Auxiliary Information for Semantic Clustering of Assessment Items

Josiah Hunsberger, Aquia Richburg, Marcus Walker


Abstract
This study identifies a selected transformer-based clustering model for operational medical assessment items that balances within blueprint-topic proximity with cluster separation. Operational analyses showed supplementary alignment with blueprint structure, flagged isolated content areas that may need additional item coverage, and identified dispersed topics for subject matter expert (SME) review.
Anthology ID:
2026.aimecon-main.38
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:
345–354
Language:
URL:
https://aclanthology.org/2026.aimecon-main.38/
DOI:
Bibkey:
Cite (ACL):
Josiah Hunsberger, Aquia Richburg, and Marcus Walker. 2026. Auxiliary Information for Semantic Clustering of Assessment Items. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 345–354, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):
Auxiliary Information for Semantic Clustering of Assessment Items (Hunsberger et al., AIME-Con 2026)
Copy Citation:
PDF:
https://aclanthology.org/2026.aimecon-main.38.pdf