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Artificial Intelligence, and particularly Generative AI (GenAI), is challenging established approaches to teaching and learning in Political Science. Beyond the adoption of new educational tools, AI raises fundamental questions about what students should learn, how disciplinary knowledge and skills should be developed, and how learning should be assessed when knowledge production and analysis can increasingly be supported or delegated to AI systems.
These developments invite political science educators to reconsider learning objectives, curricula, teaching practices, and assessment, and the forms of disciplinary knowledge and reasoning students need to develop in an AI-mediated environment. How should students learn to formulate political science questions, work with concepts and theories, evaluate political evidence, compare cases, and connect theoretical arguments with empirical analysis when AI can increasingly perform or support many of these tasks? At the same time, political science occupies a distinctive position in relation to AI: students need to learn both how to use AI critically in the study of politics and how to apply the concepts and methods of the discipline to understand AI as a political phenomenon.
This panel welcomes theoretical and empirical contributions examining how AI is reshaping the teaching and learning of political science. Particular attention is given to curriculum and assessment redesign; concept formation and theoretical reasoning; comparative and empirical analysis; political evidence and sources; AI-assisted research and disciplinary methods; classroom practices and the governance of AI use; student agency and academic integrity. Rather than assuming either the adoption or rejection of AI, the panel invites reflection on what it means to educate political science students in an AI-mediated environment.