Even With Human Help, Kids Need Motivation to Use AI Tutors. The Question Is What

Get stories like this delivered straight to your inbox. Sign up for The 74 Newsletter

Artificial intelligence-based tutoring programs promise something schools have long struggled to provide: personalized instruction for every student at a fraction of the cost of traditional 1-on-1 tutoring. 

But for that promise to be fulfilled, students have to use the AI tools. New research suggests that may be much harder to achieve than schools assume. 

In a paper describing the results of randomized controlled trials led by the Stanford SCALE Initiative, 355 students in grades 1 to 5 in two school districts were assigned either to use an AI literacy tutor independently or to use the same platform with support from a human tutor. In both, the goal was to improve reading achievement.

The human tutors encouraged students to participate, helped troubleshoot technology problems and established group norms, while building rapport and accountability. Their sessions followed a consistent structure: a brief check-in, time for students to work on the platform and final reflection. In the group with no human support, students were expected to work on the AI platform on their own. Each group was scheduled to use the tool twice a week for 30 minutes per session.  

The clearest finding from the study was that all students, regardless of whether they received human support, used the AI tutor far less than intended. For example, almost half the children in the independent-use group did not use the AI tutor at all. Those who did use the platform spent just two to five minutes per week, on average.

Students using the AI tutor with human support spent only slightly more time using the tool, but the effects were small: an increase of just one minute a week in one of the districts and 4.4 more in the other. 

There are a range of potential reasons for this low engagement, from students not finding the tool compelling to preferring to ask a teacher for help or needing more encouragement and accountability.

For both groups of students,  usage remained far below the 30-minute-per-week dosage recommended to reap academic benefits. At these levels, students likely did not receive enough of the intervention for gains in reading achievement to emerge.

These findings underscore two important points: Access to AI tutoring does not ensure meaningful use, and, while human support can increase student engagement, it may not be enough to generate measurable learning gains. The study highlights the real-world implementation challenge inherent in student-directed AI tools: Kids need to be motivated to engage and keep coming back, or these products will never deliver on their promise. Human support can help, but in this study it wasn’t enough to bring usage close to recommended levels.  

But there are other promising approaches that flip the model: rather than asking AI to replace human tutors, AI can help them become more effective. In one large randomized trial, an AI tool that provided real-time instructional suggestions to human tutors improved student mastery, with the largest gains occurring among less experienced tutors. This suggests a different vision for the future of personalized learning, one where AI and human support work together to meet the needs of tutors and individual students. The right mix will vary depending on the subject and grade level. Assuming stronger engagement, some students may thrive with primarily AI-supported learning. Others may benefit from AI tutoring paired with periodic human encouragement and accountability. Still others will require sustained relationships with human tutors, teachers or mentors. 

That is where the field should focus next.

But before schools and districts implement AI tutoring tools with the expectation that they will transform learning, they need more, better evidence about what it actually takes to engage students. To date, the research remains surprisingly limited. A review by the SCALE Initiative identified more than 800 studies examining AI in education, yet only 20 used rigorous designs that allowed researchers to examine the causal effect of the AI tool on teaching and learning. In other words, enthusiasm for AI in education has far outpaced rigorous evidence about whether students will use these tools, and whether they improve student learning. 

Widening the evidence base is essential. Funders, researchers and education leaders should prioritize uncovering which kinds of human supports are needed for which students, under which conditions and whether they generate enough meaningful engagement to improve learning. Also important is research to inform decisions about which models are both effective and scalable. Organizations like OnYourMark and the University of Chicago Education Lab, both Overdeck Family Foundation grantees, are beginning to explore more cost-effective, hybrid approaches that use AI to personalize instruction while strategically deploying human support where it matters most. Whether these hybrid approaches can improve learning at scale remains an open question, and an important one to test. 

Education has seen this story before. New technologies arrive with the promise of reaching more students at lower cost, only to produce disappointing results because of implementation challenges. And while AI has enormous potential to expand access to personalized learning, there is no reason to assume it will succeed simply because the technology is more advanced.

The next generation of research should help education leaders and practitioners understand not just how to build better digital tools, but how to build learning environments where students actually use them and benefit.

Disclosure: The Overdeck Family Foundation provides financial support to The 74.

Did you use this article in your work?

We’d love to hear how The 74’s reporting is helping educators, researchers, and policymakers. Tell us how