Pedagogy in Theory of Computing and Algorithms

How do we help undergraduates master the rigorous material of Theory of Computing and Algorithms courses while keeping them engaged and confident, especially in the era of Generative AI? Additionally, what goals do educators of these courses believe are important? This panel's goal is to further the discussion of these questions. The panel consists of four educators from distinct institution types who will share evidence-based, classroom-tested strategies for these courses. After the panel gives their position statements, the moderator will guide a structured discussion on motivating abstract topics, assessment and feedback at scale, integrating contemporary tools, and aligning theory/algorithms courses with varied curricula. Specifically, the panel will discuss Generative AI and Large Language Models' place within these courses, the pedagogical implications of autograder usage in these courses, and broader learning goals educators should strive for in these courses.