In a prior paper, we argued that Artificial Intelligence (AI) should be placed on a different foundation, one based on pattern recognition and feature learning rather than symbol manipulation and feature engineering. In this paper, we provide a proof of concept of an AI course that follows that proposed approach. Students study how these systems become so incredibly powerful through machine learning of features and through pattern matching. Students learn how those systems represent knowledge and they study their currently limited reasoning abilities. Students spend time discussing the accomplishments of current systems, positive as well as negative and they study the projected impact of anticipated systems. In this paper, we give a brief argument of why one would want to offer such a course. We present a detailed outline of the contents of such a course, together with learning materials and their proposed use. We summarize relevant anonymous student feedback and offer a subjective evaluation of the pilot course.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of ten AI assignments from the 2019 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http: //modelai.gettysburg.edu.

We developed a course in which students train an instance of Watson and develop an application that interacts with the trained instance. Additionally, students learn technical in-formation about the Jeopardy! version of Watson and they discuss a future infused with cognitive assistants. In this poster, we justify this course, characterize major assessment items and provide advice on choosing a domain.

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.

In this paper, we describe how we integrated the materials from the 2013 IBM The Great Minds Challenge (TGMC) - Watson Technical Edition into our Introductory Artificial Intelligence course. We describe the variety of materials made available by IBM, as well as the nature of the competition and the datasets that are at the heart of it. We detail how, where and in what form we integrated the materials into our course. We describe assignments that are based on the materials from the competition as well as additional materials we incorporated into our course. We finish by evaluating our experience in teaching with the materials as well as summarize relevant student feedback. We make recommendations for those who wish to adopt the materials.

We describe a knowledge-server we developed for our data structures course. This server holds information traditionally contained in textbooks. Additionally, it houses information that is not easily contained in textbooks, such as animations and presentation slides of important concepts. It furthermore references materials from diverse sources available on the World Wide Web. In addition to course materials, the server makes available a variety of ways in which to navigate the information. In addition to searching concepts and a directory listing of concepts, it makes available a navigable graph, representing the ontology of the course materials. This enables students to more quickly find information and to better see relationships between materials introduced in the course. We see knowledge servers as taking the role of textbooks in the not so distant future.

We present a breadth-first, lecture-and lab-based approach to introducing Computer Science that uses functional programming.Functional programming provides a lowoverhead introduction to programming (no types, few constructs, and little syntax), enabling students to write, in their first semester, programs sophisticated enough to exemplify important concepts of Computer Science.It also encourages good programming style (modular design and testing, e.g.) and serves as an introduction to an important problem-solving paradigm.The course gives the students a broad overview of Computer Science and helps them gauge their interest in the field.