Building a 1:1 teacher was one of the original dreams of AI.
It’s finally possible.
To maximize human potential, we must scale 1:1 teaching.
We now have the tools to do this.
We’re building the components of an AI teacher in three parts.
The key to unlocking AI teaching for young kids is Child Speech Perception
Children learn by talking, so the best interface for young kids to interact with technology is natural language. But existing speech recognition does not work for children.
We’re leveraging advances in self-supervised machine learning to build 10x better child speech perception.
A great teacher knows the child in front of them and continuously maps their cognitive state.
An AI teacher builds a granular picture of a child’s understanding and situates them along a learning pathway. Think of it as continuous phenotyping for every stage of early learning.
The best teachers find a way to engage learners at just the right level for them to stretch and grow.
Large generative models unlock the ability to create personalized stories and teaching materials that evolve and interact to meet children at that just-right apex for learning. We're building Language Models that are safe and produce outputs aligned with pedagogical best practices and produce content driven by the interests of each unique child.
Building a Learning Companion
We’re building delightful UI that sparks a love for learning and centers the child, not the technology
Our team brings together world-class AI and learning science expertise
A radically different way for young children to interact with technology.
Learning is done by a child, not to a child. A teacher puts the child at the center of the learning.
Organization of learners
Organization of learners
Learner goals / Progress indicators
Reading is the gateway to learning
Yet, 69% of 4th graders are behind in the USA and literacy is a huge problem globally.
Launching our First Product
Read with Ello
Who We Are
The tools for building a real AI teacher have arrived
Our team has played a central role in researching & developing the building blocks of AI teaching.
Advances in neural information processing systems. 2020;33:12449-60.
U.S. Patent Application No. 17/644,767
Kalantarian, J. Schwartz, R. Patnaik, B. Chrisman, N. Stockham, K. Paskov, N. Haber, D. P. Wall
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 2020 Aug;5(8);759-69
JMIR Pediatrics and Parenting. 2022 Apr 8;5(2):e26760.
Proceedings of the 37th International Conference on Machine Learning, 2020;119:5306-15.