Improving Assessment of Tutoring Practices using Retrieval-Augmented Generation

This paper evaluates GPT-3.5 and GPT-4 models' ability to automatically assess novice human tutors' use of social-emotional learning strategies during one-on-one middle-school math tutoring sessions, comparing four prompting strategies including RAG. The work aims to enable scalable, automated evaluation of tutor competencies to inform personalized tutor training programs.

One-on-one tutoring is an effective instructional method for enhancing learning, yet its efficacy hinges on tutor competencies. Novice math tutors often prioritize content-specific guidance, neglecting aspects such as social-emotional learning. Social-emotional learning promotes equity and inclusion and nurturing relationships with students, which is crucial for holistic student development. Assessing the competencies of tutors accurately and efficiently can drive the development of tailored tut

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