<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mojoyinola-etal-2025-enhancing">
    <titleInfo>
        <title>Enhancing Item Difficulty Prediction in Large-scale Assessment with Large Language Model</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Mubarak</namePart>
        <namePart type="family">Mojoyinola</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Olasunkanmi</namePart>
        <namePart type="given">James</namePart>
        <namePart type="family">Kehinde</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Judy</namePart>
        <namePart type="family">Tang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Joshua</namePart>
            <namePart type="family">Wilson</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Christopher</namePart>
            <namePart type="family">Ormerod</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Magdalen</namePart>
            <namePart type="family">Beiting Parrish</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>National Council on Measurement in Education (NCME)</publisher>
            <place>
                <placeTerm type="text">Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-218-84229-1</identifier>
    </relatedItem>
    <abstract>Field testing is a resource-intensive bottleneck in test development. This study applied an interpretable framework that leverages a Large Language Model (LLM) for structured feature extraction from TIMSS items. These features will train several classifiers, whose predictions will be explained using SHAP, providing actionable, diagnostic insights insights for item writers.</abstract>
    <identifier type="citekey">mojoyinola-etal-2025-enhancing</identifier>
    <location>
        <url>https://aclanthology.org/2025.aimecon-wip.27/</url>
    </location>
    <part>
        <date>2025-10</date>
        <extent unit="page">
            <start>218</start>
            <end>222</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Enhancing Item Difficulty Prediction in Large-scale Assessment with Large Language Model
%A Mojoyinola, Mubarak
%A Kehinde, Olasunkanmi James
%A Tang, Judy
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
%D 2025
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-218-84229-1
%F mojoyinola-etal-2025-enhancing
%X Field testing is a resource-intensive bottleneck in test development. This study applied an interpretable framework that leverages a Large Language Model (LLM) for structured feature extraction from TIMSS items. These features will train several classifiers, whose predictions will be explained using SHAP, providing actionable, diagnostic insights insights for item writers.
%U https://aclanthology.org/2025.aimecon-wip.27/
%P 218-222

Markdown (Informal)

[Enhancing Item Difficulty Prediction in Large-scale Assessment with Large Language Model](https://aclanthology.org/2025.aimecon-wip.27/) (Mojoyinola et al., AIME-Con 2025)

• Enhancing Item Difficulty Prediction in Large-scale Assessment with Large Language Model (Mojoyinola et al., AIME-Con 2025)

ACL

• Mubarak Mojoyinola, Olasunkanmi James Kehinde, and Judy Tang. 2025. Enhancing Item Difficulty Prediction in Large-scale Assessment with Large Language Model. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 218–222, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).