Overview

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This course establishes the science and mindset behind effective human-AI collaboration. You will explore how complementary intelligence works, how AI agents learn and adapt, and how to teach AI your professional context so its outputs reflect your role and expertise. You will also build the personal learning loops that keep your skills sharp as AI capabilities grow. Through hands-on activities you will produce a task allocation matrix, a reliance map for your role, a context-teaching prompt library, and a continuous learning-loop playbook. By the end, you will have a clear map of when to lead, when to follow, and when to co-work with AI—plus a portfolio-ready human-AI teaming playbook built around your own responsibilities. You will work with generative AI chat interfaces such as ChatGPT and Claude, Coursera Coach, and provided task-allocation and reliance-mapping templates. No prior AI experience is required.

Syllabus

The Science of Human-AI Teaming
- Discover how humans and AI naturally work best together. You will explore what "complementary intelligence" means, learn when pairing with AI outperforms working alone, and understand how delegating tasks affects your professional skills over time. By the end of this course, you will build a practical task allocation matrix and a reliance map tailored to your everyday role.
How AI Agents Learn and Adapt
- You’ll explore the types, functions, and real-world applications of AI agents. Through hands-on experience with tools like CrewAI, you’ll learn to build and implement AI agents.
Teaching AI Your Context and Expertise
- You’ll explore the use of Generative AI (GenAI) as a strategic tool and it will guide users on setting up a GenAI playground, crafting effective prompts, and critically evaluating model responses.
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.
Building Continuous Learning Loops
- Turn one-off AI use into a system that keeps improving. Design iterative feedback loops, run measured improvement cycles against a metric, and build a personal human-AI learning loop playbook tailored to specific roles.
Project Module: Your Human-AI Teaming Playbook
- In this project, you assemble a single, role-specific Human-AI Teaming Playbook that pulls together the course's core skills: a task allocation and reliance map, a context-teaching and role-definition approach, a confidence-calibration method, and a continuous learning loop. You'll be able to justify, in one coherent document, when to lead, follow, or co-work with AI using ChatGPT or Claude. It matters because this playbook is a portfolio-ready artifact you can show a hiring manager, interviewer, or teammate.

Taught by

Professionals from the Industry

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