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Teaching Integrated AI through Interdisciplinary Project-Driven Courses

Eric Eaton

Abstract


Different subfields of AI (such as vision, learning, reasoning, planning, and others) are often studied in isolation, both in individual courses and in the research literature. This promulgates the idea that these different AI capabilities can easily be integrated later, whereas, in practice, developing integrated AI systems remains an open challenge for both research and industry. Interdisciplinary project-driven courses can fill this gap in AI education, providing challenging problems that require the integration of multiple AI methods. This article explores teaching integrated AI through two project-driven courses: a capstone-style graduate course in advanced robotics, and an undergraduate course on computational sustainability and assistive computing. In addition to studying the integration of AI techniques, these courses provide students with practical applications experience and exposure to social issues of AI and computing. My hope is that other instructors find these courses as useful examples for constructing their own project-driven courses to teach integrated AI.

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DOI: https://doi.org/10.1609/aimag.v38i2.2730

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