Course Syllabus
Syllabus
Course Instructor
Instructor: Professor David Johnson (office 3146 MEB)
Instructor contact: david.e.johnson@utah.edu
Office Hours:
My office hours are a great time to ask about general course topics or other issues. I will hold office hours after lecture starting in the lecture room, essentially until I work through all questions. Then, I will have in office hours in MEB 3146.
To Be Determined
Class Meetings
Mondays and Wednesdays: 11:50AM - 1:10PM in AEB 340Links to an external site..
Students should come to class prepared to engage with topics and do small in-class exercises.
Textbook and Course Materials
This course will use a variety of free web resources and readings. These will be posted in the weekly Canvas modules.
Course Description and Learning Outcomes
This course introduces artificial intelligence as a creative and analytical tool that is reshaping how people learn, work, and make things. Students will use AI to create, revise, and critique artifacts such as explanations, visual materials, data-informed summaries, simple prototypes, and field-specific communication pieces. These activities will emphasize goal-setting, iteration, evaluation, and the human judgment needed for responsible AI-assisted creation.
To support that work, students will learn how modern AI systems function, including key ideas from machine learning, neural networks, language models, image generation, retrieval-augmented generation, fine-tuning, and AI tool use. The course also examines AI as a historical and cultural phenomenon, including its effects on labor, creativity, authorship, bias, accountability, and responsible use.
Through hands-on activities, discussion, and project-based work, students will build practical AI literacy while learning to evaluate AI’s benefits, limitations, and evolving role in society.
There are no formal prerequisites, but students should have basic math skills such as working with percentages and interpreting graphs, equivalent to MATH 1030 or above.
- Explain the core concepts and historical developments of artificial intelligence, including key milestones, technological shifts, and the rise of modern machine learning and large language models.
- Describe how contemporary AI systems process information, including tokenization, neural networks, context windows, and probabilistic generation.
- Apply foundational AI literacy skills, such as designing effective prompts, interpreting model outputs, and using basic hands-on tools for text or image generation and retrieval-augmented reasoning.
- Analyze the societal and economic impacts of AI, including its influence on labor markets, communication, creativity, and the distribution of power in technological systems.
- Evaluate ethical considerations in AI development and deployment, including fairness, bias, accountability, transparency, and responsible human oversight, and articulate informed positions in discussions or written reflections.
Grading and Assessment
Students in the class will be assessed and graded using the following policies and guidelines:
Readings The course will use weekly readings/viewings from popular science articles, blog posts, videos, and online tutorials to provide context and depth to course activities. This will be posted in the weekly Canvas modules. A small, paper, in-class quiz (most often on Mondays) will assess the reading assignments.
In-class activities A portion of each class will be used for small, hands-on activities designed to build intuition and understanding of lecture topics. These will often be small group activities and will assessed from a worksheet or online form. Other in-class activities will use PollEV audience response questions - see information on how to register for this under the Canvas Course Resources module. You must be present in class to answer the in-class activities.
Tests There will be two midterm, paper tests used to review and assess student understanding of material. See the course schedule below for dates.
AI artifacts You will produce a number of small AI artifacts, along with evidence of thoughtful iteration and critique.
Project The course will conclude with a researched final presentation on a topic based on course activities and student interest.
Note that many of these activities are in-class. Class attendance is essential in this course!
Grading Policies
While attendance during lecture is essential for activities such as quizzes, tests, and in-class activities, the following policies are here to allow some flexibility for life events and absences:
- Two weekly reading quizzes will be dropped
- Two in-class activity scores will be dropped
The drops are automatically applied in the Canvas gradebook and do not need to be requested.
Grade Category Weights
The course grade is computed using the following weights and grade scale.
| Category | Weight |
|---|---|
| Reading Quizzes | 15% |
| In-Class Activities | 15% |
| AI Artifacts | 25% |
| Midterms | 15% each (30% total) |
| Final Researched Presentation | 15% |
Grade Scale
The grade scale used to map grade percentages to a letter grade is
| Percentage | Letter Grade |
|---|---|
| 94 and above | A |
| 90 to 94 | A- |
| 87 to 90 | B+ |
| 84 to 87 | B |
| 80 to 84 | B- |
| 77 to 80 | C+ |
| 74 to 77 | C |
| 70 to 74 | C- |
| 67 to 70 | D+ |
| 64 to 67 | D |
| 60 to 64 | D- |
| below 60 | E |
Course Schedule
The following table shows the expected topics each day during the semester. Some of these topics may sound highly technical - the goal of this course is to build intuition and understanding of how they work. Topics may be adjusted.
Some important dates are:
- October 7 Midterm 1
- December 2 Midterm 2
- Final Presentations Tuesday December 15, 10:30am - 12:30pm
| Week | Monday | Wednesday |
|---|---|---|
| 1 | Introduction to AI | AI History, Hype, and Techno-Shocks |
| 2 | AI, Jobs, and Fields of Use | AI Creation Studio: Explainers, Slash Commands, and Structured AI Use |
| 3 | No class | Artifact Launch 1: Explainer |
| 4 | Machine Learning: Concepts, Workflow, and Evaluation | K-Nearest Neighbors |
| 5 | K-Means Clustering | Artifact Launch 2: Web / Interactive Tool |
| 6 | N-Gram Text Generation: Probability, Graphs, and Limits | Neural Networks: Foundations |
| 7 | Neural Networks: Training | Midterm 1 |
| 8 | No class — Fall Break | No class — Fall Break |
| 9 | Word Embeddings | Training Base Models |
| 10 | Tokens, Context Windows, and Attention | RAG, Tool Calls, and AI With Access to the World |
| 11 | Artifact Launch 3: Data Story and Visualization | Fine-Tuning |
| 12 | RLHF and Alignment | Image Generation / Diffusion |
| 13 | Multimodal AI: Audio, Video, Music, and Story | Artifact Launch 4: Multimedia Communication |
| 14 | AI Agents | AI Bias, Governance, Ethics, and Accountability |
| 15 | Review for Midterm 2 |
Midterm 2 |
| 16 | Research-Grounded Final Project Workshop | AI Futures and Course Synthesis |
| Finals | Final Project Presentations |
Academic Policies
This course will use AI as we learn about AI. However, work that is not instructed to be done by AI must be done by you.
Read the Kahlert School of Computing's academic policiesLinks to an external site. and the College of Engineering policiesLinks to an external site..
The University of Utah has resources to help you. Please also familiarize yourself with University policiesLinks to an external site. on the ADA, safety, sexual misconduct, student support, and academic misconduct.
Changes
The syllabus may be updated with reasonable notice by the instructor.