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- AI Research Methods in Social and Behavioral Sciences: POS 4931 Fall 2026 (Teacher: Dr. Badredine Arfi)
Introduction to the use of artificial intelligence (AI) in social and behavioral science research. Explores core concepts, methods, and tools for applying AI to the collection, analysis, and interpretation of social and behavioral data. Emphasizes the integration of supervised and unsupervised learning, generative models, and causal inference within empirical research design. Focus on the ethical, methodological, and societal implications of AI in research, including algorithmic bias. This course introduces students in the social and behavioral sciences to the uses and implications of Artificial Intelligence (AI) in contemporary research methods. Through hands-on exercises, theoretical readings, and independent research projects, students will learn to critically examine and apply AI techniques to data collection, analysis, and interpretation across various domains.
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- AI in Political Science: POS 4931 Spring 2024 (Teacher: Dr. Badredine Arfi5
This is a broad introduction to contemporary issues, actors, theories, debates, and major scholarly traditions in the study of AI in politics. The course introduces the foundational assumptions, methods, and scope of AI in politics as defined by a variety of perspectives and approaches. In doing so the course guides the students to core concepts necessary for understanding how AI is playing an increasingly important role in the world. The course specifically analyzes the impact of AI on knowledge production and dissemination, evaluates the ethical implications of AI in political decision-making, investigates the role of AI in shaping political discourse and public opinion, explores the epistemological challenges posed by AI-generated knowledge, and develops critical thinking and research skills in the context of AI and politics.
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- Political Network Analysis: POS 4931 Spring 2024 (Teacher: Dr. Andrew S. Rosenberg)
Networks are ubiquitous in politics. Countries are linked in trade and alliance networks. Legislators are tied in co-sponsorship networks. Rebel groups are connected in information networks. In this class, we will not only study the theory underlying these networks, but we will also analyze them. In so doing, we will learn to apply the multi-disciplinary field of network analysis to important political science applications.
- Conflict Forecasting: The Art and Science of Prediction in Politics: POS 6933 Spring 2024 (Teacher: Dr. David S. Siroky)