Nick Haber is an Assistant Professor at The Stanford Graduate School of Education. Nick is interested in machine learning, computer vision, and human-computer interaction. His work thus far has primarily involved face detection and tracking, using Constrained Local Models. On top of this, he developed engagement scoring, gaze tracking, and emotion detection for the purpose of testing and producing engaging content in online courseware. Currently, his efforts are directed at applying machine learning and computer vision techniques to recognize emotions, so that the device can provide feedback to its users in a way that helps them learn these cues on their own.

In this talk, Nick looks at the great promise AI holds in our learning process. And how AI powered cognitive models of learning may someday allow us to better understand how different children learn differently, and how to better tailor learning experiences to individual needs. Nick goes deeper into…

1. I see great promise in AI-enabled learning tools that augment our learning processes. In designing these, we need to understand, in a computationally precise way, the learning processes we are trying to aid.
2. In designing AI-powered learning tools, embrace the human-in-the-loop: by capturing data from device interaction, we can both better understand the learning process and use that data to adapt the learning tool to the user’s needs.
3. By studying the early learning processes of children, we stand to make AI tools that learn more like we do. In turn, by making AI tools that learn like we do, we stand to learn more about human learning.
4. These AI-powered cognitive models of learning may someday allow us to better understand how different children learn differently, and how to better tailor learning experiences to individual needs.