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Harnessing AI for Educational Measurement: Standards and Emerging Frontiers

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  • Hua-Hua Chang

Abstract

The surge of AI in education raises concerns about measurement downsides. Calls for clear standards are warranted. Fortunately, the psychometrics field has a long history of developing relevant standards—like sample invariance and item bias avoidance—crucial for reliable, valid, and interpretable assessments. This established body of knowledge, not unlike traffic laws for self-driving cars, should guide AI assessment development. Measuring new constructs necessitates stronger construct validity research. Instead of rewriting the rulebook, our focus should be on educating AI developers about these standards. This commentary specifically addresses the concern of empowering instructors not with high-stakes testing but with effective item writing through AI. We explore the potential of AI to transform item development, a key area highlighted by researchers. While AI tools offer exciting possibilities for tackling educational challenges, equipping instructors to leverage them effectively remains paramount.

Suggested Citation

  • Hua-Hua Chang, 2024. "Harnessing AI for Educational Measurement: Standards and Emerging Frontiers," Journal of Educational and Behavioral Statistics, , vol. 49(5), pages 702-708, October.
    • Handle: RePEc:sae:jedbes:v:49:y:2024:i:5:p:702-708
    DOI: 10.3102/10769986241264033
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    References listed on IDEAS

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    1. Hua-Hua Chang, 2015. "Psychometrics Behind Computerized Adaptive Testing," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 1-20, March.
    2. Hong-Yun Liu & Xiao-Feng You & Wen-Yi Wang & Shu-Liang Ding & Hua-Hua Chang, 2013. "The Development of Computerized Adaptive Testing with Cognitive Diagnosis for an English Achievement Test in China," Journal of Classification, Springer;The Classification Society, vol. 30(2), pages 152-172, July.
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    Cited by:

    1. Steven Andrew Culpepper, 2024. "Introduction to the JEBS Special Section on Artificial Intelligence in Educational Statistics," Journal of Educational and Behavioral Statistics, , vol. 49(5), pages 691-693, October.

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