This workshop is a hands-on exploration of Deep Learning techniques and topics for use in the classrooms of Computer Science and related fields. Deep Learning denotes the latest in a series of advances in neural network training algorithms and hardware that allow Artificial Neural Networks (ANNs) to learn quickly and effectively, even with many, stacked layers. These types of networks can be applied to almost any learning problem, such as driving a car, describing images, controlling a robot, or understanding language. This workshop will start with the mathematical and algorithmic foundations of Deep Learning, and introduce an accessible Python-based library, called "conx," which is based on the Keras library and was developed by the workshop instructors. The workshop will demonstrate ideas through animation and visualizations, examine the path to advanced topics, and explore ideas for incorporating Deep Learning topics into the classroom. The workshop is designed to allow participants to gain a foothold with these complex topics, and to help them develop their own materials for teaching. Workshop materials will be made freely available before the workshop as Jupyter notebooks.

The field of AI has changed significantly in the past couple of years and will likely continue to do so. Driven by a desire to expose our students to relevant and modern materials, we conducted two surveys, one of AI instructors and one of AI practitioners. The surveys were aimed at gathering infor-mation about the current state of the art of introducing AI as well as gathering input from practitioners in the field on techniques used in practice. In this paper, we present and briefly discuss the responses to those two surveys.