Expert Tutoring and Human-AI Collaboration

One-on-one tutoring is one of the most effective ways to improve student outcomes and reduce educational disparities, particularly in mathematics—a subject strongly linked to college graduation rates and future earnings. Expert tutors leverage deep pedagogical and content knowledge to adaptively respond to individual students needs. They see mistakes as critical sites for learning. They can diagnose and remediate misconceptions with strategies such as Socratic questioning, tailored hints, prompting students to articulate their reasoning and self-explain, and providing elaborated, responsive explanations, all of which stimulate critical thinking and deeper understanding. Good math teaching can be especially challenging, as it requires instructors to engage students in reasoning about abstract concepts and solving complex problems, as well as understanding the developmental progression of problem-solving strategies to interpret students’ thinking on the fly.

Training novice tutors to achieve this level of expertise is challenging. Traditional professional development programs (e.g. workshops) are often costly, time-intensive, and disconnected from the real-time dynamics of tutoring environments, frequently limiting their effectiveness. As a result, students often work with novice tutors who struggle to remediate their mistakes in real time, and provide them with direct answers or overly generic feedback, missing out on critical learning opportunities. For example, tutors at FEV—our study’s context—would often respond to students with generic and scripted messages like “Incorrect”, “Please try again.”, “The answer is …". Our work seeks to address these challenges by providing a scalable, cost-effective solution that provides guidance to novice tutors in their live teaching contexts, helping them navigate the complexities of teaching mathematics. This approach also offers infrastructure for conducting experimental studies on effective tutoring practices.

Recent advances in AI have sparked excitement about the potential for LLMs to transform K-12 education, from intelligent tutoring systems (ITS) to automated feedback generation. LLMs have the potential to produce adaptive, human-like responses, enhancing earlier generations of ITSs. However, tuning LLMs to behave like expert teachers is challenging. Out-of-the-box LLMs are trained predominantly on internet text (e.g., Wikipedia) that differs greatly from authentic teacher-student interactions. Even strong prompting or fine-tuning techniques struggle to bridge this gap, as they tend to rely on surface-level patterns rather than deeper pedagogical reasoning that expert educators develop through experience. As a result, LLMs often fail to use pedagogical strategies that foster deeper understanding, like probing questions and others listed above, which can hinder students’ educational outcomes. Recent efforts have identified approaches to improving LLMs ability to produce expert-like responses in education, e.g., by providing editable templates and pedagogical instruction following. We build on prior work with Bridge, an adaptation method that captures expert decision-making by transforming raw think-aloud data with experienced educators into effective instructions for LLMs. Bridge has been validated to outperform fine-tuning and prompt-engineering baselines in generating expert-like pedagogical responses.

Human-computer collaborative systems combine the complementary strengths of humans and computers to enhance performance across various domains like healthcare, writing and data annotation. In education, this collaboration means using AI to support human expertise in situ rather than replacing it. Prior research shows AI assisting in discrete tasks like patient treatment selection, and guiding interactions in more structured, scripted environments such as call centers, where it provides novice workers with immediate, context-specific suggestions. However, far less work has addressed AI assistance for complex interaction tasks like teaching or tutoring, which entail dynamic, open-ended dialogues requiring AI to work in tandem with a human tutor over extended exchanges. Educators bring grounded, context-specific knowledge and empathy that AI lacks, while AI can speedily access vast information to offer real-time guidance. This complementary setup can preserve the critical human connection in teaching mitigate LLM lapses through human oversight—and is thus recommended by many in the field of AI and education. Drawing on prior work, we emphasize the human’s crucial role in selecting expert-curated strategies to guide LLMs in generating effective pedagogical suggestions. Prior findings suggest that without human selection, LLMs often default to repetitive strategies, whereas experienced educators employ richer, more diverse teaching strategies. This observation underscores the human’s critical role in guiding AI to deliver contextually appropriate and impactful instructional support.