Human–AI Collaboration in Educational Measurement: Transforming Assessment Data into Educational Action

Laura C. Egan, Judy H. Tang


Abstract
This paper describes a framework for human–AI collaboration in educational measurement that connects four workflow components. The framework integrates educational measurement and responsible AI principles, emphasizing AI tools as support for human expertise. Illustrative applications demonstrate how human–AI collaboration can strengthen assessment processes while maintaining measurement quality and integrity.
Anthology ID:
2026.aimecon-sessions.23
Volume:
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Coordinated Session 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:
219–224
Language:
URL:
https://aclanthology.org/2026.aimecon-sessions.23/
DOI:
Bibkey:
Cite (ACL):
Laura C. Egan and Judy H. Tang. 2026. Human–AI Collaboration in Educational Measurement: Transforming Assessment Data into Educational Action. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Coordinated Session Papers, pages 219–224, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):
Human–AI Collaboration in Educational Measurement: Transforming Assessment Data into Educational Action (Egan & Tang, AIME-Con 2026)
Copy Citation:
PDF:
https://aclanthology.org/2026.aimecon-sessions.23.pdf