Responsible use of generative AI when creating reading comprehension questions: Inference matters

Zuowei Wang; Michael Flor; Jiyun Zu; Tenaha O’Reilly; Wanjing (Anya) Ma

Responsible use of generative AI when creating reading comprehension questions: Inference matters

Zuowei Wang, Michael Flor, Jiyun Zu, Tenaha O’Reilly, Wanjing Anya Ma

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

AI-generated and expert-created reading comprehension questions can show similar item statistics yet differ in the types of inferences required. This difference stemmed from AI’s failure to follow prompts during an intermediate item generation step. Evaluations of AI-generated items should document prompts and generation steps to identify and mitigate construct-relevant differences.

Anthology ID:: 2026.aimecon-wip.27
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:: 207–210
Language:
URL:: https://aclanthology.org/2026.aimecon-wip.27/
DOI:
Bibkey:
Cite (ACL):: Zuowei Wang, Michael Flor, Jiyun Zu, Tenaha O’Reilly, and Wanjing Anya Ma. 2026. Responsible use of generative AI when creating reading comprehension questions: Inference matters. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 207–210, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):: Responsible use of generative AI when creating reading comprehension questions: Inference matters (Wang et al., AIME-Con 2026)
Copy Citation:
PDF:: https://aclanthology.org/2026.aimecon-wip.27.pdf

PDF Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{wang-etal-2026-responsible,
    title = "Responsible use of generative {AI} when creating reading comprehension questions: Inference matters",
    author = "Wang, Zuowei  and
      Flor, Michael  and
      Zu, Jiyun  and
      O{'}Reilly, Tenaha  and
      Ma, Wanjing Anya",
    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.27/",
    pages = "207--210",
    ISBN = "979-8-9983004-1-7",
    abstract = "AI-generated and expert-created reading comprehension questions can show similar item statistics yet differ in the types of inferences required. This difference stemmed from AI{'}s failure to follow prompts during an intermediate item generation step. Evaluations of AI-generated items should document prompts and generation steps to identify and mitigate construct-relevant differences."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wang-etal-2026-responsible">
    <titleInfo>
        <title>Responsible use of generative AI when creating reading comprehension questions: Inference matters</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Zuowei</namePart>
        <namePart type="family">Wang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Michael</namePart>
        <namePart type="family">Flor</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jiyun</namePart>
        <namePart type="family">Zu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tenaha</namePart>
        <namePart type="family">O’Reilly</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Wanjing</namePart>
        <namePart type="given">Anya</namePart>
        <namePart type="family">Ma</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>AI-generated and expert-created reading comprehension questions can show similar item statistics yet differ in the types of inferences required. This difference stemmed from AI’s failure to follow prompts during an intermediate item generation step. Evaluations of AI-generated items should document prompts and generation steps to identify and mitigate construct-relevant differences.</abstract>
    <identifier type="citekey">wang-etal-2026-responsible</identifier>
    <location>
        <url>https://aclanthology.org/2026.aimecon-wip.27/</url>
    </location>
    <part>
        <date>2026-10</date>
        <extent unit="page">
            <start>207</start>
            <end>210</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T Responsible use of generative AI when creating reading comprehension questions: Inference matters
%A Wang, Zuowei
%A Flor, Michael
%A Zu, Jiyun
%A O’Reilly, Tenaha
%A Ma, Wanjing Anya
%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 wang-etal-2026-responsible
%X AI-generated and expert-created reading comprehension questions can show similar item statistics yet differ in the types of inferences required. This difference stemmed from AI’s failure to follow prompts during an intermediate item generation step. Evaluations of AI-generated items should document prompts and generation steps to identify and mitigate construct-relevant differences.
%U https://aclanthology.org/2026.aimecon-wip.27/
%P 207-210

Download as File

Markdown (Informal)

[Responsible use of generative AI when creating reading comprehension questions: Inference matters](https://aclanthology.org/2026.aimecon-wip.27/) (Wang et al., AIME-Con 2026)

Responsible use of generative AI when creating reading comprehension questions: Inference matters (Wang et al., AIME-Con 2026)

ACL

Zuowei Wang, Michael Flor, Jiyun Zu, Tenaha O’Reilly, and Wanjing Anya Ma. 2026. Responsible use of generative AI when creating reading comprehension questions: Inference matters. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 207–210, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).