Key points:
Closing the achievement gap and helping all students reach their full potential is not only a matter of equity, but also a key driver of economic growth and prosperity. By equipping students with the skills, knowledge, and competencies they need to succeed, we can create a highly skilled and adaptable workforce that is capable of meeting the demands of a rapidly changing global economy. This, in turn, can fuel innovation, entrepreneurship, and job creation–and help ensure that the United States remains competitive in the 21st century.
Recent research has shown that generative AI tools have the potential to help bridge these gaps. These tools can be used to support students in a variety of ways, from personalized learning to high-dose tutoring. While more research is needed to evaluate the scalability and efficacy of these tools, they offer a promising avenue for addressing the challenges faced by students in STEM education, especially in the wake of the COVID-19 pandemic.
High-dose tutoring as part of a promising solution
High-dose tutoring (HDT) is one encouraging solution that has helped accelerate learning and reduce achievement gaps. HDT is tutoring provided to one or two students per tutor, in person, three or more times per week. It’s been shown to produce significant learning gains across a wide range of students; however, the intensive and individualized nature of HDT makes it expensive and challenging to implement on a large scale, limiting its accessibility to those who need it most.
Virtual HDT has made great strides in addressing some of these challenges. Research on Saga Education’s Virtual Math Tutoring Program has demonstrated that students who received more virtual tutoring showed the most improvement in math achievement. However, program participation overall was unexpectedly low, even though students were provided with free laptops and Wi-Fi–further highlighting the need for innovative solutions to increase engagement and scalability.
AI tutoring: A paradigm shift?
AI-enhanced tutoring systems are iterating on the insights and results presented by traditional HDT and show promise in delivering significant academic gains at scale. Tools like Tutor CoPilot, Wolfram, LiveHint AI, and Khanmigo provide personalized, adaptive learning experiences. They have the potential to capitalize on the impact of HDT and afford HDT opportunities for underserved students. As the use of generative AI in educational settings accelerates, it is essential to prioritize tools that enhance, rather than replace, students’ interactions with teachers, and to ensure that AI remains a tool for equity and inclusion.
This idea of pedagogical excellence supported by AI-powered tools has several notable and exciting foreseeable benefits for students and educators:
- Personalization and adaptability: AI-powered tutoring systems can provide tailored learning experiences that adapt to each student’s needs in real time. This personalization is particularly beneficial for students who may struggle in traditional classroom settings and provides teachers with actionable data to cater to the needs and strengths of each student.
- Scalability, accessibility, and efficiency: AI expands high-quality tutoring services to reach more students, regardless of location or economic background. By automating routine tasks and providing data-driven insights, AI empowers teachers to focus on higher-level instructional strategies rather than facilitating the tutoring tactically. This increased efficiency could allow for more widespread implementation of HDT programs.
- Enhancing educational equity: AI-enhanced tutoring can make educational outcomes more equitable, not just more accessible. By democratizing access to high-quality educational support, AI-driven tutoring could particularly benefit underserved students who would not otherwise have access to need-based supplemental education.
Navigating challenges posed by AI-enhanced tutoring
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.
