@inproceedings{zhang-rao-2026-cdm,
title = "{CDM} {Q}-Matrix Discovery with {LLM}s: Fusing Domain Knowledge with Empirical Evidence",
author = "Zhang, Susu and
Rao, V. N. Vimal",
editor = "Wilson, Joshua and
Ormerod, Christopher and
Beiting-Parrish, Magdalen",
booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference ({AIME}-Con): Works in Progress",
month = oct,
year = "2026",
address = "Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States",
publisher = "National Council on Measurement in Education (NCME)",
url = "https://aclanthology.org/2026.aimecon-wip.29/",
pages = "216--230",
ISBN = "979-8-9983004-1-7",
abstract = "We present a method for diagnostic assessment Q-matrix specification combining LLM input with model-based empirical validation. An LLM generates an initial Q-matrix from item content, and response data guide subsequent Q refinement. The approach integrates substantive rationale with empirical evidence to support scalable and measurement theory-grounded diagnostic assessment."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhang-rao-2026-cdm">
<titleInfo>
<title>CDM Q-Matrix Discovery with LLMs: Fusing Domain Knowledge with Empirical Evidence</title>
</titleInfo>
<name type="personal">
<namePart type="given">Susu</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">V</namePart>
<namePart type="given">N</namePart>
<namePart type="given">Vimal</namePart>
<namePart type="family">Rao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</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">979-8-9983004-1-7</identifier>
</relatedItem>
<abstract>We present a method for diagnostic assessment Q-matrix specification combining LLM input with model-based empirical validation. An LLM generates an initial Q-matrix from item content, and response data guide subsequent Q refinement. The approach integrates substantive rationale with empirical evidence to support scalable and measurement theory-grounded diagnostic assessment.</abstract>
<identifier type="citekey">zhang-rao-2026-cdm</identifier>
<location>
<url>https://aclanthology.org/2026.aimecon-wip.29/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>216</start>
<end>230</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T CDM Q-Matrix Discovery with LLMs: Fusing Domain Knowledge with Empirical Evidence
%A Zhang, Susu
%A Rao, V. N. Vimal
%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 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-1-7
%F zhang-rao-2026-cdm
%X We present a method for diagnostic assessment Q-matrix specification combining LLM input with model-based empirical validation. An LLM generates an initial Q-matrix from item content, and response data guide subsequent Q refinement. The approach integrates substantive rationale with empirical evidence to support scalable and measurement theory-grounded diagnostic assessment.
%U https://aclanthology.org/2026.aimecon-wip.29/
%P 216-230
Markdown (Informal)
[CDM Q-Matrix Discovery with LLMs: Fusing Domain Knowledge with Empirical Evidence](https://aclanthology.org/2026.aimecon-wip.29/) (Zhang & Rao, AIME-Con 2026)
ACL