Developing and Validating an Automatic Scoring Model for Chatbot-Based Conversational Speech

Danwei Cai; Audrey Kittredge; Ben Naismith; Xiangying Jiang; Kevin Yancey

Developing and Validating an Automatic Scoring Model for Chatbot-Based Conversational Speech

Danwei Cai, Audrey Kittredge, Ben Naismith, Xiangying Jiang, Kevin Yancey

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 paper describes the development and validation of an automated scoring model for open-ended chatbot-based conversational speech among English learners in the Duolingo learning app. The model strongly predicted human ratings, produced reliable scores, and showed concurrent validity with Duolingo English Test speaking items, demonstrating stealth proficiency assessment at scale.

Anthology ID:: 2026.aimecon-main.11
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:: 100–108
Language:
URL:: https://aclanthology.org/2026.aimecon-main.11/
DOI:
Bibkey:
Cite (ACL):: Danwei Cai, Audrey Kittredge, Ben Naismith, Xiangying Jiang, and Kevin Yancey. 2026. Developing and Validating an Automatic Scoring Model for Chatbot-Based Conversational Speech. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 100–108, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):: Developing and Validating an Automatic Scoring Model for Chatbot-Based Conversational Speech (Cai et al., AIME-Con 2026)
Copy Citation:
PDF:: https://aclanthology.org/2026.aimecon-main.11.pdf

PDF Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{cai-etal-2026-developing,
    title = "Developing and Validating an Automatic Scoring Model for Chatbot-Based Conversational Speech",
    author = "Cai, Danwei  and
      Kittredge, Audrey  and
      Naismith, Ben  and
      Jiang, Xiangying  and
      Yancey, Kevin",
    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.11/",
    pages = "100--108",
    ISBN = "979-8-9983004-0-0",
    abstract = "This paper describes the development and validation of an automated scoring model for open-ended chatbot-based conversational speech among English learners in the Duolingo learning app. The model strongly predicted human ratings, produced reliable scores, and showed concurrent validity with Duolingo English Test speaking items, demonstrating stealth proficiency assessment at scale."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="cai-etal-2026-developing">
    <titleInfo>
        <title>Developing and Validating an Automatic Scoring Model for Chatbot-Based Conversational Speech</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Danwei</namePart>
        <namePart type="family">Cai</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Audrey</namePart>
        <namePart type="family">Kittredge</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ben</namePart>
        <namePart type="family">Naismith</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xiangying</namePart>
        <namePart type="family">Jiang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kevin</namePart>
        <namePart type="family">Yancey</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): Full Papers</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">979-8-9983004-0-0</identifier>
    </relatedItem>
    <abstract>This paper describes the development and validation of an automated scoring model for open-ended chatbot-based conversational speech among English learners in the Duolingo learning app. The model strongly predicted human ratings, produced reliable scores, and showed concurrent validity with Duolingo English Test speaking items, demonstrating stealth proficiency assessment at scale.</abstract>
    <identifier type="citekey">cai-etal-2026-developing</identifier>
    <location>
        <url>https://aclanthology.org/2026.aimecon-main.11/</url>
    </location>
    <part>
        <date>2026-10</date>
        <extent unit="page">
            <start>100</start>
            <end>108</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T Developing and Validating an Automatic Scoring Model for Chatbot-Based Conversational Speech
%A Cai, Danwei
%A Kittredge, Audrey
%A Naismith, Ben
%A Jiang, Xiangying
%A Yancey, Kevin
%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 cai-etal-2026-developing
%X This paper describes the development and validation of an automated scoring model for open-ended chatbot-based conversational speech among English learners in the Duolingo learning app. The model strongly predicted human ratings, produced reliable scores, and showed concurrent validity with Duolingo English Test speaking items, demonstrating stealth proficiency assessment at scale.
%U https://aclanthology.org/2026.aimecon-main.11/
%P 100-108

Download as File

Markdown (Informal)

[Developing and Validating an Automatic Scoring Model for Chatbot-Based Conversational Speech](https://aclanthology.org/2026.aimecon-main.11/) (Cai et al., AIME-Con 2026)

Developing and Validating an Automatic Scoring Model for Chatbot-Based Conversational Speech (Cai et al., AIME-Con 2026)

ACL

Danwei Cai, Audrey Kittredge, Ben Naismith, Xiangying Jiang, and Kevin Yancey. 2026. Developing and Validating an Automatic Scoring Model for Chatbot-Based Conversational Speech. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 100–108, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).