Beginner4 hours4 modules
AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
- What AI is. Distinguish AI, machine learning, and ordinary software.
- How systems learn. Describe the role of examples, objectives, and generalization.
- Models and inference. Explain what happens when a trained model produces an output.
- Evidence and uncertainty. Check claims, benchmarks, and confident-sounding outputs.
Capstone: AI claim fact check. Choose one public AI claim and produce a one-page evidence review for a nontechnical reader.
Beginner5 hours4 modules
Responsible AI User
Use AI productively while protecting privacy, checking outputs, and preserving human accountability.
- Prompting with context. Turn vague requests into testable instructions.
- Reviewing outputs. Detect unsupported claims and decide what requires verification.
- Privacy and ownership. Choose safe inputs and understand content rights.
- A dependable workflow. Combine AI assistance with checkpoints and human ownership.
Capstone: Human-in-the-loop workflow. Design and test an AI-assisted workflow for a real recurring task.
Intermediate6 hours4 modules
AI at Work
Evaluate workplace use cases, run safe pilots, measure value, and communicate changes responsibly.
- Use-case selection. Prioritize tasks where AI can create measurable value.
- Pilots and experiments. Design a comparison that can reveal whether AI actually helps.
- People and jobs. Assess task changes without reducing workers to job titles.
- Operational governance. Assign ownership for approvals, monitoring, and incidents.
Capstone: AI pilot proposal. Produce a decision-ready pilot proposal for a real organization.
Intermediate6 hours4 modules
AI Policy & Society
Analyze AI systems through rights, equity, governance, safety, and public-interest outcomes.
- Ethics and impact. Distinguish values, harms, tradeoffs, and measurable outcomes.
- Privacy, security, and safety. Separate privacy, misuse, accident, and security risks.
- Law and governance. Compare regulation, standards, audits, procurement, and internal controls.
- Public communication. Explain evidence and uncertainty without hype or false balance.
Capstone: Public-interest AI briefing. Publish a sourced briefing on one current AI policy or social-impact question.
Intermediate8 hours4 modules
Building with AI Systems
Understand language models, retrieval, agents, evaluation, cost, and deployment safeguards through practical system design.
- Language-model systems. Reason about tokens, context, generation, and model tradeoffs.
- Grounding with retrieval. Know when and how retrieval can improve evidence access.
- Agents and tools. Bound multi-step systems with permissions and checkpoints.
- Evaluation and operations. Measure system performance before and after deployment.
Capstone: AI system design review. Produce an architecture and evaluation plan for a source-grounded AI application.