CS 199 UAI: Using and Understanding AI

Draft Syllabus: This syllabus will change before the semester begins. It is based on how the course ran in Spring 2026.
Pure Elective: This course does not meet any degree, program, or general education requirements.

Description

CS 199 UAI is a first course in using and understanding generative AI. Students use generative AI to complete a variety of creative tasks: images, video, and music; study guides; data analyses; and websites. As a final project, students design and build a project of their own using generative AI.

Through first-hand experience, students learn what AI is good at and where it struggles, which makes them more effective users of AI tools. Students also study generative AI from several perspectives: how language models predict text, how neural networks learn, how AI represents meaning, how models are trained to be helpful, and what training and running these models costs in energy, labor, and data.

Throughout the course, students grapple with the big questions posed by the rise of AI: What is intelligence? Does AI understand? How is AI changing work and relationships? Who decides how AI behaves? And how can we support human flourishing as AI becomes more capable?

Catalog Description

Introduces students to using and understanding generative AI through hands-on creative projects and conceptual exploration. Students will use AI tools to create websites, images, music, videos, study guides, data analyses, and personal assistants, gaining practical experience with AI's capabilities and limitations. The course explores how AI systems learn, the infrastructure and data required to train and deploy models, and the history of AI development. Throughout, students examine fundamental questions about intelligence, AI's societal impact, and how to support human flourishing in an age of increasingly capable AI. No programming or technical background required. 3 credit hours.

Information

Website: usingandunderstanding.ai
Course: CS 199 UAI, CRN 65871
Credit: 3 credit hours, letter graded
Time: Tuesdays and Thursdays, 2:00-3:15pm
Location: 3217 Everitt Laboratory
Semester: January 19 to May 5, 2027
First Meeting: Tuesday, January 19 (tentative)
Prerequisites: None
Instructor: Geoffrey Challen (challen@illinois.edu)
Course Staff: To be announced
Office Hours: By appointment

Meetings

The course meets twice a week for 75 minutes. Every meeting is an in-class activity that you work through with classmates, usually in pairs, with the class coming back together to share what each group found. Activities run on the course website, where you record findings, reflections, and links to your AI conversations as you go. Every activity meeting ends with a few minutes of feedback on how it went.

In Spring 2026, the course held 25 meetings: a welcome meeting, 8 labs, 6 exploratory meetings, and 10 discussions. Spring 2027 has more meeting days, so the schedule below adds a planned new exploratory meeting and leaves a few open.

Labs

Labs are hands-on sessions where you use generative AI tools to make something. No technical background or programming experience is required. Modern AI tools let you create complex things, like working websites or data analyses, by describing what you want and refining the results. You don't need to read or modify the underlying code. Your job is to decide what you want, give the AI good instructions, and check whether the result meets your goals.

The Spring 2026 labs were:

AI Scavenger Hunt (two meetings): Mapping where AI is strong and where it fails, working in driver and observer pairs
Creative Media: Generating images, video, and music, and comparing what different people get from the same idea
Study Guides: Using AI to study for your other courses with techniques from learning science
Data Analysis: Asking questions of a real dataset and producing charts and reports with AI
Creating Websites: Building and publishing a website through conversation with an AI agent
Final Project Workshops (two meetings): Pitching, scoping, building, and showing your final project

Most labs end with a gallery or share-out where the class looks at what everyone made.

Exploratory Meetings

Exploratory meetings are guided investigations of how AI works. You use interactive tools built for the course: a next-word probability explorer, a Markov text generator, a single artificial neuron, a digit-recognizing network, an embedding explorer, and simulations of the stages of model training. Then you write your own questions about what you saw, the class picks the best ones, and small groups investigate them and report back.

Discussions

Discussions focus on AI's role in society. Before each one, you read a few short articles (or, once, watch a documentary) and complete a preparation chat about them with an AI agent. In class, you discuss a series of questions with a new partner each round, followed by a whole-class share-out. Some rounds bring an AI participant into a small group chat.

