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    <abstract>This study aims to develop and evaluate an AI-based platform that automatically grade and classify problem-solving strategies and error types in students’ handwritten fraction representations involving number lines. The model development procedures, and preliminary evaluation results comparing with available LLMs and human expert annotations are reported.</abstract>
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%0 Conference Proceedings
%T Automated Diagnosis of Students’ Number Line Strategies for Fractions
%A Wang, Zhizhi
%A Zhang, Dake
%A Li, Min
%A Tao, Yuhan
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
%D 2025
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-218-84229-1
%F wang-etal-2025-automated
%X This study aims to develop and evaluate an AI-based platform that automatically grade and classify problem-solving strategies and error types in students’ handwritten fraction representations involving number lines. The model development procedures, and preliminary evaluation results comparing with available LLMs and human expert annotations are reported.
%U https://aclanthology.org/2025.aimecon-wip.21/
%P 178-184

Markdown (Informal)

[Automated Diagnosis of Students’ Number Line Strategies for Fractions](https://aclanthology.org/2025.aimecon-wip.21/) (Wang et al., AIME-Con 2025)

• Automated Diagnosis of Students’ Number Line Strategies for Fractions (Wang et al., AIME-Con 2025)

ACL

• Zhizhi Wang, Dake Zhang, Min Li, and Yuhan Tao. 2025. Automated Diagnosis of Students’ Number Line Strategies for Fractions. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 178–184, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).