AI-enhanced tutoring: Bridging the achievement gap in American education

eSchool News

Key points:

...

Although the initial results of AI-powered tutoring are extremely promising, its widespread implementation faces technical, ethical, and practical obstacles. For example, the Tutor CoPilot project has identified several technical challenges, including the need for robust content knowledge, the ability to engage in multi-turn conversations, and the capacity to provide explanations tailored to individual students’ needs. These needs must be addressed before these tools are widely adopted, and they are likely best addressed by a collaborative effort between researchers, educators, and developers to refine and improve these systems to maximize impact.

Embedded evaluation and continuous improvement in all development and deployment phases will help ensure these tools truly deliver on their promise of improving educational outcomes for all students.

AI-enhanced tutoring represents an imminent and transformative opportunity to create a more accessible, equitable, and effective education system. By combining the proven benefits of high-dose tutoring with the scalability and adaptability of AI, educators have the potential to bridge long-standing achievement gaps and provide all students with the support they need to succeed. As AI in tutoring and education progresses, it’s essential to continue to invest in research, development, and implementation of these innovative tools to help bring about educational equity for all students.

...

Click here to read full article
News

Mentioned Publication

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

 

Generative AI, particularly Language Models (LMs), has the potential to transform real-world domains with societal impact, particularly where access to experts is limited. For example, in education, training novice educators with expert guidance is important for effectiveness but expensive, creating significant barriers to improving education quality at scale. This challenge disproportionately hurts students from under-served communities, who stand to gain the most from high-quality education and are most likely to be taught by inexperienced educators. We introduce Tutor CoPilot, a novel Human-AI approach that leverages a model of expert thinking to provide expert-like guidance to tutors as they tutor. This study presents the first randomized controlled trial of a Human-AI system in live tutoring, involving 900 tutors and 1,800 K-12 students from historically under-served communities. Following a preregistered analysis plan, we find that students working on mathematics with tutors randomly assigned to have access to Tutor CoPilot are 4 percentage points (p.p.) more likely to master topics (p<0.01). Notably, students of lower-rated tutors experienced the greatest benefit, improving mastery by 9 p.p. relative to the control group. We find that Tutor CoPilot costs only $20 per-tutor annually, based on the tutors’ usage during the study. We analyze 550,000+ messages using classifiers to identify pedagogical strategies, and find that tutors with access to Tutor CoPilot are more likely to use strategies that foster student understanding (e.g., asking guiding questions) and less likely to give away the answer to the student, aligning with high-quality teaching practices. Tutor interviews qualitatively highlight how Tutor CoPilot’s guidance helps them to respond to student needs, though tutors flag common issues in Tutor CoPilot, such as generating suggestions that are not grade-level appropriate. Altogether, our study of Tutor CoPilot demonstrates how Human-AI systems can scale expertise in real-world domains, bridge gaps in skills and create a future where high-quality education is accessible to all students.