.. Lecture overview document

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