Developing and evaluating an AI agent to read and interpret learning maps

Dante Cisterna, Jonathan Schuster, Amber Samson


Abstract
We describe the development and evaluation of an AI conversational agent designed to interpret learning maps. Results indicate the agent can accurately identify map components and structures, especially when different informational formats are combined. Findings highlight the tool’s feasibility to inform the development of tools that use and interpret learning maps for teachers.
Anthology ID:
2026.aimecon-sessions.13
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:
110–117
Language:
URL:
https://aclanthology.org/2026.aimecon-sessions.13/
DOI:
Bibkey:
Cite (ACL):
Dante Cisterna, Jonathan Schuster, and Amber Samson. 2026. Developing and evaluating an AI agent to read and interpret learning maps. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Coordinated Session Papers, pages 110–117, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):
Developing and evaluating an AI agent to read and interpret learning maps (Cisterna et al., AIME-Con 2026)
Copy Citation:
PDF:
https://aclanthology.org/2026.aimecon-sessions.13.pdf