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Researchers introduce CSTutorBench, a pedagogically grounded benchmark for evaluating smaller language models as AI tutors in block-based programming environments, addressing privacy and cost concerns around deploying large models in schools. Testing 11 models ranging from 4B to 120B parameters reveals that while SLMs excel at surface-level communication, they struggle with deeper tutoring behaviors like avoiding direct answers and tracking student debugging progress. Model family and instruction-tuning approach proved better predictors of tutoring quality than parameter size, and targeted prompt engineering improved performance in 10 of 11 tested models.
Why it matters: As schools seek cost-effective, privacy-preserving AI tutoring solutions, this benchmark provides concrete guidance for selecting and optimizing smaller language models for educational deployment in underrepresented domains like programming.
