Presenting AI to Non Ph.D-Bound Students

Frank Klassner

Graduate students are often introduced to AI as a collection of research problems from which they will select a dissertation topic and, ultimately, a research career. After they earn their PhD’s and become faculty at computer science departments, this view of AI serves them well in attracting their own research assistants. AI curricula for PhD students, therefore, tend to be based on this view of AI as a field of future research promise. However, I argue that this view is not an appropriate basis for an AI curriculum for non Ph.D-bound students. Considering that this category represents the large majority of computer science students, it is important that undergraduate AI curricula take these students’ needs into account. Having taught three undergraduate computer science courses as a research-oriented AI Ph.D student, I want to address two issues that AI faculty must face in estab- lishing curricula for non Ph.D-bound computer science students. Using a curriculum sketch this paper discusses (1) how to present AI as a field with practical value and concrete progress as well as research promise and (2) how to provide and encourage links between AI and computer science curricula.

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