Overview

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This course builds the judgment layer of AI-augmented work. You will learn to evaluate AI outputs critically, red-team them for bias and hallucination, run root cause analysis on AI reasoning, calibrate your confidence, and justify AI-informed decisions to stakeholders. A short GenAI Literacy module on responsible and ethical AI use extends the content into everyday workplace practice. You will complete a reliability audit, execute a red-team protocol with a failure-mode report, run structured analysis using frameworks such as 5 Whys, fishbone, and Pareto, score outputs against a confidence rubric, and produce a stakeholder-ready decision brief. By the end you will have a defensible framework for making AI-informed decisions at work. You will work with generative AI chat interfaces, analytical frameworks and templates (provided), Miro, synthetic media verification tools, and Coursera Coach. Ideal for professionals preparing for greater decision-making responsibility.

Syllabus

Critical Thinking as an AI Collaborator
- Learn to explore the use of Generative AI (GenAI) as a strategic tool for employees. It guides users on setting up a GenAI playground, crafting effective prompts, and critically evaluating model responses. The course demonstrates GenAI's capabilities in handling queries and acting as a thought partner, with practical exercises and readings to enhance understanding.
AI Output Verification and Red-Teaming
- You will move from judging AI output to systematically stress-testing it. Run red-team bias protocols, detect hallucinations across many outputs, and assess whether images, audio, or video are synthetic, producing failure-mode reports, accuracy scorecards, and authenticity reports along the way. Verification builds the confidence needed to put AI-assisted work in front of stakeholders.
GenAI MODULE: Responsible and Ethical GenAI Use at Work
- Building on core AI-evaluation skills, this module shows how to use generative AI responsibly and ethically at work. Using ChatGPT and provided workplace scenarios, you will reason through privacy, disclosure, bias, and accountability questions and draft a personal responsible-use checklist. Getting the ethics right is what makes AI-augmented work trustworthy to colleagues, customers, and regulators.
Structured Analysis for AI-Augmented Decisions
- AI is already shaping workplace decisions through risk scores, predictive flags, recommendations, and summaries. But when someone asks how a decision was made, "the AI suggested it" is not an answer. This module introduces complementary frameworks for working with AI-informed decisions: root cause analysis adapted for AI systems.
Confidence Calibration: When to Trust and When to Override AI
- Calibrate how much to trust an AI output and when to override it. Score outputs with a confidence rubric, catalog uncertainty signals that mark tool limits, and make documented trust/override decisions across varied scenarios. Calibrated reliance builds the foundational judgment required to keep AI-augmented decisions safe.
Project Module: AI-Informed Decision Justification Package
- In this capstone-style project you assemble a single, portfolio-ready decision justification package for a realistic high-stakes decision, integrating the full set of critical AI decision-making skills. Using ChatGPT and provided templates, you will verify AI outputs, calibrate confidence, run root cause analysis, and produce a transparent, stakeholder-facing justification. It is the artifact that shows a hiring manager you can lead AI-augmented decisions responsibly.

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