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Generalizability Theory for Randomly Parallel Testing

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Title: Generalizability Theory for Randomly Parallel Testing
Language: English
Authors: Won-Chan Lee, Stella Y. Kim, Seungwon Shin
Source: Journal of Educational Measurement. 2026 63(1).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 21
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Descriptors: Generalizability Theory, Artificial Intelligence, Error of Measurement, Test Reliability, Educational Testing
DOI: 10.1111/jedm.70029
ISSN: 0022-0655
1745-3984
Abstract: Advancements in artificial intelligence (AI) have brought significant changes to testing practices, including the emergence of randomly parallel testing (RPT), in which examinees receive different but psychometrically similar sets of items generated from templates or AI-based systems. This paper presents a generalizability theory (GT) framework for estimating conditional standard errors of measurement (CSEMs) and related reliability indices, with a particular focus on design structures commonly encountered in RPT within domain-referenced testing contexts. The proposed framework supports the evaluation of score precision across a variety of operational designs, including crossed, nested, and multivariate configurations. Several illustrative examples are provided to demonstrate the methodology in practical settings. The paper also addresses key psychometric and interpretive challenges associated with RPT and outlines promising directions for future research.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1501420
Database: ERIC
FullText Text:
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: Advancements in artificial intelligence (AI) have brought significant changes to testing practices, including the emergence of randomly parallel testing (RPT), in which examinees receive different but psychometrically similar sets of items generated from templates or AI-based systems. This paper presents a generalizability theory (GT) framework for estimating conditional standard errors of measurement (CSEMs) and related reliability indices, with a particular focus on design structures commonly encountered in RPT within domain-referenced testing contexts. The proposed framework supports the evaluation of score precision across a variety of operational designs, including crossed, nested, and multivariate configurations. Several illustrative examples are provided to demonstrate the methodology in practical settings. The paper also addresses key psychometric and interpretive challenges associated with RPT and outlines promising directions for future research.
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