AI Tutoring Can Safely and Effectively Support Students: An Exploratory RCT in UK Classrooms

LearnLM Team, Google & Eedi

One-to-one tutoring is widely considered the gold standard for personalized education, yet it remains
prohibitively expensive to scale. To evaluate whether generative AI might help expand access to this
resource, we conducted an exploratory randomized controlled trial (RCT) with 𝑵 = 165 students across
five UK secondary schools. We integrated LearnLM—a generative AI model fine-tuned for pedagogy—into
chat-based tutoring sessions on the Eedi mathematics platform. In the RCT, expert tutors directly
supervised LearnLM, with the remit to revise each message it drafted until they would be satisfied
sending it themselves. LearnLM proved to be a reliable source of pedagogical instruction, with supervising
tutors approving 76.4% of its drafted messages making zero or minimal edits (i.e., changing only one or
two characters). This translated into effective tutoring support: students guided by LearnLM performed
at least as well as students chatting with human tutors on each learning outcome we measured. In
fact, students who received support from LearnLM were 5.5 percentage points more likely to solve
novel problems on subsequent topics (with a success rate of 66.2%) than those who received tutoring
from human tutors alone (rate of 60.7%). In interviews, tutors highlighted LearnLM’s strength at
drafting Socratic questions that encouraged deeper reflection from students, with multiple tutors even
reporting that they learned new pedagogical practices from the model. Overall, our results suggest
that pedagogically fine-tuned AI tutoring systems may play a promising role in delivering effective,
individualized learning support at scale.