Effective mathematics education requires identifying and responding to students’ mistakes. For AI to support pedagogical applications, models must perform well across different levels of student proficiency. Our work provides an extensive, year-long snapshot of how 11 vision-language models (VLMs) perform on DrawEduMath, a QA benchmark involving real students’ handwritten, hand-drawn responses to math problems. We find that models’ weaknesses concentrate on a core component of math education: student error. All evaluated VLMs underperform when describing work from students who may require more pedagogical help, and across all QA, they struggle the most on questions related to assessing student error. Thus, while VLMs may be optimized to be math problem solving experts, our results suggest that they require alternative development incentives to adequately support educational use cases.

Lingua is an application developed for the Church of Jesus Christ of Latter-day Saints that performs both real-time interpretation of live speeches and automatic video dubbing (AVD). Like other AVD systems, it can perform synchronized automatic dubbing, given video files and optionally, corresponding text files using a traditional ASR–MT–TTS pipeline. Lingua’s unique contribution is that it can also operate in real-time with a slight delay of a few seconds to interpret live speeches. If no source-language script is provided, the translations are exactly as recognized by ASR and translated by MT. If a script is provided, Lingua matches the recognized ASR segments with script segments and passes the latter to MT for translation and subsequent TTS. If a human translation is also provided, it is passed directly to TTS. Lingua switches between these modes dynamically, enabling translation of off-script comments and different levels of quality for multiple languages. (see extended abstract)