It is generally recognized that an introductory Artificial Intelligence (AI) course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core AI topics that are typically covered. This project addresses this problem and enhances the student learning experience in the introductory Artificial Intelligence course by (1) introducing machine learning elements into the AI course, (2) implementing a set of unifying machine learning laboratory projects to tie together the core AI topics, and (3) developing, applying, and testing an adaptable framework for the presentation of core AI topics which emphasizes the strong tie between AI and computer science. This is a multi-institutional effort that engages a community of 20 scholars from a broad range of universities working together on the development, implementation, and testing of curricular material, in a manner that fosters the integration of research and education. The target audience is juniors and seniors in Computer Science, Computer Engineering, and Computer Information Systems enrolled in an introductory Artificial Intelligence course. The project deliverable is a laboratory manual consisting of a suite of adaptable, self-contained, hands-on laboratory projects that can be closely integrated into a one-term AI course and which would supplement introductory AI texts. The curricular material builds on existing successful work, funded by NSF CCLI A&I. It involves further development and testing of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. Through the design and implementation of learning systems that enhance commonly-deployed applications, the innovative model for teaching artificial intelligence provides a simple and elegant means to communicate the power of the core ideas of AI in a manner that engages students in experiential education.
The goal of our work is to develop a project-based framework for teaching core artificial intelligent (AI) topics through a unifying theme of machine learning. Our goal is to enhance the student learning experience in the introductory Artificial Intelligence courses. This goal was accomplished through the development, implementation, and testing of a suite of adaptable and self-contained, hands-on laboratory projects that can be closely integrated into the AI course. A total of 26 projects have been developed; each project involves the development of a machine learning system in a specific application. The applications span a large area including network security, recommender systems, game playing, intelligent agents, computational chemistry, robotics, conversational systems, cryptography, web document classification, vision, data integration in databases, bioinformatics, pattern recognition, and data mining. The projects were implemented and tested at over 30 institutions nationwide. Our experiences and results of assessment show that the projects enhanced the student learning experiences. Students were better motivated to learn the fundamental concepts both of AI and machine learning. Our projects introduced students to important areas of research within the field of machine learning. The projects also stimulated student interest in additional AI and Machine Learning related topics. As a result, several students were motivated to pursue independent study and research in machine learning. Some of this research resulted in student co-authorship of papers and in presentations at computing conferences.
