EDU-CIRCUIT-HW: Evaluating Multimodal Large Language Models on Real-World University-Level STEM Student Handwritten Solutions

1. 1Collect and annotate a large dataset of university-level STEM student handwritten solutions
2. 2Develop and evaluate multimodal large language models on this dataset to assess their ability to accurately interpret mathematical formulas, diagrams, and textual reasoning
3. 3Compare the performance of different models and identify areas for improvement
4. 4Use the insights gained to inform the development of more effective automated grading and feedback systems

AI researchers and educators can benefit from this study as it provides a new benchmark for evaluating multimodal large language models, which can help improve the accuracy of automated grading and feedback systems

💡 Multimodal large language models can be effectively evaluated on real-world university-level STEM student handwritten solutions using a carefully designed benchmark

📝 Evaluating multimodal LLMs on real-world STEM student handwritten solutions 🤖