AI’s Impact on Math Education

Peering into the crystal ball to see how artificial intelligence (AI) will change education can be a tricky exercise. While some previous unknowns are taking shape, such as how much teachers will embrace the time-saving power of the technology, other aspects remain foggy. Among them: How will the technology change the way schools teach K-12 fundamentals like math? And is a predictive technology even a good fit for math instruction?

Bob Hughes, the director of education for the Gates Foundation, which is investing more than $1 billion in math education, offers clarity on these questions. An integral part of Hughes’s job is keeping his fingers on the pulse of innovations in the K-12 sector that might improve educational outcomes for kids, and AI is driving many of those innovations right now. (Editorial Projects in Education, the publisher of Education Week, receives support from the foundation for its coverage of math instruction, but retains sole editorial control over its articles.)

According to Hughes, from AI-powered teaching assistants and tutors to programs analyzing teachers’ lectures and providing feedback on their delivery, many products poised to change how schools teach math and how students learn it are emerging.

Here’s a look at the developments he is closely watching, and how the K-12 education sector is adapting.

A Criticism of AI Tools

AI tools are often criticized for not being accurate. Hughes notes that math is incredibly challenging for AI. In the early days, early chatbots were like comparing their output to a seven-year-old’s grasp of fundamentals in math.

"We’ve made real progress in math in both large and small language models, so that chatbots are increasingly accurate," he adds. "They’re doing well on national benchmarks of accuracy. We can now say with greater confidence that the math done by AI chatbots is accurate."

Next Steps

Hughes emphasizes that once the accuracy concerns are addressed, teachers must think through applications of AI to create engaging learning experiences. This is critical, as many students grasp basic math but struggle with subjects like algebra, leading to emotional challenges.

"How does AI enable a young person to have more opportunities to do math that is relevant and meaningful to them?" Hughes asks. "We are really at the beginning stages of that work."

Motivating Students Through AI

AI can also motivate students by personalizing their learning experience. An example Hughes provides is asking baseball fans to think of fractions and decimals in a baseball context, thereby making learning more engaging.

Additionally, AI could innovate assessment methods. Hughes questions, "What would assessment look like if we started to introduce open-ended questions?" He believes flexibility in assessments could track each student’s individual contributions in group work.

What Students Should Learn

There is no straightforward answer regarding what students need to learn given AI’s capabilities. Hughes argues that it is vital for students to understand math deeply. He cautions against depending solely on AI for basic skills.

Hughes concludes, "We need kids who can flex as we learn more and as the technology evolves."