Assessing Large Language Model Performance in Post-Certification-Examination Comment Categorization

Huaping Sun; Kristin O’Brien; Colleen Burke Kave; David Shin; Qiao Lin; Jeffrey Marc Lyness

Assessing Large Language Model Performance in Post-Certification-Examination Comment Categorization

Huaping Sun, Kristin O’Brien, Colleen Burke Kave, David Shin, Qiao Lin, Jeffrey Marc Lyness

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Abstract

This study evaluated an LLM for analyzing 1,406 Neurocritical Care examination comments coded for sentiment and thematic categories. Human raters showed high agreement, whereas LLM-human agreement was moderate. Thematic definitions reduced performance, but human-coded examples improved accuracy. LLMs may support preliminary coding, although human review remains necessary.

Anthology ID:: 2026.aimecon-wip.8
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:: 57–61
Language:
URL:: https://aclanthology.org/2026.aimecon-wip.8/
DOI:
Bibkey:
Cite (ACL):: Huaping Sun, Kristin O’Brien, Colleen Burke Kave, David Shin, Qiao Lin, and Jeffrey Marc Lyness. 2026. Assessing Large Language Model Performance in Post-Certification-Examination Comment Categorization. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 57–61, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):: Assessing Large Language Model Performance in Post-Certification-Examination Comment Categorization (Sun et al., AIME-Con 2026)
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PDF:: https://aclanthology.org/2026.aimecon-wip.8.pdf

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@inproceedings{sun-etal-2026-assessing,
    title = "Assessing Large Language Model Performance in Post-Certification-Examination Comment Categorization",
    author = "Sun, Huaping  and
      O{'}Brien, Kristin  and
      Kave, Colleen Burke  and
      Shin, David  and
      Lin, Qiao  and
      Lyness, Jeffrey Marc",
    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.8/",
    pages = "57--61",
    ISBN = "979-8-9983004-1-7",
    abstract = "This study evaluated an LLM for analyzing 1,406 Neurocritical Care examination comments coded for sentiment and thematic categories. Human raters showed high agreement, whereas LLM-human agreement was moderate. Thematic definitions reduced performance, but human-coded examples improved accuracy. LLMs may support preliminary coding, although human review remains necessary."
}

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%0 Conference Proceedings
%T Assessing Large Language Model Performance in Post-Certification-Examination Comment Categorization
%A Sun, Huaping
%A O’Brien, Kristin
%A Kave, Colleen Burke
%A Shin, David
%A Lin, Qiao
%A Lyness, Jeffrey Marc
%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 sun-etal-2026-assessing
%X This study evaluated an LLM for analyzing 1,406 Neurocritical Care examination comments coded for sentiment and thematic categories. Human raters showed high agreement, whereas LLM-human agreement was moderate. Thematic definitions reduced performance, but human-coded examples improved accuracy. LLMs may support preliminary coding, although human review remains necessary.
%U https://aclanthology.org/2026.aimecon-wip.8/
%P 57-61

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Markdown (Informal)

[Assessing Large Language Model Performance in Post-Certification-Examination Comment Categorization](https://aclanthology.org/2026.aimecon-wip.8/) (Sun et al., AIME-Con 2026)

Assessing Large Language Model Performance in Post-Certification-Examination Comment Categorization (Sun et al., AIME-Con 2026)

ACL

Huaping Sun, Kristin O’Brien, Colleen Burke Kave, David Shin, Qiao Lin, and Jeffrey Marc Lyness. 2026. Assessing Large Language Model Performance in Post-Certification-Examination Comment Categorization. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 57–61, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).