AI for Educational Assessment: Opportunities and Challenges through the Lens of Evidence-Centered Design and Measurement Values

The rise of large language models (LLMs) and generative AI is revolutionizing educational assessment, presenting transformative opportunities alongside critical challenges. This chapter explores these new opportunities through the lens of Evidence-Centered Design and examines the challenges along two key dimensions: adherence to the core measurement values and the growing concerns about academic integrity. The goal is to provide researchers, educators, and policymakers with a concise overview of the responsible and effective integration of AI into educational measurement.

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Publication Details

Published
2025-08-08
DOI
https://doi.org/10.35542/osf.io/dacxj_v2
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.00

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article

AI for Educational Assessment: Opportunities and Challenges through the Lens of Evidence-Centered Design and Measurement Values

Jiangang Hao
Online Learning and Analytics
article

AI for Educational Assessment: Opportunities and Challenges through the Lens of Evidence-Centered Design and Measurement Values

Jiangang Hao
article en

Abstract

The rise of large language models (LLMs) and generative AI is revolutionizing educational assessment, presenting transformative opportunities alongside critical challenges. This chapter explores these new opportunities through the lens of Evidence-Centered Design and examines the challenges along two key dimensions: adherence to the core measurement values and the growing concerns about academic integrity. The goal is to provide researchers, educators, and policymakers with a concise overview of the responsible and effective integration of AI into educational measurement.

Princeton University (US)
Nanyang Technological University
Openalex Percentile: Top 79%
Online Learning and Analytics
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