Agencies  \nThe National Center for the Improvement of Educational Assessment, Inc. (the Center for Assessment) is a New Hampshire based not-for-profit (501(c)(3)) corporation. Founded in September 1998, the Center’s mission is to improve student learning by partnering with educational leaders to advance effective practicesand policies in support of high-quality assessment and accountability systems. The Center for Assessment does this by providing services directly to states, school districts, and partner organizations to support state and district assessment and accountability systems.  \nThis work is licensed under the Creative Commons Attribution 4.0 International License (CC BY) . To view a copy of this license, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/) .  \nACKNOWLEDGMENTS:  \nWe acknowledge the support and feedback from André Rupp, Sue Lottridge, Laura Hamilton, Ruhan Circi, Juan D’Brot, and other colleagues whose ideas and suggestions improved this paper. Special thanks to Catherine Gewertz, whose keen insights and thorough edits significantly improved the clarity of our work. Any errors and omissions are our own.  \nSUGGESTED CITATION:  \nLorié, W., & Dadey, N. (2026) . Artificial intelligence in large-scale assessment programs: Applications and considerations for state education agencies. Dover, NH: The National Center for the Improvement of Educational Assessment.  \nPAGE 2  \nARTIFICIAL INTELL IGENCE IN LARGE-SCALE ASSESSMENT PROGRAMS:  \nApplications and Considerations for State Education Agencies  \nTABLE OF CONTENTS  \nEXECUTIVE SUMMARY ....................................................4  \nINTRODUCTION ..............................................................6  \n• The Large-Scale Assessment Lifecycle ....................... 6  \n• AI and Generative AI..................................................... 7  \nWHY AI IN LARGE-SCALE ASSESSMENT? .......................9  \n• Humans in the Loop..................................................... 9  \nAI APPLICATIONS AND CONSIDERATIONS .................11  \n• Construct Definition ................................................... 11  \n• Content Development................................................ 15  \n• Field Testing and Equating ........................................ 21  \n• Administration ............................................................ 23  \n• Scoring ......................................................................... 26  \n• Reporting ..................................................................... 30  \n• Other Applications...................................................... 34  \nCONCLUSION ................................................................37  \n• Implications for State Education Agencies ..............37  \nREFERENCES ..................................................................39  \nPAGE 3  \nARTIFICIAL INTELLIGENCE IN LARGE-SCALE ASSESSMENT PROGRAMS:  \nApplications and Considerations for State Education Agencies  \nEXECUTIVE SUMMARY  \nThis paper explores the potential of artificial intelligence (AI), especially generative AI (GenAI),1 in educational assessment, with a focus on large-scale assessment (LSA) programs—specifically statewide summative assessments designed to fulfill federal accountability requirements.  \nWe’ve structured the paper around the LSA life cycle, analyzing how AI currently influences, or could influence, each phase, from defining what we’re measuring to reporting, and how it could support processes that cross stages. For each phase of the life cycle, we outline the main activiti","cbCaiupwKsGlm2im","https://ap.wps.com/l/cbCaiupwKsGlm2im","pdf",2130856,47,"English","# Executive Summary\n## The Large-Scale Assessment Lifecycle\n## AI and Generative AI\n# Why AI in Large-Scale Assessment?\n## Humans in the Loop\n# AI Applications and Considerations\n## Construct Definition\n## Content Development\n## Field Testing and Equating\n## Administration\n## Scoring\n## Reporting\n## Other Applications\n# Conclusion\n## Implications for State Education Agencies\n# References","[{\"question\":\"What parts of the large-scale assessment lifecycle does the paper cover?\",\"answer\":\"It covers construct definition, content development, field testing and equating, administration, scoring, reporting, and other applications, aligned to the large-scale assessment lifecycle.\"},{\"question\":\"How can generative AI support content development for assessments?\",\"answer\":\"GenAI systems can produce assessment items, synthesize passages and stimuli, and assist human authors by providing feedback on item quality and alignment.\"},{\"question\":\"Why does the paper emphasize human responsibility in construct definition?\",\"answer\":\"It argues that defining and validating constructs must remain with human experts to preserve validity, fairness, and interpretive coherence of test scores.\"}]","ARTIFICIAL INTELLIGENCE IN LARGE-SCALE ASSESSMENT PROGRAMS - Applications and Considerations for State Education Agencies | PDF",1792057271,118]