Supporting Distractor Quality Review Through Interpretable Semantic and Lexical Diagnostics

Michelle Banawan; Shubham Chakraborty; Katerina Christhilf; Linh Huynh; Tracy Arner; Danielle S. McNamara

Supporting Distractor Quality Review Through Interpretable Semantic and Lexical Diagnostics

Michelle Banawan, Shubham Chakraborty, Katerina Christhilf, Linh Huynh, Tracy Arner, Danielle McNamara

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Abstract

MCQ-Diag is a diagnostic tool for reviewing distractor quality in multiple-choice assessments through three interpretable indicators: semantic plausibility, semantic uniqueness, and lexical distinctiveness. Rather than assigning automated judgments, it presents these as evidence within an interactive review environment. Semantic plausibility and lexical distinctiveness show modest, statistically significant validity against expert ratings.

Anthology ID:: 2026.aimecon-main.28
Volume:: Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
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:: 253–259
Language:
URL:: https://aclanthology.org/2026.aimecon-main.28/
DOI:
Bibkey:
Cite (ACL):: Michelle Banawan, Shubham Chakraborty, Katerina Christhilf, Linh Huynh, Tracy Arner, and Danielle McNamara. 2026. Supporting Distractor Quality Review Through Interpretable Semantic and Lexical Diagnostics. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 253–259, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):: Supporting Distractor Quality Review Through Interpretable Semantic and Lexical Diagnostics (Banawan et al., AIME-Con 2026)
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PDF:: https://aclanthology.org/2026.aimecon-main.28.pdf

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@inproceedings{banawan-etal-2026-supporting,
    title = "Supporting Distractor Quality Review Through Interpretable Semantic and Lexical Diagnostics",
    author = "Banawan, Michelle  and
      Chakraborty, Shubham  and
      Christhilf, Katerina  and
      Huynh, Linh  and
      Arner, Tracy  and
      McNamara, Danielle",
    editor = "Wilson, Joshua  and
      Ormerod, Christopher  and
      Beiting-Parrish, Magdalen",
    booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference ({AIME}-Con): Full Papers",
    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-main.28/",
    pages = "253--259",
    ISBN = "979-8-9983004-0-0",
    abstract = "MCQ-Diag is a diagnostic tool for reviewing distractor quality in multiple-choice assessments through three interpretable indicators: semantic plausibility, semantic uniqueness, and lexical distinctiveness. Rather than assigning automated judgments, it presents these as evidence within an interactive review environment. Semantic plausibility and lexical distinctiveness show modest, statistically significant validity against expert ratings."
}

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%0 Conference Proceedings
%T Supporting Distractor Quality Review Through Interpretable Semantic and Lexical Diagnostics
%A Banawan, Michelle
%A Chakraborty, Shubham
%A Christhilf, Katerina
%A Huynh, Linh
%A Arner, Tracy
%A McNamara, Danielle
%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 banawan-etal-2026-supporting
%X MCQ-Diag is a diagnostic tool for reviewing distractor quality in multiple-choice assessments through three interpretable indicators: semantic plausibility, semantic uniqueness, and lexical distinctiveness. Rather than assigning automated judgments, it presents these as evidence within an interactive review environment. Semantic plausibility and lexical distinctiveness show modest, statistically significant validity against expert ratings.
%U https://aclanthology.org/2026.aimecon-main.28/
%P 253-259

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Markdown (Informal)

[Supporting Distractor Quality Review Through Interpretable Semantic and Lexical Diagnostics](https://aclanthology.org/2026.aimecon-main.28/) (Banawan et al., AIME-Con 2026)

Supporting Distractor Quality Review Through Interpretable Semantic and Lexical Diagnostics (Banawan et al., AIME-Con 2026)

ACL

Michelle Banawan, Shubham Chakraborty, Katerina Christhilf, Linh Huynh, Tracy Arner, and Danielle McNamara. 2026. Supporting Distractor Quality Review Through Interpretable Semantic and Lexical Diagnostics. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 253–259, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).