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. Recently, work has been done to address the diversity of topics covered in the course and to create a theme-based approach. Our work incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses and uses a project-based application-based approach. 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 also provides a bridge between AI technology and modern software engineering. Machine learning is now considered as a technology for both software development (especially suitable for difficult-to-program applications or for customizing software) and building intelligent software (i.e., a tool for AI programming). The difficulties mentioned above associated with the introductory AI course, combined with the increasingly important role of machine learning in computer science in general and software development in particular, are the motivating factors for this NSF funded project. The specific objectives are listed below:
  • Enhance the student learning experience in the AI course by implementing a unifying theme of machine learning to tie together the diverse topics in the AI course.
  • Increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science.
  • Highlight the bridge that machine learning provides between AI technology and modern software engineering.
  • Introduce students to an increasingly important research area, thus motivating them to pursue more advanced courses in machine learning and to pursue undergraduate research projects in this area.
  These objectives are accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. This work builds on the success of our earlier project funded by NSF CCLI A&I DUE #0409497. It involves further development and testing of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. Under the CCLI A&I project, we developed and pilot-tested our proof-of-concept which included six hands-on laboratory projects that were implemented and pilot-tested at three institutions. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. This 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. We believe this will stimulate student interest, have a dramatic impact on their motivation, and enhance their learning experiences.
  A total of 26 projects, that span a variety of applications will be produced and tested. Each project will involve 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.
  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.
  A broader impact of this project will be achieved through the collaborative development, dissemination, and separate testing of these hands-on laboratory projects at the institutions of 20 participating faculty members, including the two PIs, from 18 diverse institutions nationally. The institutions are selected to allow for further development and testing on diverse users in different settings. The effectiveness of this project is being evaluated with the assistance of internal and external evaluators through a multi-tier evaluation system involving faculty, students, and an external advisory board.