@inproceedings{foltz-etal-2026-principled,
title = "Principled Approaches to Building {AI} Partners for Assessing and Supporting Small-Group Collaboration",
author = "Foltz, Peter W and
Chandler, Chelsea and
Ko, Mon-Lin Monica",
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.45/",
pages = "350--357",
ISBN = "979-8-9983004-1-7",
abstract = "Collaboration is complex and multifaceted, blending cognitive, social, and emotional components that resist simple measurement. We present a framework linking what is measured, where, and how evidence is warranted. This paper synthesizes key design dimensions for AI collaboration partners and demonstrates how they embed measurement science into practice."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="foltz-etal-2026-principled">
<titleInfo>
<title>Principled Approaches to Building AI Partners for Assessing and Supporting Small-Group Collaboration</title>
</titleInfo>
<name type="personal">
<namePart type="given">Peter</namePart>
<namePart type="given">W</namePart>
<namePart type="family">Foltz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chelsea</namePart>
<namePart type="family">Chandler</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mon-Lin</namePart>
<namePart type="given">Monica</namePart>
<namePart type="family">Ko</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>Collaboration is complex and multifaceted, blending cognitive, social, and emotional components that resist simple measurement. We present a framework linking what is measured, where, and how evidence is warranted. This paper synthesizes key design dimensions for AI collaboration partners and demonstrates how they embed measurement science into practice.</abstract>
<identifier type="citekey">foltz-etal-2026-principled</identifier>
<location>
<url>https://aclanthology.org/2026.aimecon-wip.45/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>350</start>
<end>357</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Principled Approaches to Building AI Partners for Assessing and Supporting Small-Group Collaboration
%A Foltz, Peter W.
%A Chandler, Chelsea
%A Ko, Mon-Lin Monica
%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 foltz-etal-2026-principled
%X Collaboration is complex and multifaceted, blending cognitive, social, and emotional components that resist simple measurement. We present a framework linking what is measured, where, and how evidence is warranted. This paper synthesizes key design dimensions for AI collaboration partners and demonstrates how they embed measurement science into practice.
%U https://aclanthology.org/2026.aimecon-wip.45/
%P 350-357
Markdown (Informal)
[Principled Approaches to Building AI Partners for Assessing and Supporting Small-Group Collaboration](https://aclanthology.org/2026.aimecon-wip.45/) (Foltz et al., AIME-Con 2026)
ACL