If you're seeing this message, it means we're having trouble loading external resources on our website.

If you're behind a web filter, please make sure that the domains *.kastatic.org and *.kasandbox.org are unblocked.

Main content

A new chapter in education

Overview article of how AI might be used as a tool to power educational outcomes

How do students learn?

We know a lot about how students learn. We know students learn more when they:
  • are actively engaged with the material to be learned
  • get immediate feedback on their responses to new material being learned
  • work on material that is just at the edge of what they can do when provided with a little support
  • see value in what they are learning
All of these things are hard to manage consistently in classrooms with 25-30 students and one teacher. The teacher simply can't provide each student with the individual attention they need.
Since personal computers have become widely available, educational technology has tried to fill this gap. But these solutions have struggled to really act as tutors, in part because they were not easily able to consume and produce open-ended responses in natural language.
In other words, it's difficult for teachers—and it has been difficult for computers—to provide consistently rich, personalized, insightful, actionable feedback to student writing on a large scale.
That is changing.

What will large language models mean for student learning?

Large language models have arrived—and they offer the potential to change how learners interact with educational technology.
We believe that activities in which students interact with LLMs as collaborators and thought partners could hold the key to a new generation of engaging and efficacious online learning experiences!
We know learning outcomes are improved through 1) active engagement, 2) immediate feedback, 3) working at a personal learning edge, and 4) perceived value. Therefore, for example, LLMs can promote learning by:
  • Leading students through the steps of the writing process while offering rubric-based feedback on drafts
  • Encouraging students to check their understanding of procedural and application-based skills and tasks
  • Engaging with individual students on deeper learning questions like why, what if, and how
  • Helping students link what they are learning to their goals, their lives, and the things they are interested in
These ideas aren’t wishful thinking or “maybe someday” ideas, but things that we can ask these models to do now. However, we need to understand how they act in the real world, with real students, and we'll need hard proof that they can improve engagement and learning outcomes.
We should be asking these questions of any educational technology—and we can also help find the answers together.

Want to join the conversation?