Course design / Higher education

Foundations before fascination.

Foundations of Generative AI for Business Leadership is designed for non-technical students who need more than a list of tools. It develops the understanding, practice, and judgment required to lead in an AI-enabled workplace.

Discuss course design

The design thesis

AI literacy should survive the next product release.

A course built around today’s interface will be outdated before the next catalog cycle. This course is built around durable capabilities: understanding what the system is doing, directing it intentionally, judging what comes back, and taking responsibility for the result.

Concepts+Practice+Judgment=Fluency

The learning arc

Five moves from first principles to leadership.

Each stage adds complexity while keeping students responsible for the thinking that surrounds the output.

Understand

Build the mental model

How generative models work, why outputs vary, what context changes, and where confident errors come from.

Direct

Learn to shape the work

Prompt design, decomposition, examples, constraints, iteration, and evaluation—not magic words or frozen formulas.

Apply

Use AI inside real tasks

Research, analysis, communication, ideation, and workflow redesign with clear limits and evidence checks.

Evaluate

Make judgment visible

Source verification, bias, privacy, quality criteria, failure analysis, and decisions about when not to delegate.

Lead

Move from use to governance

Responsible adoption, human oversight, change management, measurement, and an actionable business recommendation.

The published textbook

Fifteen chapters shape the course journey.

The course follows Dr. Yoo’s Foundations of Generative AItextbook, moving from how the technology works to ethical use, advanced prompting, and its impact on work and society.

View the textbook

Part 1

How It Works

01
Overview of Generative AI Technologies
02
Understanding the Generative AI Space
03
Understanding How Generative AI Works

Part 2

Ethics

04
Global Concerns
05
Developing Your Personal Code of Ethics

Part 3

Prompt Engineering

06
The Basics
07
Advanced Techniques
08
Multistep, Multilevel, and Nested Prompts
09
Educational Uses Part 1: Role-Playing Scenarios
10
Educational Uses Part 2: Personalized Learning
11
Building a Custom Chatbot

Part 4

Societal Impact

12
GenAI at Work
13
Data Privacy and Security
14
Algorithmic Bias
15
Future Trends

Learning by doing

The assignments reveal the thinking.

Students do not receive credit simply for producing something polished. They show how they framed, tested, revised, and defended the work.

Ethics

Personal AI code of ethics

Students define specific boundaries for their own AI use, then use ethical frameworks to explain and defend those choices.

Applied design

Custom chatbot project

Students use design thinking, advanced prompting, and ethical constraints to build a tool for a real learning need.

Process evidence

Document, explain, and defend

Students provide working drafts, AI conversation threads, in-class work, and oral explanations to make their reasoning visible.

Independent mastery

Closed-book cumulative final

Students validate that their understanding of the course concepts is their own.

Student voice

What durable AI learning feels like from the inside.

Anonymous reflections from students in Foundations of Generative AI, Spring 2026.

“Because of this class, I feel better prepared and more competitive, since I now know how to use AI effectively. It’s a skill I can carry with me into my future career.”
Career readiness

“The skills I see carrying into other courses and into my career are: the ability to think structurally about communication, to anticipate where a process can break down, and to design interactions that guide someone toward a specific outcome.”
Structured communication

“This course has taught me to be more precise with language, more intentional in how I structure instructions, and more aware of how design choices influence outcomes.”
Intentional design

Durable skills for an AI-enabled workplace.

01
Explain core generative AI concepts in plain language.
02
Design and evaluate effective human–AI workflows.
03
Recognize reliability, privacy, bias, and governance risks.
04
Defend when AI adds value—and when it does not.

Course & curriculum partnerships

Build learning that lasts longer than the tool cycle.

Dr. Yoo works with faculty and academic leaders on course design, program integration, certificate pathways, and faculty preparation.

Start a conversation