MMTutorBench: The First Multimodal Benchmark for AI Math Tutoring
This addresses the need for better evaluation of AI tutoring systems in education, though it is incremental as it builds on existing multimodal models and benchmarks.
The authors tackled the lack of benchmarks for AI math tutoring by introducing MMTutorBench, a multimodal benchmark with 685 problems, and found that proprietary models outperform open-source ones but still lag behind human tutors, with OCR pipelines degrading performance and rubric-based evaluation proving reliable.
Effective math tutoring requires not only solving problems but also diagnosing students' difficulties and guiding them step by step. While multimodal large language models (MLLMs) show promise, existing benchmarks largely overlook these tutoring skills. We introduce MMTutorBench, the first benchmark for AI math tutoring, consisting of 685 problems built around pedagogically significant key-steps. Each problem is paired with problem-specific rubrics that enable fine-grained evaluation across six dimensions, and structured into three tasks-Insight Discovery, Operation Formulation, and Operation Execution. We evaluate 12 leading MLLMs and find clear performance gaps between proprietary and open-source systems, substantial room compared to human tutors, and consistent trends across input variants: OCR pipelines degrade tutoring quality, few-shot prompting yields limited gains, and our rubric-based LLM-as-a-Judge proves highly reliable. These results highlight both the difficulty and diagnostic value of MMTutorBench for advancing AI tutoring.