AI and Research Methods*

*New course running in Methods School 2025!

This five-day intensive course offers social science researchers a comprehensive introduction to the practical applications of Large Language Models (LLMs) like GPT in their research workflows. Through hands-on activities, ethical discussions, and real-world examples, participants will learn how to leverage LLMs to streamline literature reviews, enhance observational research designs, and automate or expedite data collection. The course emphasizes practical takeaways, ensuring that participants leave with actionable skills to responsibly integrate AI into various stages of the research process. By the end of the course, participants will be equipped to critically and effectively use LLMs while maintaining research integrity and transparency.

Dates

This one-week, 17.5-hour course runs Monday-Friday, 30 June - 4 July, 2025. The course is scheduled for 1:30 pm - 5:00 pm.

 

Classroom Location

Faculty of Arts and Social Sciences

 

Instructor

Charles Crabtree, Dartmouth College

 

Detailed Description

This five-day course provides a comprehensive and practical overview of how artificial intelligence—particularly Large Language Models (LLMs) like GPT—can be integrated across the stages of social science research. Participants will learn to harness AI tools for literature reviews, research question development, theory building, measurement design, and writing. The course emphasizes methodological transparency, ethical usage, and reproducibility. It is designed for researchers who want to enhance their workflows without compromising academic rigor.

By the end of the course, participants will:

  • Understand LLM functionality and their applications in social science.
  • Critically evaluate and integrate AI into qualitative and quantitative research workflows.
  • Use AI to develop research designs, construct theory, and synthesize literature.
  • Recognize ethical concerns and apply responsible AI use practices.

 

Prerequisites

Participants should have a basic understanding of the research process in social sciences and some familiarity with academic writing and reading peer-reviewed articles. No prior programming experience or AI expertise required.

 

Requirements

Participants are:

  • Required to attend all lectures and hands-on lab sessions.
  • Expected to have access to an internet-connected computer and to be able to use the statistical software R for this course.
  • Required to submit a short research design outline incorporating AI-supported components. (During the course) 
  • Required to participate in class discussions and a peer-feedback session on Day 5. (During the course)

 

Readings

Readings are to be determined given ongoing developments in AI, but will feature a mix of methodological introductions and substantive applications.