@inproceedings{guo-etal-2026-scaling,
title = "Scaling Human-{AI} Collaboration: Translating Multi-Source Assessment Data into Formative Learner Profiles",
author = "Guo, Hongwen and
Johnson, Matthew S and
Saldivia, Luis and
Worthington, Michelle and
Lee, Jeremy and
Ercikan, Kadriye",
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.24/",
pages = "225--235",
ISBN = "979-8-9983004-2-4",
abstract = "We present a scalable dual-agent architecture translating multi-source assessment data {--} integrating performance with process logs {--} into formative data insights. Decoupling classification from text generation, embedding expert rubrics, and optimizing latency enables rapid first-draft generation. These insights reveal underlying learning behaviors, facilitating targeted intervention without increasing teachers' cognitive burden."
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%0 Conference Proceedings
%T Scaling Human-AI Collaboration: Translating Multi-Source Assessment Data into Formative Learner Profiles
%A Guo, Hongwen
%A Johnson, Matthew S.
%A Saldivia, Luis
%A Worthington, Michelle
%A Lee, Jeremy
%A Ercikan, Kadriye
%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 guo-etal-2026-scaling
%X We present a scalable dual-agent architecture translating multi-source assessment data – integrating performance with process logs – into formative data insights. Decoupling classification from text generation, embedding expert rubrics, and optimizing latency enables rapid first-draft generation. These insights reveal underlying learning behaviors, facilitating targeted intervention without increasing teachers’ cognitive burden.
%U https://aclanthology.org/2026.aimecon-sessions.24/
%P 225-235
Markdown (Informal)
[Scaling Human-AI Collaboration: Translating Multi-Source Assessment Data into Formative Learner Profiles](https://aclanthology.org/2026.aimecon-sessions.24/) (Guo et al., AIME-Con 2026)
ACL
- Hongwen Guo, Matthew S Johnson, Luis Saldivia, Michelle Worthington, Jeremy Lee, and Kadriye Ercikan. 2026. Scaling Human-AI Collaboration: Translating Multi-Source Assessment Data into Formative Learner Profiles. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Coordinated Session Papers, pages 225–235, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).