Nine papers on AI in education
A selection from April–September 2026: classroom trials, virtual students, knowledge prediction and learning from teaching logs.
Articles
The N+1 approach to education
Pavel Katunin on incremental teaching, reinforcement learning and the three models behind Glite’s approach.
How do we build a concept extractor?
Finding learning concepts in text and student answers: how the models work, how accurate they are, and what they cost at scale.
From AI Tools to AI Teammates: Educational Applications in the Gen AI Era
Dr. Zheng Yuan on language learning, assessment and the shift from individual AI tools to systems that work with people.
Teacher-Led Tooling for the Adoption and Improvement of AI Models
Simon Woodhead on how teacher expertise, diagnostic questions and careful evaluation can improve educational AI.
The Future of Learning
Peter Romov on learner models, personalised teaching and combining different approaches to AI in education.
ERCT: A Simple Standard for Making Sense of Educational RCTs
Twelve questions for reading educational randomised controlled trials more critically.
The 2 Sigma Problem and the AI Tutor Dream
What the two-sigma tutoring claim tells us—and what the original evidence leaves unresolved.
Faster and Safer Testing for AI Tutors
Can student models help us evaluate teaching strategies before trying them with learners?
Tutoring as a reinforcement learning task
Teaching as a sequence of decisions under uncertainty: a reinforcement learning perspective.
Education as Medicine
An argument for testing educational practice with the same seriousness we bring to medical evidence.