Generative AI in Psychological Research: Course Overview

This course explores the transformative impact of generative AI technologies on psychological research methods. Below is an overview of the complete lecture series, organized into three comprehensive modules.

Module 1: Foundations of AI in Psychology

Lecture 01.1 – Introduction to Generative AI in Psychology

  • Overview of generative AI capabilities relevant to psychological research

  • Evolution from traditional to AI-augmented research methodologies

  • The spectrum of LLM applications in psychology

  • Key ethical and methodological considerations

  • Case study: The emergence of “Silicon Samples”

Lecture 01.2 – Survey Design with Large Language Models

  • Traditional challenges in survey design

  • How LLMs can enhance question generation and refinement

  • Response scale optimization using AI

  • Bias detection and cultural adaptation

  • Practical implementation through human-AI collaboration

  • Optimizing LLM prompts for survey design

  • Case example: Developing a Likert-scale measure of climate anxiety

Lecture 01.3 – Synthetic Respondents: Simulation and Supplementation

  • The promise of synthetic respondents for data generation

  • Empirical findings on synthetic respondent fidelity

  • Technical artifacts vs. true understanding in LLM responses

  • Appropriate use cases and critical limitations

  • Case study: Depression prediction from synthetic clinical interviews

  • Best practices for working with synthetic respondents

Module 2: AI-Driven Data Collection

Lecture 02.1 – Interactive AI Surveys

  • The promise of conversational AI surveys

  • Adaptive questioning and improved engagement

  • Empirical evidence on AI interview effectiveness

  • Implementation considerations for conversation design

  • Case study: An AI-driven mental health assessment

  • Ethical considerations for interactive AI surveys

  • Future directions in AI-driven data collection

Lecture 02.2 – Privacy Considerations

  • Understanding the privacy landscape in psychological research

  • Comparison of closed API LLMs vs. open-source models

  • Performance vs. privacy trade-offs

  • Practical privacy preservation strategies

  • Hybrid approaches to balance capability and privacy

  • Documentation and transparency requirements

  • Case study: Privacy-preserving clinical assessment

Lecture 02.3 – Additional Privacy Considerations

  • Understanding privacy regulations (GDPR, HIPAA)

  • IRB requirements and obtaining informed consent

  • Data minimization and anonymization techniques

  • Privacy-preserving model deployment options

  • Synthetic data approaches for confidentiality

Lecture 02.4 – BFITraitTalk_AI Tutorial

  • Overview: A working prototype that delivers the Big Five Inventory via a conversational AI interviewer

  • Installation and setup: Running a local LLM for privacy-preserving surveys

  • Codebase walkthrough: Flask app architecture and component interactions

  • LLM integration: Using Gemma 3 for natural language understanding

  • Survey logic: Managing the adaptive conversation flow

  • Backend structure: Data flow and state management

  • Psychological design considerations: Balancing conversational engagement with measurement validity

  • Ethical considerations: Privacy, transparency, and participant experience

  • Customization and extension ideas: Adapting the framework for other survey types