This foundational course takes African journalists on an experiential journey into AI and gender. You begin by using AI tools, writing headlines, summarizing stories, before any theory is introduced. Then you interrogate: Who was represented? What biases emerged? Through a Master Tracker, you document observations across eight modules, building a cumulative case file.
Moving from utility to infrastructure to source, you learn structured testing, bias measurement, cost tracking, and how to interrogate AI as a hostile witness. The course culminates in a Pitch Lab, where you present evidence-based recommendations to peers and editors. You leave with practical tools, an action plan, and preparation for specialized tracks.
Curriculum
- 7 Sections
- 7 Lessons
- 6 Weeks
Expand all sectionsCollapse all sections
- Module 1Key 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 Tracker1
- Module 2Key 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.1
- Module 3Key Topics in this module include: What is infrastructure? Defining infrastructure as the invisible systems that shape how things work, AI as political infrastructure, AI as socioeconomic infrastructure, AI as cultural infrastructure, AI as institutional infrastructure, “Ooh, that’s why this happened” and Adding infrastructure analysis to Master Tracker.1
- Module 4Key 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 exercises1
- Module 5Key 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:1
- Module 6Key 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).1
- Module 7Key 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:1
Requirements
- A willingness to experiment
- Basic digital literacy; If you can send an email and browse the internet, you have the skills needed.
- Access to a computer or smartphone with reliable internet
- Commitment to active participation; Learning happens through doing. You will be expected to use AI tools, complete weekly exercises, maintain a Master Tracker, and participate in live sessions. Active engagement is what makes the course work.
- Curiosity about gender and media You do not need to be a gender expert, but you should be open to examining how gender shapes newsroom practices, AI systems, and storytelling.
- A newsroom context (or connection to one) The course is most valuable if you can apply learning to a real or aspirational newsroom context. This could be your current workplace, a media organization you collaborate with, or a vision of where you want to work.
- Approximately 2–4 hours per week This includes experimenting with AI tools, watching pre-recorded lectures, completing exercises, updating your Master Tracker, and attending one 90-minute live session per week.
No comments yet! You be the first to comment.