3 papers
cs.CL2026
The Aftermath of DrawEduMath: Vision Language Models Underperform with Struggling Students and Misdiagnose Errors
Li Lucy, Albert Zhang, Nathan Anderson +2
Effective mathematics education requires identifying and responding to students' mistakes. For AI to support pedagogical applications, models must perform well across different lev…
cs.CL2025
DrawEduMath: Evaluating Vision Language Models with Expert-Annotated Students' Hand-Drawn Math Images
Sami Baral, Li Lucy, Ryan Knight +4
In real-world settings, vision language models (VLMs) should robustly handle naturalistic, noisy visual content as well as domain-specific language and concepts. For example, K-12…
cs.CL2024
Mathfish: Evaluating Language Model Math Reasoning via Grounding in Educational Curricula
Li Lucy, Tal August, Rose E. Wang +3
To ensure that math curriculum is grade-appropriate and aligns with critical skills or concepts in accordance with educational standards, pedagogical experts can spend months caref…