@inproceedings{kwako-etal-2026-llm,
title = "{LLM}-Based Rubric Refinement in Multi-Agent Automated Scoring",
author = "Kwako, Alexander and
Everett, Cristina and
Wang, Harry",
editor = "Wilson, Joshua and
Ormerod, Christopher and
Beiting-Parrish, Magdalen",
booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference ({AIME}-Con): Coordinated Session Papers",
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-sessions.2/",
pages = "9--22",
ISBN = "979-8-9983004-2-4",
abstract = "In this study, we marry automated scoring with automated rubric revision in an iterative, mutually-reinforcing process. One LLM agent scores student responses; a second, the Error Analysis Agent, examines human{--}engine scoring discrepancies and suggests rubric revisions. We show that this positive feedback loop improves automated scoring performance."
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%0 Conference Proceedings
%T LLM-Based Rubric Refinement in Multi-Agent Automated Scoring
%A Kwako, Alexander
%A Everett, Cristina
%A Wang, Harry
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting-Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Coordinated Session Papers
%D 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-2-4
%F kwako-etal-2026-llm
%X In this study, we marry automated scoring with automated rubric revision in an iterative, mutually-reinforcing process. One LLM agent scores student responses; a second, the Error Analysis Agent, examines human–engine scoring discrepancies and suggests rubric revisions. We show that this positive feedback loop improves automated scoring performance.
%U https://aclanthology.org/2026.aimecon-sessions.2/
%P 9-22
Markdown (Informal)
[LLM-Based Rubric Refinement in Multi-Agent Automated Scoring](https://aclanthology.org/2026.aimecon-sessions.2/) (Kwako et al., AIME-Con 2026)
ACL
- Alexander Kwako, Cristina Everett, and Harry Wang. 2026. LLM-Based Rubric Refinement in Multi-Agent Automated Scoring. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Coordinated Session Papers, pages 9–22, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).