Key topics in this module include: Course orientation and experiential arc introduction, What is AI? (Brief definitions/ light touch) + crash course on prompt engineering, Live Tool Lab: Participants use ChatGPT/Gemini/ NotebookLM/ any LLM to write a headline, summarize a story, suggest sources, Immediate reflection: What felt impressive? What felt off? What felt invisible? and Preview of Master Tracker

Key Topics in this module include: What is bias? Defining bias in AI contexts, Where does bias come from? Gender as a lens: Introducing AWiM research on gendered inequalities in AI and newsrooms, How bias reflects in our work and Introducing the Master Tracker: How to track bias observations across the course.

Key Topics in this module include: Why test systematically? What are we testing for? Structured testing methodologies, Bias Testing Checklist: Systematic tool for documenting test results and Live testing exercises

Key Topics in this module include: The gap between testing and evidence, What is evidence in this context? From individual test to pattern, From pattern to meaning, Types of evidence for different audiences, Quantifying and qualifying evidence, aggregating participant evidence, From evidence to decision (preview of Module 7 and Preparing for Module 8 Pitch Lab:

Key topics in this module include: Why "hostile witness"? Why "source"? The hostile witness framework for AI, AI as a source of knowledge interrogated, AI as a source of power interrogated, AI as a source of inequality interrogated, AI as a source of safety and harm interrogated, AI as a source of narrative control interrogated, AI as a source of possibility interrogated with hope, Revisiting evidence from Module 5 through interrogation and Preparing for Module 7 (Trackers) and Module 8 (Pitch).

Key Topics in this module include: Why trackers? The Bias Tracker for policy and accountability, The Cost Tracker for tooling and resource decisions and Integrating trackers with the Master Tracker: