Artificial Intelligence in Large-Scale Assessment Programs: Applications and Considerations for State Education Agencies
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| Title: | Artificial Intelligence in Large-Scale Assessment Programs: Applications and Considerations for State Education Agencies |
|---|---|
| Language: | English |
| Authors: | Will Lorié, Nathan Dadey, National Center for the Improvement of Educational Assessment, Inc. (NCIEA) |
| Source: | National Center for the Improvement of Educational Assessment. 2026. |
| Availability: | National Center for the Improvement of Educational Assessment. P.O. Box 351, Dover, NH 03821. Tel: 603-516-7900; Fax: 603-516-7910; e-mail: recep@nciea.org; Web site: http://www.nciea.org |
| Peer Reviewed: | N |
| Page Count: | 47 |
| Publication Date: | 2026 |
| Intended Audience: | Administrators |
| Document Type: | Reports - Evaluative |
| Descriptors: | Artificial Intelligence, State Departments of Education, Student Evaluation, Evaluation Methods, Educational Assessment, Summative Evaluation, Accountability, Test Construction, Testing, Scoring, Information Dissemination, Computer Uses in Education |
| Abstract: | This paper explores the potential of artificial intelligence (AI), especially generative AI, in educational assessment, with a focus on large-scale assessment (LSA) programs--specifically statewide summative assessments designed to fulfill federal accountability requirements. The paper is structured around the life cycle of large-scale assessment programs: (1) Construct definition; (2) Content development; (3) Field testing and equating; (4) Administration; (5) Scoring; and (6) Reporting. We analyze 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 activities involved and then examine current and potential AI applications for that phase. We also offer considerations for state education agencies aiming to use AI in their programs at each stage. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | ED679214 |
| Database: | ERIC |
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