SUMMARYProjects in the Artificial Intelligence course have evolved over the years. Along the way, they have taken several forms, including small-scale LISP/Prolog projects, larger-scale object-oriented projects in CLOS/C++, projects organized around games, and more recently, projects organized around the concept of agents. All along, educators have attempted to make the projects more appealing and instructive at the same time.In this panel, we will examine three disparate approaches for making AI projects more instructive and engaging: •The first approach organizes all the projects around a central theme, in this case, machine learning; •The second approach uses inexpensive robots as the platform for traditional projects; •The third approach moves to a software platform that enables working with advanced or simulated robots as well. All three approaches have been evaluated, and make supplementary materials available for use by interested faculty. MACHINE LEARNING -INGRID RUSSELLWe will present a suite of adaptable hands-on projects that can be closely integrated into a one term AI course. Our work unifies the Artificial Intelligence (AI) course around the theme of machine learning and creates an adaptable framework for presenting core AI concepts around that theme. Machine learning is inherently connected with the AI core topics and provides methodology and technology to enhance real-world applications within many of these topics. Machine learning is now considered as a technology for both software development (especially suitable for difficult-toprogram applications or for customizing software) and building intelligent software (i.e., a tool for AI programming). Our projects emphasize the relationship between AI and computer science in general, and software development in particular and highlight the bridge that machine learning provides between AI technology and modern software engineering. Each project involves the design and development of a learning system which will enhance a particular commonly-deployed application. In an introductory course one wishes to impart a wide variety of topics efficiently, indexing the major areas of the field. A machine learning application can be rapidly prototyped, allowing learning to be grounded in engaging experience without limiting the important breadth of an introductory course. The projects span several applications including Web user profiling, character recognition, the N-Puzzle problem, the jeopardy dice game Pig, Web document classification, and the popular board game Clue.We will present our projects as well as our experiences using them. Evaluation results indicate that the projects enhanced the student learning experience in the introductory AI course and that students demonstrated a better understanding of fundamental AI concepts such as Knowledge Representation and Search. Students were better motivated to learn the fundamental concepts both of AI and machine learning. The projects also stimulate students' interest in additional AI and machine learning related ...