Teaching
I'm a methods teacher at heart. I teach students to think carefully about how knowledge is produced and to pick approaches that fit their questions rather than defaulting to whatever their department rewards. Since 2026, I've been teaching AI-integrated versions of my qualitative methods and introduction to political science research methods courses, where students learn to critically cowork with AI rather than either uncritically adopting or reflexively rejecting it.
I'm also a believer in co-teaching. Working with a colleague in the same classroom changes the dynamic for everyone: students see disagreement modeled productively, and instructors push each other to be better. I wrote a piece about this with my great friend, colleague, and co-author Andrey Semenov in Times Higher Education.
I've also written an op-ed with another great friend Nikita Durnev on integrating harassment prevention into first-year curricula, something I care about both as a teacher and as co-chair of NU's Anti-Harassment Committee.
Methods courses
Qualitative Research Methods
A graduate course covering the full arc of qualitative research: design, data collection, and analysis. We compare positivist and interpretivist qualitative approaches and work through case studies, process tracing, interviews, participant observation, and participatory action research. In the AI-integrated version of this course, students learn to critically cowork with AI as a co-analyst while maintaining methodological rigor and reflexivity about what changes when a non-human agent participates in qualitative analysis.
Interpretivism as Philosophy and Method
A graduate seminar on non-positivist research traditions. The first half covers philosophy of social science, interpretive epistemology, and the question of whether AI can participate in interpretive research. The second half is a hands-on campus ethnography. Students choose between a standard ethnography track and an AI-enhanced track, where they deliberately integrate AI into research design and data analysis as a collaborator. AI-track teams write AI positionality statements, maintain full AI conversation logs as appendices, and critically reflect on what changes when a non-human agent enters the interpretive process.
Introduction to Political Science Research Methods
Students build their own research proposals from scratch: research questions, literature review, theory, methodology. In the AI-integrated version, students learn a critical coworking practice with AI throughout the research process, from literature search to research design. The goal is to make methods accessible and personally meaningful, not abstract and intimidating.
AI in teaching
AI from a Social Science Perspective (Fall 2026)
A new course co-designed with Claude (Anthropic). The course is structured in three blocks. Block 1 (What Are We Dealing With?) tackles philosophy of mind, consciousness, and the ontological status of AI. Block 2 (Working Together) is hands-on: students learn prompting as methodology, cowork with AI on qualitative and quantitative research tasks, and confront the ethics of AI-assisted research. Block 3 (AI in the World) examines the material costs of intelligence, geopolitics of AI development, and governance frameworks, with particular attention to Central Asian gaps. Students maintain an AI Interaction Portfolio across the semester, write a position paper on what we owe AI, conduct a collaborative research exercise with Claude as a team member, and produce a policy brief on AI governance.