Implementation Considerations for Automated AI Grading of Student Work

Zewei Tian, Alex Liu, Lief Esbenshade, Shawon Sarkar, Zachary Zhang, Kevin He, Min Sun


Abstract
19 K-12 teachers participated in a co-design pilot study of an AI education platform, testing assessment grading. Teachers valued AI’s rapid narrative feedback for formative assessment but distrusted automated scoring, preferring human oversight. Students appreciated immediate feedback but remained skeptical of AI-only grading, highlighting needs for trustworthy, teacher-centered AI tools.
Anthology ID:
2025.aimecon-main.2
Volume:
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
Month:
October
Year:
2025
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:
9–20
Language:
URL:
https://aclanthology.org/2025.aimecon-main.2/
DOI:
Bibkey:
Cite (ACL):
Zewei Tian, Alex Liu, Lief Esbenshade, Shawon Sarkar, Zachary Zhang, Kevin He, and Min Sun. 2025. Implementation Considerations for Automated AI Grading of Student Work. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 9–20, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):
Implementation Considerations for Automated AI Grading of Student Work (Tian et al., AIME-Con 2025)
Copy Citation:
PDF:
https://aclanthology.org/2025.aimecon-main.2.pdf