Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.23633 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911343918972928 |
|---|---|
| author | LearnLM Team Eedi : Wang, Albert Rysbek, Aliya Huber, Andrea Nambiar, Anjali Kenolty, Anna Caulfield, Ben Lilley-Draper, Beth Groot, Bibi Veprek, Brian Burdett, Chelsea Willis, Claire Barton, Craig Smith, Digory Mu, George Walters, Harriet Jurenka, Irina Hulls, Iris Stalley-Moores, James Caton, Jonathan Wilkowski, Julia Alarakyia, Kaiz McKee, Kevin R. McCafferty, Liam Dalton, Lucy Kunesch, Markus Malubay, Pauline Kidson, Rachel Wells, Rich Wheeler, Sam Wiltberger, Sara Mohamed, Shakir Woodhead, Simon Brazão, Vasco |
| author_facet | LearnLM Team Eedi : Wang, Albert Rysbek, Aliya Huber, Andrea Nambiar, Anjali Kenolty, Anna Caulfield, Ben Lilley-Draper, Beth Groot, Bibi Veprek, Brian Burdett, Chelsea Willis, Claire Barton, Craig Smith, Digory Mu, George Walters, Harriet Jurenka, Irina Hulls, Iris Stalley-Moores, James Caton, Jonathan Wilkowski, Julia Alarakyia, Kaiz McKee, Kevin R. McCafferty, Liam Dalton, Lucy Kunesch, Markus Malubay, Pauline Kidson, Rachel Wells, Rich Wheeler, Sam Wiltberger, Sara Mohamed, Shakir Woodhead, Simon Brazão, Vasco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_23633 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI tutoring can safely and effectively support students: An exploratory RCT in UK classrooms LearnLM Team Eedi : Wang, Albert Rysbek, Aliya Huber, Andrea Nambiar, Anjali Kenolty, Anna Caulfield, Ben Lilley-Draper, Beth Groot, Bibi Veprek, Brian Burdett, Chelsea Willis, Claire Barton, Craig Smith, Digory Mu, George Walters, Harriet Jurenka, Irina Hulls, Iris Stalley-Moores, James Caton, Jonathan Wilkowski, Julia Alarakyia, Kaiz McKee, Kevin R. McCafferty, Liam Dalton, Lucy Kunesch, Markus Malubay, Pauline Kidson, Rachel Wells, Rich Wheeler, Sam Wiltberger, Sara Mohamed, Shakir Woodhead, Simon Brazão, Vasco Computers and Society Artificial Intelligence Machine Learning 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. |
| title | AI tutoring can safely and effectively support students: An exploratory RCT in UK classrooms |
| topic | Computers and Society Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2512.23633 |