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    <abstract>This study explores the use of large language models to simulate human responses to Likert-scale items. A DeBERTa-base model fine-tuned with item text and examinee ability emulates a graded response model (GRM). High alignment with GRM probabilities and reasonable threshold recovery support LLMs as scalable tools for early-stage item evaluation.</abstract>
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%0 Conference Proceedings
%T Simulating Rating Scale Responses with LLMs for Early-Stage Item Evaluation
%A Demirkaya, Onur
%A Wei, Hsin-Ro
%A Johnson, Evelyn
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
%D 2025
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-218-84228-4
%F demirkaya-etal-2025-simulating
%X This study explores the use of large language models to simulate human responses to Likert-scale items. A DeBERTa-base model fine-tuned with item text and examinee ability emulates a graded response model (GRM). High alignment with GRM probabilities and reasonable threshold recovery support LLMs as scalable tools for early-stage item evaluation.
%U https://aclanthology.org/2025.aimecon-main.41/
%P 385-392

Markdown (Informal)

[Simulating Rating Scale Responses with LLMs for Early-Stage Item Evaluation](https://aclanthology.org/2025.aimecon-main.41/) (Demirkaya et al., AIME-Con 2025)

• Simulating Rating Scale Responses with LLMs for Early-Stage Item Evaluation (Demirkaya et al., AIME-Con 2025)

ACL

• Onur Demirkaya, Hsin-Ro Wei, and Evelyn Johnson. 2025. Simulating Rating Scale Responses with LLMs for Early-Stage Item Evaluation. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 385–392, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).