KSOD 2635 - AI and Sociology

Course Description

This course examines artificial intelligence (AI) from three sociological perspectives:

1.

AI as an Object of Study – Understanding how AI technologies shape and are shaped by social structures, including institutions, inequalities, and ideologies.

2.

AI as a Data Source and Method – Exploring AI-assisted data collection, simulation, content analysis, and coding in sociological research.

3.

AI as a Contested Tool in Research and Writing – Critically engaging with AI for literature searches, outlining, and hypothesis generation, while assessing ethical and epistemological implications.

Through lectures, hands-on exercises, and discussions, students will critically analyze AI’s benefits and limitations. We will examine how AI enhances efficiency in data collection, analysis, translation, and organization, while also addressing challenges like bias, hallucinations, misinformation, ethical concerns, and the potential erosion of critical thinking skills.

Learning Objectives

By the end of this course, students will:

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Analyze AI’s impact on and effects of social structures from a sociological perspective.

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Understand and apply AI tools for data collection and analysis, including AI-assisted coding and content analysis.

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Evaluate the ethical and methodological implications of using AI in research.

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Develop critical engagement skills with AI-driven literature search, writing assistance, and idea organization.

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Identify the boundaries and ethical considerations in AI-enhanced academic work.

Course Assessment

To pass this course, students will need to attend and regularly participate in class discussions and activities.

Course Structure

Session 1: AI as an Object of Sociological Study (4 hours)

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Introduction to AI and Society

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Algorithmic Bias & Social Inequality

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AI and the Information Environment

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AI and the Future of Work

Workshop Activity: Case study analysis on algorithmic bias in hiring, policing, or healthcare.

Reading: Joyce, K., & Cruz, T. M. (2024). A Sociology of Artificial Intelligence: Inequalities, Power, and Data Justice. Socius, 10.

Session 2: AI as a Tool for Data Collection and Analysis (4 hours)

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AI in Sociological Research: AI-assisted qualitative and quantitative methods

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Automated Content Analysis: Understanding sentiment analysis, topic modeling, and coding with AI

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Opportunities & Risks: Efficiency vs. data privacy, bias, and research integrity

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Simulations and Synthetic Data Collection and Analysis with AI

Workshop Activity: Hands-on AI-assisted Data Coding (using a sample dataset) and a second activity with a Simulation Exercise

Reading: Than, L., Law, T., Nelson, L.K., McCall, L. 2025 “Updating “The Future of Coding”: Qualitative Coding with Generative Large Language Models,” PrePrint SocArXiv Papers

Joon Sung Park, Carolyn Q. Zou, Aaron Shaw, Benjamin Mako Hill, Carrie Cai, Meredith Ringel Morris, Robb Willer, Percy Liang, Michael S. Bernstein. 2024 “Generative Agent Simulations of 1,000 People,” PrePrint SocArXiv Papers

Session 4: AI and the Writing Process – Ethics & Critical Engagement (2 hours)

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AI for Literature Search & Organization: Strengths and pitfalls of AI-generated summaries.

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AI in Academic Writing: Structuring arguments, outlining, and critical thinking gaps.

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Plagiarism, Hallucinations, and False Data: Ethical concerns and limits of AI-generated content.

Workshop Activity: Students compare AI-generated and human-written literature reviews, identifying strengths and weaknesses.

Reading: Lieberman, Lisa. 2024. “AI and the Death of Student Writing,” Chronicle of Higher Education. June 7.

Session 4: The Future of AI and Sociology – Student-Led Discussions (2 hours)

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The Role of AI in Future Sociological Research: What should be embraced vs. avoided?

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AI in Academia & Policy: How should universities regulate AI use?

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AI and Organizing your Research

Workshop Activity: Students design possible research projects incorporating AI in every step with small group discussions and then in-class presentations.

Reading: Bankins, S., Ocampo, A.C., Marrone, M., Restubog, S.L.D. and Woo, S.E., 2024. A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of organizational behavior.