Rethinking Pilot Data: Evaluating LLM Synthetic Data for Scale Development

Margarita Olivera-Aguilar; Samuel H. Rikoon; Paul D. Bailey; Michael B. Kruse; Kamal Middlebrook

Rethinking Pilot Data: Evaluating LLM Synthetic Data for Scale Development

Margarita Olivera-Aguilar, Samuel H. Rikoon, Paul D. Bailey, Michael B. Kruse, Kamal Middlebrook

Correct Metadata for

Use this form to create a GitHub issue with structured data describing the correction. You will need a GitHub account. Once you create that issue, the correction will be reviewed by a staff member.

⚠️ Mobile Users: Submitting this form to create a new issue will only work with github.com, not the GitHub Mobile app.

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>. You may use ... for bold, ... for italic, ... for underline, <sc>...</sc> for small-caps, <tt>...<tt> for typewriter text, <url>...</url> for URLs, <a href=...> for hyperlinks, and <par/> for paragraph breaks.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the PDF.)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

This study evaluated whether LLM- synthetic data can support K-12 instrument development. We generated LLM-synthetic datasets under various prompt conditions for two surveys and one assessment. Our findings indicate that while some prompt conditions successfully reproduced the overall latent structure of the instruments, recovery of item-level parameters was generally inadequate.

Anthology ID:: 2026.aimecon-wip.9
Volume:: Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
Month:: October
Year:: 2026
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:: 62–68
Language:
URL:: https://aclanthology.org/2026.aimecon-wip.9/
DOI:
Bibkey:
Cite (ACL):: Margarita Olivera-Aguilar, Samuel H. Rikoon, Paul D. Bailey, Michael B. Kruse, and Kamal Middlebrook. 2026. Rethinking Pilot Data: Evaluating LLM Synthetic Data for Scale Development. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 62–68, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):: Rethinking Pilot Data: Evaluating LLM Synthetic Data for Scale Development (Olivera-Aguilar et al., AIME-Con 2026)
Copy Citation:
PDF:: https://aclanthology.org/2026.aimecon-wip.9.pdf

PDF Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{olivera-aguilar-etal-2026-rethinking,
    title = "Rethinking Pilot Data: Evaluating {LLM} Synthetic Data for Scale Development",
    author = "Olivera-Aguilar, Margarita  and
      Rikoon, Samuel H.  and
      Bailey, Paul D.  and
      Kruse, Michael B.  and
      Middlebrook, Kamal",
    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.9/",
    pages = "62--68",
    ISBN = "979-8-9983004-1-7",
    abstract = "This study evaluated whether LLM- synthetic data can support K-12 instrument development. We generated LLM-synthetic datasets under various prompt conditions for two surveys and one assessment. Our findings indicate that while some prompt conditions successfully reproduced the overall latent structure of the instruments, recovery of item-level parameters was generally inadequate."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="olivera-aguilar-etal-2026-rethinking">
    <titleInfo>
        <title>Rethinking Pilot Data: Evaluating LLM Synthetic Data for Scale Development</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Margarita</namePart>
        <namePart type="family">Olivera-Aguilar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Samuel</namePart>
        <namePart type="given">H</namePart>
        <namePart type="family">Rikoon</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Paul</namePart>
        <namePart type="given">D</namePart>
        <namePart type="family">Bailey</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Michael</namePart>
        <namePart type="given">B</namePart>
        <namePart type="family">Kruse</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kamal</namePart>
        <namePart type="family">Middlebrook</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>This study evaluated whether LLM- synthetic data can support K-12 instrument development. We generated LLM-synthetic datasets under various prompt conditions for two surveys and one assessment. Our findings indicate that while some prompt conditions successfully reproduced the overall latent structure of the instruments, recovery of item-level parameters was generally inadequate.</abstract>
    <identifier type="citekey">olivera-aguilar-etal-2026-rethinking</identifier>
    <location>
        <url>https://aclanthology.org/2026.aimecon-wip.9/</url>
    </location>
    <part>
        <date>2026-10</date>
        <extent unit="page">
            <start>62</start>
            <end>68</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T Rethinking Pilot Data: Evaluating LLM Synthetic Data for Scale Development
%A Olivera-Aguilar, Margarita
%A Rikoon, Samuel H.
%A Bailey, Paul D.
%A Kruse, Michael B.
%A Middlebrook, Kamal
%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 olivera-aguilar-etal-2026-rethinking
%X This study evaluated whether LLM- synthetic data can support K-12 instrument development. We generated LLM-synthetic datasets under various prompt conditions for two surveys and one assessment. Our findings indicate that while some prompt conditions successfully reproduced the overall latent structure of the instruments, recovery of item-level parameters was generally inadequate.
%U https://aclanthology.org/2026.aimecon-wip.9/
%P 62-68

Download as File

Markdown (Informal)

[Rethinking Pilot Data: Evaluating LLM Synthetic Data for Scale Development](https://aclanthology.org/2026.aimecon-wip.9/) (Olivera-Aguilar et al., AIME-Con 2026)

Rethinking Pilot Data: Evaluating LLM Synthetic Data for Scale Development (Olivera-Aguilar et al., AIME-Con 2026)

ACL

Margarita Olivera-Aguilar, Samuel H. Rikoon, Paul D. Bailey, Michael B. Kruse, and Kamal Middlebrook. 2026. Rethinking Pilot Data: Evaluating LLM Synthetic Data for Scale Development. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 62–68, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).