Stanford: Tutor CoPilot – A Human-AI Approach for Scaling Real-Time Expertise

2024-11-13

Summary of https://studentsupportaccelerator.org/sites/default/files/Tutor_CoPilot.pdf This paper describes the development and evaluation of "Tutor CoPilot," a human-AI system designed to improve the quality of tutoring sessions, particularly for novice tutors working with K-12 students from historically underserved communities. The system leverages large language models (LLMs) trained on expert thinking to generate real-time,...

Summary of https://studentsupportaccelerator.org/sites/default/files/Tutor_CoPilot.pdf

This paper describes the development and evaluation of "Tutor CoPilot," a human-AI system designed to improve the quality of tutoring sessions, particularly for novice tutors working with K-12 students from historically underserved communities.

The system leverages large language models (LLMs) trained on expert thinking to generate real-time, expert-like suggestions for tutors, providing them with guidance on how to respond to student questions and mistakes.

The research utilizes a randomized controlled trial with over 900 tutors and 1,800 students, demonstrating that Tutor CoPilot significantly improves student learning outcomes, particularly for lower-rated tutors.

Additionally, the study finds that tutors using Tutor CoPilot are more likely to use high-quality pedagogical strategies that foster deeper understanding.

This approach offers a scalable and cost-effective alternative to traditional training programs, making high-quality education more accessible to all students.

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