Automated Item Neutralization for Non-Cognitive Scales: A Large Language Model Approach to Reducing Social-Desirability Bias

Sirui Wu, Daijin Yang


Abstract
This study explores an AI-assisted approach for rewriting personality scale items to reduce social desirability bias. Using GPT-refined neutralized items based on the IPIP-BFM-50, we compare factor structures, item popularity, and correlations with the MC-SDS to evaluate construct validity and the effectiveness of AI-based item refinement in Chinese contexts.
Anthology ID:
2025.aimecon-wip.1
Volume:
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
Month:
October
Year:
2025
Address:
Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States
Editors:
Joshua Wilson, Christopher Ormerod, Magdalen Beiting Parrish
Venue:
AIME-Con
SIG:
Publisher:
National Council on Measurement in Education (NCME)
Note:
Pages:
1–13
Language:
URL:
https://aclanthology.org/2025.aimecon-wip.1/
DOI:
Bibkey:
Cite (ACL):
Sirui Wu and Daijin Yang. 2025. Automated Item Neutralization for Non-Cognitive Scales: A Large Language Model Approach to Reducing Social-Desirability Bias. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 1–13, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):
Automated Item Neutralization for Non-Cognitive Scales: A Large Language Model Approach to Reducing Social-Desirability Bias (Wu & Yang, AIME-Con 2025)
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PDF:
https://aclanthology.org/2025.aimecon-wip.1.pdf