Generative AI for Research

• Course Overview

• Introduction to the Course
• Course Caveats
• AI Content Disclaimer

• Foundations of Generative AI
• History of AI: From Neurons to Neural Networks
• A Lightning Tour of AI (Video)
• But What Is a Neural Network?
• Understanding How Generative AI Works
• An Introduction to Transformers
• Current Generative AI Landscape (Feb 2026)

• LLM Architecture Deep Dive
• Training Large Language Models
• Fine-Tuning, RLHF and Alignment
• How AI Image Generation Works

• What Does AI Actually Consume?
• Infrastructure, Scale, and the Rebound Problem
• Critical Minerals and AI
• Sustainable AI: What Can Be Done?

• Ethical Frameworks and Four Lenses
• Ubuntu, Relational Ethics and the Just AI Framework
• Transparency, Authorship and Integrity
• Applying Ethics: Case Studies and Your Framework
• The Broader Landscape of AI Ethics

• The AI Literature Review Landscape
• Free Tools Deep Dive
• Paid Tools and When They Are Worth It
• The Hallucinated Citation Crisis
• Building Your Research Workflow with Claude
• Hands-On Activities and Assessment

• Writing as Thinking — Why the Process Matters
• Research Ideation with AI
• AI Writing Tools — Landscape and Honest Assessment
• Scientific Integrity and the Writing Pipeline
• Building Your AI Writing Workflow
• Hands-On Activities and Assessment
• Using AI to Review Your Own Work (Supplementary)

• Natural Language to Code — The New Interface
• AI-Assisted Data Analysis in Practice
• Visualization with AI
• Verification of AI-Generated Code
• Building Your Data Analysis Workflow
• Agentic Data Analysis: Giving AI Tools, Not Just Questions
• Hands-On Activities and Assessment

• What Multimodal AI Can See, Hear, and Read
• AI and Scientific Images
• Document Intelligence
• Transcription and Audio Analysis
• Video and Multimodal Workflows
• Hands-On Activities and Assessment

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