Is your multimodal large language model a good science tutor?
This work addresses the need for better educational AI assistants by providing a framework to evaluate and improve MLLMs as science tutors, though it is incremental as it builds on existing models and datasets.
The paper tackles the problem that multimodal large language models (MLLMs) are evaluated only for answer accuracy in scientific reasoning, ignoring teaching ability in educational contexts, and shows that fine-tuning an underperforming model (Qwen2-VL-2B) using preference optimization improves it into more effective science tutors, with results indicating strong problem-solving does not guarantee high-quality tutoring.
Multimodal large language models (MLLMs) demonstrate impressive performance on scientific reasoning tasks (e.g., ScienceQA). However, most existing benchmarks focus narrowly on the accuracy of the final answer while ignoring other metrics. In particular, when applying MLLMs to educational contexts, the goal is not only correctness but also the ability to teach. In this paper, we propose a framework that evaluates MLLMs as science tutors using a comprehensive educational rubric and a simulated student model that judges the teaching performance of the tutors. Given a list of candidate MLLM science tutors, we use rubric-based student judgments to produce a range of tutor performance scores, identifying both strong and weak tutors. Using the training section of the ScienceQA dataset, we then construct a data set of pairwise comparisons between the outputs of strong and weak tutors. This enables us to apply multiple preference optimization methods to fine-tune an underperforming tutor model (Qwen2-VL-2B) into more effective ones. Our results also show that strong problem-solving skills do not guarantee high-quality tutoring and that performance optimization-guided refinements can yield more educationally aligned tutor models. This approach opens avenues for building MLLMs that serve not only as problem solvers, but as genuinely helpful educational assistants.