Integrating Generative Artificial Intelligence into Social Science Research: Measurement, Prompting, and Simulation

This overview looks at how generative artificial intelligence (GenAI) can be used in social‑science research. It focuses on three main areas: accurate measurement, better prompting, and behavior simulation. Traditional social‑science methods often struggle with cost and scale when they try to measure complex human actions or run large surveys. The paper shows that generative AI models can help by serving as measurement tools, virtual respondents, and interactive agents. The framework stresses that AI‑generated results must be checked against human data to keep measurements reliable. It also points out that well‑structured prompts improve the model’s consistency and reduce false or invented answers. The paper also explores using multi‑agent large language model

(LLM) environments to simulate social attitudes and group dynamics. These computational methods can work at a scale never seen before, but they have drawbacks. There are no standard ways to validate the results, and the models can inherit biases from their training data. Fried Engineers offers research guidance and organizational help for M.Tech students. They assist in creating solid evaluation plans, refining prompt templates, and building simulated settings to study both the strengths and limits of generative AI in computational social science.

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