Students learn what generative AI actually is, how it produces a response, and what that response really costs — in compute, energy, and human labor. From there, they build the confidence and troubleshooting habits to use AI tools independently, and learn the safety rules for what does and doesn't belong in a prompt.

This module tackles the questions students are often afraid to ask: how bias shows up in AI outputs, what privacy and data protection actually require, and who is accountable when AI gets something wrong. Students also learn to recognize when AI is the wrong tool for the job entirely, a critical and often underrated skill.

Students explore where AI genuinely fits into real work — as productivity support, a creative collaborator, and a decision-support tool — while learning to verify and review AI's contributions rather than accepting them at face value.

The practical core of the course: writing clear, specific instructions; iterating on responses that miss the mark; prompting for real analysis and explanation, not just surface answers; and optimizing prompts so students get better results with less back-and-forth.

Students use AI as a research tool while learning to evaluate what it gives them — checking claims, comparing multiple AI responses against each other, and building the habit of verification before trust.

Students learn to interpret and analyze data with AI's help, and — just as importantly — how to ask the analytical questions that lead to real insight instead of surface-level summaries.

Students go beyond using AI to designing it — learning what AI agents actually are, building a working AI assistant, and testing and refining its behavior against real tasks.

Real work rarely fits in a single prompt. Students learn to break complex tasks into workflow steps, pass information cleanly between them, and bring in external tools and information without losing control of the process.

Students learn when and how to disclose AI involvement in their work, how to think through who is affected by an AI-assisted decision, and how to apply everything they have learned to their own field. The module closes with each student writing a personal AI policy that reflects their own values.

 For the capstone, students revisit and improve three to five pieces of work from earlier in the course. The results give direct, visible proof of how their AI judgment has grown since Module 1.