As generative AI has swept into classrooms across the country, the promoters and the skeptics have offered starkly different visions of the technology’s effects on student learning.
In the best-case scenario, AI could finally personalize instruction for every student, working with kids on the specific skills they need and giving targeted feedback. In the worst-case scenario, AI outsources the learning process and flattens student creativity and voice.
But a new review of the emerging research on AI in education finds that the reality isn’t so simple, or so binary.
Researchers at the AI Hub for Education, part of Stanford University’s Accelerator for Learning, analyzed 52 randomized controlled trials and quasi-experimental studies that tested the effect of student and teacher use of generative AI on students’ academic achievement in K-12 and higher education, perception of their learning experience, and social-emotional outcomes. The paper is an update of a similar review conducted in March of this year.
Some use cases stood out as more promising than others, but almost all offered caveats.
When students used generative AI for learning support, like generating explanations of concepts or practice problems, it generally led to better learning gains than when students used the tools to complete their work. Across the studies, though, neither use led to consistent positive outcomes.
Teachers’ generative AI use can increase the quantity and quality of student feedback, even leading to improved student outcomes. But it can also have unintended consequences. At least one study found teacher AI use reduced student motivation and confidence.