Learning Objectives

By the end of this course, students will be able to:

Effectively use generative AI tools for creative and analytical tasks
Understand the capabilities and limitations of current AI systems
Critically evaluate AI-generated content for accuracy and appropriateness
Explain how AI models learn and represent information
Analyze the societal implications of AI technology
Design and build creative projects using AI tools
Engage thoughtfully with ethical questions surrounding AI development and deployment

Course Approach: No Technical Prerequisites Required

This course is designed for students with no programming or technical background. A core theme of the course is how AI is opening up capabilities that used to require specialized technical knowledge. When you create a website in Week 10, you won't review HTML or JavaScript. You'll describe what you want in plain language, refine the results, and verify that the site works as intended. Accomplishing technical tasks through conversation with AI is new and changing quickly, and it is a real shift in who can build things with technology.

The labs and the discussions are connected. As you use AI tools, you'll run into the same capabilities, limitations, and ethical questions we discuss in class. Your direct experience will ground our conversations about training data, verification, work, and societal impact. Those conversations, in turn, should make you a more thoughtful and effective AI collaborator.

Materials

Textbook: None required
Readings: Assigned articles are provided on the course website
Technology: Bring a laptop to every meeting unless told otherwise
AI Tools: The course website has its own AI chat, and most activities also use ChatGPT, Claude, or Gemini. In Spring 2026, the Creative Media lab used Gemini for images, Pika for video, and Suno for music, and the websites lab used Replit. The free tiers of these tools were enough. The AI tools landscape changes quickly, so the specific tools for Spring 2027 may differ.
Technical Prerequisites: None

Readings

Discussions include short assigned readings to complete before class, along with a preparation chat about them. Readings are posted on the course website and will be updated for Spring 2027. The Spring 2026 discussions used:

The Medium Is the Message: Marshall McLuhan, "The Medium is the Message," from Understanding Media
AlphaGo: The documentary AlphaGo (2017)
Does AI Understand?: Ted Chiang, "ChatGPT Is a Blurry JPEG of the Web," and James Somers, "The Case That A.I. Is Thinking"
From Simple Parts: Two Quanta Magazine articles on how AI differs from the brain and on abilities that emerge in large models
Training Data and Its Costs: Articles on AI data centers, local opposition to them, and the AI industry's use of other people's work
AI and Work: Articles on which jobs AI will change, programmers automating their own work, and whether AI can be pro-worker
AI Companions, Agents, and Trust: Articles on AI companions, teens and role-playing chatbots, and AI agents acting on people's behalf
AI Safety, Alignment, and Governance: Articles on the dispute between Anthropic and the U.S. government and on AI and government surveillance
Human Flourishing in an Age of AI: Articles on the human skills AI lacks, the AI economy, and the AI bubble

Assessment and Grading

The assessment structure for Spring 2027 is still being finalized. This section describes how students were assessed in Spring 2026. Grading weights for Spring 2027 will be posted before the semester begins.

Attendance and Participation: Most of the work in this course happens in class. Each meeting's activity records your participation as you submit findings, reflections, conversation links, and things you made.
Preparation Chats: Before each discussion, you complete a preparation chat about the reading.
Conversational Assessments: Assessments are conversations with an AI interviewer rather than multiple-choice quizzes.
Final Project: A project of your own design, built in class.
Final Project Video: A short video walking through your final project.

Attendance

Students are expected to attend all class meetings. However, each student receives 3 excused absences that may be used for any reason without documentation. You can check your current attendance record on your status page. If you anticipate needing additional absences due to extenuating circumstances, please contact Geoff as early as possible.

Preparation Chats

Before each discussion, you talk through the reading with an AI agent. The agent asks what stood out to you, pushes on your reasoning, and helps you connect the reading to your own experience with AI. It tracks whether you have engaged with the main ideas well enough to be ready for discussion. The goal is to arrive with something to say, not to pass a test.

Conversational Assessments

In a conversational assessment, an AI interviewer asks you to explain concepts from the course and to take and defend positions on open questions. It follows up on your answers, the way a person would in an oral exam. A separate AI evaluator grades the conversation against a rubric on a four-level scale: Excellent, Satisfactory, Needs Improvement, and Unsatisfactory.

