Who we are

The Teachable AI Lab (or TAIL for short) is an interdisciplinary research group at Drexel University’s College of Computing and Informatics. Our mission is to better understand how people teach and learn and to build machines that can teach and learn like people do. We engage in both use-inspired and fundamental research to achieve this mission.

What we do

Our research focuses primarily on three thrust areas: (1) Teachable Systems, (2) Human-Like AI/ML Models, and (3) Computational Models of Human Learning and Decision Making. As highlighted in the following figure, these thrust areas are synergistic and support one another.

Three Teachable AI Lab research thrusts and how they relate to one

Within each thrust, we aim to address the following questions:

  • Teachable Systems: How do we build systems people can teach and interact with, like they would another human, while still taking advantage of key non-human features of AI/ML systems?

  • Human-like AI/ML: How do we develop cognitive systems that can learn like humans (incrementally, with few examples, etc.) and that produce human relatable/explainable/understandable outputs? The emphasis will be on both developing distinct AI and ML components as well as on putting these components together to create integrated systems.

  • Computational Models of Human Learning and Decision Making: How can we leverage human data to guide human-like computational model design? How can we leverage these human-like models to better understand human decision making and learning?

For more information about our lab, see our lab vision blog post and our pages on research and publications.

Our values

Our lab is committed to developing a positive lab culture where individuals are supported and invested in the lab mission. To support this commitment, we have created a lab manual that describes our lab, the cultural values we aspire to, and expectations for lab members.

Interested in getting involved?

Please send an email briefly detailing your background and interests to Christopher MacLellan.