  This study compared the classification performance of Gemini Pro and GPT-4V
in educational settings. Employing visual question answering (VQA) techniques,
the study examined both models' abilities to read text-based rubrics and then
automatically score student-drawn models in science education. We employed both
quantitative and qualitative analyses using a dataset derived from
student-drawn scientific models and employing NERIF (Notation-Enhanced Rubrics
for Image Feedback) prompting methods. The findings reveal that GPT-4V
significantly outperforms Gemini Pro in terms of scoring accuracy and Quadratic
Weighted Kappa. The qualitative analysis reveals that the differences may be
due to the models' ability to process fine-grained texts in images and overall
image classification performance. Even adapting the NERIF approach by further
de-sizing the input images, Gemini Pro seems not able to perform as well as
GPT-4V. The findings suggest GPT-4V's superior capability in handling complex
multimodal educational tasks. The study concludes that while both models
represent advancements in AI, GPT-4V's higher performance makes it a more
suitable tool for educational applications involving multimodal data
interpretation.
