ML Learning

Thirty years ago, Media Lab founding faculty member Seymour Papert laid the foundation for a new theory of learning through construction. He created tools for children to be designers and creators, rather than just consumers of technology, positing that learning happens best when people are actively constructing knowledge through creative experimentation and the design of sharable objects. Today, the ML Learning Initiative is built on similar principles and aims to bring the collective creativity of the Media Lab to bear on the future of learning.

The ML Learning initiative is built around a cohort of learning innovators from across the diverse Media Lab groups. We explores learning across many dimensions, ranging from neurons to nations, from early childhood to lifelong scholarship, and from human creativity to machine intelligence.  In addition to creating tools and models, the initiative provides non-profit and for-profit mechanisms to help promising innovations to scale.

Thirty years ago, Media Lab founding faculty member Seymour Papert laid the foundation for a new theory of learning through construction. He created tools for children to be designers and creators, rather than just consumers of technology, positing that learning happens best when people are actively constructing knowledge through creative experimentation and the design of sharable objects. Today, the ML Learning Initiative is built on similar principles and aims to bring the collective creativity of the Media Lab to bear on the future of learning.

The ML Learning initiative is built around a cohort of learning innovators from across the diverse Media Lab groups. We explores learning across many dimensions, ranging from neurons to nations, from early childhood to lifelong scholarship, and from human creativity to machine intelligence.  In addition to creating tools and models, the initiative provides non-profit and for-profit mechanisms to help promising innovations to scale.

Group at a glance