Generative AI in Survey Design and Data Collection

This material explores the transformative impact of large language models and other generative AI technologies on psychological research methods, with a special focus on survey design, data collection, and analysis. The shift from traditional research methods to AI-augmented approaches represents a significant paradigm shift in computational psychology. This lecture series examines both the promising opportunities and ethical challenges of this emerging field.

Attribution

This documentation is based on contemporary research in computational psychology and AI. The content synthesizes findings from numerous studies, academic publications, and industry reports to provide a comprehensive overview of how generative AI is reshaping psychological inquiry. We gratefully acknowledge the contributions of researchers across multiple disciplines whose work has informed this material.

Objectives of This Lecture Series

After completing this lecture, you will be able to:

• Understand how generative AI can enhance survey design in psychological research

• Evaluate the potential and limitations of synthetic respondent data

• Implement interactive AI-driven survey techniques

• Analyze Likert-scale data using both traditional and AI-augmented methods

• Address privacy and ethical considerations in AI-enabled research

• Apply these techniques to real-world psychological studies

• Design and implement conversational AI survey tools

Lecture Structure

This lecture series is divided into three modules:

1. Foundations of AI in Psychology: Introduction to generative AI and its applications in survey design and synthetic data

2. AI-Driven Data Collection: Implementing interactive AI surveys, addressing privacy considerations, and building AI-powered survey tools

3. Practical Applications: Analysis techniques and future directions for AI in psychological research

Lecture Overview

• Generative AI in Psychological Research: Course Overview
  • Module 1: Foundations of AI in Psychology
  • Module 2: AI-Driven Data Collection
• Lecture 01.1 – Introduction to Generative AI in Psychology
  • Introduction
  • Capabilities of Modern Generative AI
  • The Evolution from Traditional to AI-Augmented Research
  • The Spectrum of LLM Applications in Psychology
  • Key Ethical and Methodological Considerations
  • Case Study: The Emergence of “Silicon Samples”
  • Conclusion
• Lecture 01.2 – Survey Design with Large Language Models
  • Introduction
  • Traditional Challenges in Survey Design
  • How LLMs Can Enhance Survey Design
  • Case Study: AI-Generated vs. Human-Generated Questionnaires
  • Practical Implementation: Human-AI Collaboration in Survey Design
  • Optimizing LLM Prompts for Survey Design
  • Limitations and Best Practices
  • Case Example: Developing a Likert-Scale Measure of Climate Anxiety
  • Conclusion
• Lecture 01.3 – Synthetic Respondents: Simulation and Supplementation
  • Introduction
  • The Promise of Synthetic Respondents
  • The Scientific Reality: Empirical Findings on Synthetic Respondent Fidelity
  • Proper Use Cases and Limitations
  • Case Study: Depression Prediction from Synthetic Clinical Interviews
  • Best Practices for Working With Synthetic Respondents
  • Conclusion
• Lecture 02.1 – Interactive AI Surveys
  • Introduction
  • The Promise of Conversational AI Surveys
  • Empirical Evidence: Do AI Interviews Work?
  • Implementing AI Conversational Surveys
  • Case Study: An AI-Driven Mental Health Assessment
  • Ethical Considerations
  • Best Practices for AI-Driven Surveys
  • Future Directions
  • Conclusion
• Lecture 02.2 – Privacy Considerations
  • Introduction
  • Understanding the Privacy Landscape
  • Closed API LLMs vs. Open-Source Models: A Privacy Comparison
  • Practical Privacy Preservation Strategies
  • Hybrid Approaches: Balancing Privacy and Capability
  • Responsible Documentation and Transparency
  • Case Study: Privacy-Preserving Clinical Assessment
  • Future Directions in Privacy-Preserving AI
  • Conclusion
• Lecture 02.4 - Conversational AI Survey (BFITraitTalk_AI Tutorial)
  • Overview
  • Setup
  • Codebase Walkthrough
  • Frontend Design (User Interface)
  • Survey Logic and Adaptive Interview Flow
  • Backend Structure and Data Flow
  • Psychological Design Considerations
  • Ethical and Methodological Considerations
  • Customization and Extension Ideas
  • Conclusion
• References

Indices and tables

• Index

• Module Index

• Search Page