Data Science Ethics Lecturer, School of Information
Generative AI: Fundamentals, Applications, and Challenges
What You'll Learn
- Understand the basics of generative AI
- Discuss the application of generative AI
- Describe the general risks associated with generative AI systems
3 Modules
3 Hours
1 hr per module (approx.)
Rating
About Generative AI: Fundamentals, Applications, and Challenges
The rapidly evolving landscape of generative artificial intelligence (AI) means that both novice and skilled users alike should be aware of the proper applications and challenges of these powerful tools.
In “Generative AI: Fundamentals, Applications, and Challenges,” you’ll discuss the potential benefits, uses, and challenges of a variety of applications for generative AI. Explore the impact these tools could have on business, operations, consumers, society, and the environment. Aspects of the course also dive into the general risks associated with this technology, including issues such as copyright infringement, outdated data, malicious attack surfaces, bias, and more. Throughout this course, you'll gain a strong baseline of knowledge for generative AI, one that will allow you to explore further considerations about its impact on businesses and society.
Skills You'll Gain
- AI Agents
- AI Innovation
- Artificial Intelligence
- Generative Adversarial Networks
- Generative AI Agents
- Generative Artificial Intelligence
- Generative Model Architectures
- Responsible AI
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to Generative AI: Fundamentals, Applications, and Challenges, the first course in the Responsible Generative AI series. This online course introduces the core ideas behind generative artificial intelligence and its responsible use. You will explore how GenAI works, where it is applied across domains, and the ethical and societal challenges it presents. This course builds a strong foundation for understanding GenAI’s capabilities, limitations, and implications for business and society.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: Introduction to the Course
- Reading: Welcome to the Course
- Video: Introduction to Responsible Generative AI
- Discussion Prompt: Introduce Yourself
- Video: What is Responsible and Trustworthy Generative AI?
- Reading: Course Syllabus
- Reading: Help Us Learn About You!
- Reading: Module Introduction
- Video: Basics of Generative AI
- Video: Predictive vs. Generative AI
- Video: Lifecycle of Generative AI
- Video: Data Training
- Video: Fine Tuning
- Video: System vs. User Prompts
Module 2: Use Cases of Generative AI
- Reading: Module Introduction
- Video: Use Cases
- Discussion Prompt: Reflection Opportunity
Module 3: Responsible Generative AI Concepts
- Reading: Module Introduction
- Video: Inaccuracies in Outputs
- Video: Data Protection
- Video: Bias & Stereotyping
- Video: Copyright Infringement
- Video: Malicious Attack Surfaces
- Video: Outdated Training Data
- Reading: Practice with Prompts
- Discussion Prompt: Reflection Opportunity
- Reading: Supplemental Resources
- Reading: Post-Course Survey
- Reading: Staying Current with Responsible Generative AI and Governance
Grading Policy
Learners must complete all required assessments to pass the course. An overall score of 80% or higher is required to earn the certificate. The course grade is based on two knowledge checks worth 40% each, and two honor code assignments worth 10% each of your final grade.
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Beginner Level
No prerequisites required.
