The integration of artificial intelligence (ai) tools in education has shown great potential to improve teaching methods and learning experiences, especially where access to experienced educators is limited. A prominent ai-based approach is the use of language models (LMs) to assist tutors in real-time. These systems can provide expert-like suggestions that help tutors improve student engagement and performance. By equipping novice educators with real-time guidance, ai tools have the potential to close the education experience gap and create a more equitable learning environment. This is particularly crucial in classrooms with diverse student abilities and educational backgrounds.
The fundamental problem in education is the high cost and limited scalability of traditional tutoring training programs. Comprehensive professional development sessions can cost up to $3,300 per teacher per year, making it difficult for schools with tight budgets to offer quality training. These programs often require tutors to invest a lot of time outside of their teaching hours, making them impractical for part-time educators. Additionally, many professional development programs must be aligned with the specific needs of novice tutors, which means they do not address the dynamic, real-time challenges they face during live tutoring sessions. Consequently, many tutors develop their skills on the job, resulting in inconsistent teaching quality and missed learning opportunities for students.
Educators have turned to professional development workshops and training seminars to improve their skills. However, these methods are not always effective due to their static nature, which does not meet the real-time interaction needs of teachers. To address this, some educators have tried using online forums and support networks, but they lack the structured feedback necessary for professional growth. Additionally, tailoring generic training programs for specific educational settings remains a challenge, and many tutors, particularly those working in underserved communities, find it difficult to implement these strategies effectively.
Researchers at Stanford University developed Copilot Tutora collaborative human-ai system designed to provide real-time guidance to tutors during live tutoring sessions. Tutor CoPilot aims to replicate the decision-making process of expert educators by providing practical, context-specific suggestions similar to those of experts. The system uses think-aloud protocols captured by experienced tutors to train the ai model to provide real-time feedback. This innovative approach allows less experienced tutors to provide high-quality instruction that closely aligns with best teaching practices.
Tutor CoPilot works by integrating within a virtual tutoring platform, where tutors can activate it during sessions for immediate assistance. The ai system then analyzes the context of the conversation and the topic of the lesson to offer suggestions that the tutor can implement instantly. Suggestions include asking guiding questions to encourage student reasoning, providing hints to support problem solving, and affirming correct answers. Tutor CoPilot allows tutors to customize these suggestions, making it convenient to adapt to each student's unique needs. The platform also includes a security mechanism that anonymizes the names of students and tutors, guaranteeing user privacy during interactions.
The performance of Tutor CoPilot was tested in a large-scale, randomized, controlled trial involving 900 tutors and 1,800 students from Title I schools. The results were significant: students working with tutors using Tutor CoPilot had four percentage points more likely to master math topics than the control group, where only 62% of students achieved mastery. Interestingly, the positive impact was even greater for tutors initially rated as less effective. For these tutors, the mastery rate increased by nine percentage points, closing the gap between the most and least experienced educators. The study also found that Tutor CoPilot costs only $20 per tutor per year, making it a cost-effective alternative to traditional training programs.
Key findings revealed that Tutor CoPilot frequently encouraged tutors to employ high-quality pedagogical strategies. For example, tutors using the system were more likely to prompt students to explain their reasoning, use guiding questions to promote deeper understanding, and avoid simply revealing answers. These strategies are aligned with best practices for effective teaching and have been shown to significantly improve student outcomes. Additionally, interviews with tutors indicated that they found the system useful for breaking down complex concepts. However, occasional problems with the tool provided suggestions that needed adjustment to grade level.
Key findings from Tutor CoPilot research:
- The study involved 900 tutors and 1,800 K-12 students from underserved communities.
- Students working with Tutor CoPilot were four percentage points more likely to achieve mastery of the topic.
- Tutors rated as least effective showed the greatest improvement, with their students' mastery rates increasing by nine percentage points.
- Tutor CoPilot costs just $20 per tutor per year, compared to traditional training programs, which cost more than $3,300 per teacher.
- The system encourages the use of high-quality teaching strategies, such as prompting students to explain their thinking and ask guiding questions.
In conclusion, the study results show that the integration of human-ai collaborative systems such as Tutor CoPilot in education can significantly improve the quality of teaching, particularly in underserved communities. The research team demonstrated that Tutor CoPilot improves the effectiveness of novice tutors and provides a scalable solution to improve educational outcomes across diverse student populations. At a fraction of the cost of traditional training programs, Tutor CoPilot offers a promising path to making high-quality education accessible to all students.
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