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        <title>Predicting IRT Parameters for Passage-Based Reading Items Using NLP</title>
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        <name type="personal">
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    <abstract>This study predicts IRT item difficulty and discrimination for passage-based reading comprehension items using lexical, syntactic, and semantic NLP features. Modeling interactions among passages, stems, and options, Light- GBM with semantic embeddings best predicts difficulty (r = 0.594), while discrimination remains harder to recover from text alone.</abstract>
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%0 Conference Proceedings
%T Predicting IRT Parameters for Passage-Based Reading Items Using NLP
%A Liu, Ruitao
%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 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-0-0
%F liu-2026-predicting
%X This study predicts IRT item difficulty and discrimination for passage-based reading comprehension items using lexical, syntactic, and semantic NLP features. Modeling interactions among passages, stems, and options, Light- GBM with semantic embeddings best predicts difficulty (r = 0.594), while discrimination remains harder to recover from text alone.
%U https://aclanthology.org/2026.aimecon-main.4/
%P 30-35

Markdown (Informal)

[Predicting IRT Parameters for Passage-Based Reading Items Using NLP](https://aclanthology.org/2026.aimecon-main.4/) (Liu, AIME-Con 2026)

• Predicting IRT Parameters for Passage-Based Reading Items Using NLP (Liu, AIME-Con 2026)

ACL

• Ruitao Liu. 2026. Predicting IRT Parameters for Passage-Based Reading Items Using NLP. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 30–35, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).