High-impact tutoring has been heralded as an evidenced-backed solution for addressing the significant academic gaps that schools are facing. But scaling and sustaining these programs are challenging for schools because they require a lot of human tutors, time, and money.
The rise of artificial intelligence-based tutoring software has given schools a way to expand access, lower costs, and improve personalization of a key learning-recovery approach.
However, research on the effectiveness of AI tutoring models is limited and still emerging.
To help educators navigate the AI tutoring landscape, the National Student Support Accelerator and the AI Hub for Education at Stanford’s SCALE Initiative recently published a research brief examining the current research on the effectiveness of using AI in tutoring.
As schools deal with an uncertain funding environment, they might consider using AI tutoring to reduce costs, and “we wanted to make a more actionable tool for education system leaders on what the evidence says about AI tutoring,” said Chris Agnew, the director of the AI Hub for Education, which researches AI’s impact on student learning.
Part of the challenge with researching the effectiveness of AI tutoring is that the term could refer to a wide range of tutoring models, Agnew said.
“People hear the term ‘AI tutor,’ and they think that is a young person engaging directly with a chatbot,” he said. “What our brief outlines is that AI tutors exist on an AI-human spectrum, and that spectrum exists on a range of relational intensity. Depending on where you fall on that range, there is either strong evidence to support [it] or very little evidence.”
According to the research brief, evaluations of AI tutoring tools suggest that:
- Many students don’t engage with AI tutors.
- Adding human oversight helps engagement, but not achievement.
- The strongest results of effectiveness so far come from AI tools built for human tutors to use, not for student use.
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