In Spring 2026, students took their first conversational assessment, on the Turing test, in class during the Assessments and Agents meeting. Practice assessments on creativity and on later course topics were available online with unlimited attempts. The End-of-Semester Quiz was a 50-minute conversational assessment taken once at the Computer-Based Testing Facility, with a practice version available beforehand.

Final Project

You design and build a project of your own choosing using generative AI. It can extend one of the labs or go somewhere new. The final project is done entirely in class, across the two Final Project Workshops, and you are not expected to work on it outside class. In the first workshop you brainstorm, pitch your idea to a partner, narrow it based on their feedback, and start building. In the second you finish and show it to a small group of classmates. Scope matters more than ambition: you have about two hours of build time, and a small finished project is better than a large unfinished one. Projects are graded on completion, not polish.

Final Project Video

You record a short video (up to 10 minutes) walking through your final project: what you built, who it's for, what was hard, and what surprised you. You upload it to your own YouTube account as Public or Unlisted and submit the link on the course website, which captions it automatically. The video is due at the final meeting. Submitted projects may appear in the course's project showcase.

AI in Course Operations

CS 199 UAI is an experiment in integrating AI into every part of a course. AI is not just the subject of study. It is part of how the course itself operates. AI tools inform our course design, support our instruction, and contribute to how we assess your learning.

In Spring 2026, students interacted with AI in ways that went beyond the creative work in labs:

During class: AI agents joined some small-group discussions as participants
Outside class: Students completed a preparation chat with an AI agent before each discussion, and could ask questions of a course assistant that knows the course materials
For assessment: Conversational assessments were conducted by an AI interviewer and graded by an AI evaluator, including during a proctored assessment at the CBTF

All of these interactions are designed with human oversight and fallbacks. We're not replacing human instruction with AI. We're exploring how AI can support and enhance learning.

If this approach makes you uncomfortable, this may not be the right course for you. We encourage you to reflect on your own comfort level with AI-mediated learning before enrolling. This is not a judgment. Reasonable people have different views on appropriate AI integration in education, and we respect that. But AI integration is fundamental to this course's design, so students who prefer minimal AI involvement in their education should consider other options.

AI Tool Usage Policy

This course is designed to teach you how to effectively and responsibly use generative AI tools. You are encouraged to experiment with AI throughout the course to complete assignments and explore creative possibilities. We will be teaching you these skills throughout the semester, so your ability to meet these expectations will improve over the course of the term. You are expected to: (1) understand and be able to explain any AI-generated content you submit, (2) critically evaluate AI outputs for accuracy and appropriateness, (3) document your AI usage process when requested, and (4) develop your own judgment about when AI tools are and aren't suitable for different tasks. The goal is not just to use AI, but to become a thoughtful and effective human-AI collaborator. Regardless of how much AI assistance you use, you are responsible for the results of your work. This includes the choice not to use AI when you determine it's inappropriate for a task, context, or purpose.

A Note on This Course's Approach

This course teaches you to use AI tools. That pedagogical choice is not neutral, and we want to be transparent about it.

Generative AI systems create real harms. They consume enormous resources, they were trained on data collected without consent, they threaten creative livelihoods, they can spread misinformation at scale, and they may pose risks we don't yet fully understand. Reasonable people, including many of your professors, believe that using these tools is ethically problematic, that they undermine genuine learning, or that their harms outweigh their benefits. These are serious positions that deserve serious consideration, not dismissal.

This course proceeds from the premise that AI tools are becoming pervasive and that learning to use them critically, understanding both their capabilities and their costs, is valuable. But we recognize this premise is contestable. Throughout the semester, you may conclude that you don't want to use AI, or that you want to use it only in limited ways, or that its harms are severe enough that broader societal resistance is warranted. Those are legitimate conclusions, and this course should equip you to reach them thoughtfully rather than foreclose them.

We ask you to engage with AI tools so you can evaluate them from experience rather than assumption. But engagement is not endorsement, and facility with a tool doesn't obligate you to use it after this course ends.

A practical note: Due to the structure of this course, we do expect you to continue using AI tools through the end of the semester. The labs require hands-on AI use, and your ability to reflect critically on AI depends on sustained engagement with it. If you conclude mid-semester that you find AI use ethically untenable, we respect that position, but continuing in this course may not be the right choice. We encourage you to talk with Geoff if you find yourself in this situation.

AI Policies Vary Across Contexts

The skills you develop in this course must be applied with judgment about context. AI policies vary dramatically across courses, institutions, professions, and situations, and for good reasons.

In other courses: Many instructors prohibit or restrict AI use, and these policies reflect legitimate pedagogical concerns. When a professor asks you to write an essay without AI assistance, they're not being old-fashioned. They may be assessing your individual understanding, developing skills that require struggle, or maintaining equity among students with different AI access. The fact that you can use AI doesn't mean you should, and violating course policies is academic dishonesty regardless of your personal views on AI.

In professional contexts: Some fields have strict requirements about AI use in documentation, research, or creative work. Legal, medical, journalistic, and academic contexts may require disclosure, prohibit AI assistance entirely, or have evolving standards you'll need to navigate.

In personal judgment: Even where AI is permitted, you'll need to decide when its use is appropriate. Does using AI for this task serve your goals? Does it align with your values? Are you comfortable with the tradeoffs?

This course teaches you to use AI effectively. It also teaches you to think critically about whether to use it. Both skills matter.

Tentative Schedule

This schedule follows the order of the Spring 2026 meetings, shifted to Spring 2027 dates. Topics, dates, and readings will change. There are no meetings during spring break, March 13-21.

Getting Started (Weeks 1-3)

Week 1

Tuesday, January 19: Welcome and AI Perspectives. First-day introductions through AI-themed discussions.
Thursday, January 21: Lab: AI Scavenger Hunt (Part 1). Working in pairs to map the shape of AI's abilities. Can you catch it making things up, or agreeing with you when it shouldn't?

Week 2

Tuesday, January 26: Lab: AI Scavenger Hunt (Part 2). New challenges: what AI forgets, and when it is confidently wrong.
Thursday, January 28: Exploratory: Assessments and Agents. Take your first conversational assessment, then watch what happens when AI agents talk to each other.

Week 3

Tuesday, February 2: Lab: Creative Media. Create images, video, and music with AI, and compare what different people get from the same idea.
Thursday, February 4: Discussion: The Medium Is the Message. What 1960s media theory reveals about our relationship with AI.

How AI Works (Weeks 4-8)

Week 4

Tuesday, February 9: Exploratory: How Do LLMs Work? Next-word prediction, temperature, and a simple text generator you can train yourself.
Thursday, February 11: Discussion: AlphaGo: Intelligence, Creativity, and the Mirror. Watch the AlphaGo documentary before class.

Week 5

Tuesday, February 16: Lab: Study Guides. Use AI to study for your other courses with practice testing and a study aid you build. Bring materials from another course.
Thursday, February 18: Discussion: Does AI Understand? Do AI systems understand, or do they compress?

Week 6

Tuesday, February 23: Exploratory: Neurons and Networks. Explore a single artificial neuron and a network that recognizes handwritten digits.
Thursday, February 25: Discussion: From Simple Parts. How complex behavior emerges from simple building blocks, in brains and in AI.

Week 7

Tuesday, March 2: Exploratory: Embeddings and Knowledge. How AI represents meaning as positions in space.
Thursday, March 4: Exploratory: The Hardware Behind AI. Why training and running AI requires specialized chips and enormous data centers. New for Spring 2027.

Week 8

Tuesday, March 9: Discussion: Training Data and Its Costs. The energy, human, intellectual, and political costs of AI.
Thursday, March 11: Lab: Data Analysis. Use AI to analyze a real dataset, create visualizations, and find something interesting.

Spring Break: March 13-21

AI and Society (Weeks 9-12)

Week 9

Tuesday, March 23: Discussion: AI and Work. How AI is changing work, who benefits, and what should be done.
Thursday, March 25: Exploratory: How AI Learns to Be Helpful. Pretraining, instruction tuning, and learning from human preferences. You'll rate AI responses yourself.

Week 10

Tuesday, March 30: Discussion: AI Companions, Agents, and Trust. Emotional bonds with AI, AI acting on your behalf, and who is responsible when things go wrong.
Thursday, April 1: Lab: Creating Websites. Build and publish a website by describing what you want to an AI agent.

Week 11

Tuesday, April 6: Discussion: AI Safety, Alignment, and Governance. Who controls how AI behaves: companies, governments, or something else?
Thursday, April 8: Exploratory: The Future of AI. Where AI is heading beyond bigger models: mixture of experts, models on your own devices, and specialization.

Week 12

Tuesday, April 13: Open. Topic to be announced.
Thursday, April 15: Lab: Final Project Workshop 1. Pitch your project, get peer feedback, narrow your scope, and start building.

Final Projects and Synthesis (Weeks 13-15)

Week 13

Tuesday, April 20: Discussion: Human Flourishing in an Age of AI. What makes us human, what AI changes, and what would change if AGI arrives.
Thursday, April 22: Lab: Final Project Workshop 2. Finish your project and show it to classmates.

Week 14

Tuesday, April 27: Open. Topic to be announced.
Thursday, April 29: Open. Topic to be announced.

Week 15

Tuesday, May 4: Reflection and Synthesis. A personal look back at a semester with AI, and a chance to help redesign the course. Final project video due.

PHIL 442: The AI Revolution

Philosophy also offers PHIL 442: The AI Revolution, which examines AI from philosophical and humanistic perspectives.

PHIL 442 examines the current "Artificial Intelligence Revolution," addressing how recent developments in AI challenge traditional ways of thinking. The course explores ethical, social, interpretive, conceptual, technological, and existential implications of AI. It requires at least one philosophy course as a prerequisite and offers 3 undergraduate or 4 graduate credit hours.

How the courses differ:

CS 199 UAI is hands-on and experiential. You create with AI tools while building conceptual understanding. No prerequisites required.
PHIL 442 is philosophically grounded, examining AI through established frameworks in ethics, epistemology, and philosophy of mind. Requires prior philosophy coursework.

Both courses grapple with fundamental questions about AI's implications for society and humanity, but from different disciplinary perspectives and with different pedagogical approaches.

CS 398: Applied Large Language Models

Illinois has also offered CS 398: Applied Large Language Models. Both courses address generative AI but serve different audiences and goals.

CS 199 UAI requires no technical background and focuses on using AI tools through natural language while understanding AI's broader implications. Students complete creative projects (images, music, websites, data analysis) by conversing with AI rather than writing code. The course emphasizes using frontier models (like ChatGPT, Claude, and Gemini) effectively, since these represent the current state of the art for most tasks. Equally important, the course examines how AI works conceptually, the ethical questions it raises, and its impact on society. The goal is to develop thoughtful, effective AI collaborators who understand both what AI can do and what it means.

CS 398 is a technical course focused on building custom AI applications. Students learn to run models locally, understand transformer architectures, work with embeddings, and build systems like chatbots, Q&A assistants, and research agents. The course emphasizes working with open-weight models that run on your own hardware, providing independence from commercial APIs and deeper understanding of the underlying technology. It requires programming prerequisites (CS 101, 105, 107, or 124) and involves hands-on coding projects. The goal is to give students practical skills to build AI-powered tools independently.

Which course is right for you?

If you want to use AI tools effectively and think critically about AI's role in society, without needing to code: CS 199 UAI
If you want to build AI applications, understand the technology at a deeper level, and have programming experience: CS 398
If you're interested in both: the courses complement each other well

Course Policies

Standard university policies regarding academic integrity, disability accommodations, mental health resources, and other institutional requirements will be included here in the final syllabus.