AI-140-paul.wilmoth-2026-08-20-01-11-45
Below is a syllabus template that includes WSU's required syllabus elements.
Title of Course [Introduction to Artificial Intelligence]
Prefix and Number [AI 101]
Semester and Year [tbd]
Number of Credit Hours [3]
Prerequisites:
Course Details
Day and Time: [tbd]
Meeting Location: [tbd]
Instructor Contact Information
Instructor Name: [tbd]
Instructor Contact Information: [office location, phone, email] [tbd]
Instructor Office Hours: [click here for best practices] [tbd]
TA Name: [tbd]
TA Contact Information: [office location, phone, email]: [tbd]
TA Office Hours: [click here for best practices] [tbd]
Course Description
AI 101 provides an engaging, interdisciplinary introduction to artificial intelligence for students from all majors. The course introduces the capabilities of AI, explains at a high level how AI systems work, and examines their implications for individuals, professions, and society.
Through stories, demonstrations, case studies, discussions, and no-code activities, students explore the history of AI; rules and learning from data; machine learning and neural networks; computer vision; language and conversational AI; generative AI; games, robotics, and AI agents; and applications across academic and professional fields.
Students develop practical AI literacy by learning to use accessible AI tools, evaluate and verify AI-generated outputs, recognize limitations, and determine when AI should or should not be used. Questions involving bias, privacy, safety, accessibility, misinformation, sustainability, and human oversight are integrated throughout the course.
The course provides AI majors with a broad and motivating introduction before technical study and enables students from other majors to understand and use AI thoughtfully within their disciplines.
Course Materials
Books: Artificial Intelligence: A Modern Approach, Fourth Edition, Prentice Hall, 2020. 64$
Other Materials: Instructor-prepared notes, short readings, videos, case studies, public datasets, and accessible AI demonstrations and tools will be provided through Canvas. Alternatives will be available when an activity requires an external AI platform.
Fees: 0$
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Course Learning Outcomes (students will be able to:) |
Activities Supporting the Learning Outcomes | Assessment of the Learning Outcomes |
|---|---|---|
|
1. Explain what AI is, how it has developed, and what current AI systems can and cannot do (6e). |
Lectures, demonstrations, timelines, discussions, and myth-versus-reality activities |
Concept checks, reflections, and midterm assessment |
|
2. Describe at a high level how rules, data, algorithms, training, and evaluation contribute to AI systems (7g). |
Visual explanations, worked examples, data activities, and no-code demonstrations |
No-code laboratory, concept checks, and midterm assessment |
|
3. Recognize major AI areas including machine learning, neural networks, vision, language, generative AI, robotics, and agents and identify representative applications (7g). |
Application tours, demonstrations, case studies, guest speakers, and comparison activities |
Quizzes, reflections, and final project |
|
4. Use accessible AI tools to complete a practical task and document the process followed (7f). |
Guided no-code laboratories, prompting activities, and project workshops |
Laboratory portfolio and final project |
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5. Evaluate AI-generated outputs for evidence, accuracy, reliability, uncertainty, and limitations (6b, 6e). |
Output audits, source-verification activities, model comparisons, and peer review |
Generative-AI laboratory and final project evaluation |
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6. Identify potential benefits and risks involving bias, privacy, safety, security, accessibility, misinformation, sustainability, and human oversight (4a, 6e). |
Integrated case studies, stakeholder discussions, and responsible-use activities |
Reflections, human–AI workflow laboratory, and project impact statement |
|
7. Communicate how AI could be used within an academic, professional, creative, or societal context (3a). |
Interdisciplinary project development, peer feedback, presentations, and career exploration |
Final project report, presentation, and individual reflection |
| Dates | Lesson Topic | Assignment | Assessment |
|---|---|---|---|
|
Week 1 |
What Is AI? AI in everyday life; narrow and general AI; automation, intelligence, myths, and expectations |
AI-in-my-life inventory |
Concept check |
| Week 2 [dates] |
How Did We Get Here? A story-driven history from symbolic AI and expert systems to machine learning and generative AI; why AI has advanced rapidly |
AI timeline and myth-versus-reality activity |
Reflection |
| Week 3 [dates] |
Rules, Data, and Patterns Hand-coded rules versus learning from examples; data, features, labels, quality, and representation. Use email spam classification and medical diagnosis as applications. |
Data and decision activity |
Concept check |
| Week 4 [dates] |
How Machines Learn Supervised, unsupervised, and reinforcement learning through familiar examples such as recommendation engines, spam filtering, and game playing agents as applications.s |
No-Code Laboratory 1 launched: Teach a Model from Examples |
Quiz |
| Week 5 [dates] |
Neural Networks and Model Quality Pattern recognition, layers, training, testing, errors, bias, and overconfidence—without equations. Use image classification, speech recognition, and machine translation as applications |
Interactive model demonstration |
No-Code Laboratory 1 |
| Week 6 [dates] |
AI That Sees and Hears Computer vision, speech recognition, medical imaging, accessibility, and surveillance |
Vision-and-speech application activity |
Concept check |
| Week 7 [dates] |
AI That Uses Language Natural-language processing, chatbots, large language models, and cultural or language limitations |
Chatbot comparison and verification activity |
Quiz |
| Week 8 [dates] |
Connecting the Ideas Review of data, models, applications, capabilities, and limitations |
Course synthesis activity |
Midterm assessment |
| Week 9 [dates] |
AI That Creates Generative and multimodal AI for text, images, audio, and video; prompting, iteration, hallucinations, deepfakes, and copyright |
No-Code Laboratory 2: Use and Audit Generative AI |
Laboratory report |
| Week 10 [dates] |
AI That Acts Game-playing systems, robotics, reinforcement learning, autonomous systems, and autonomous AI agents |
Design an AI agent or robotic workflow |
Concept check |
| Week 11 [dates] |
When Should We Trust AI? Evidence, source verification, uncertainty, human oversight, and deciding when not to use AI |
AI-output verification challenge |
Reflection |
| Week 12 [dates] |
Using AI Responsibly Bias, fairness, privacy, security, safety, accessibility, misinformation, environmental societal impact, and academic integrity |
No-Code Laboratory 3: Design a Responsible Human–AI Workflow |
Laboratory report |
| Week 13 [dates] |
AI Across Disciplines Applications in healthcare, agriculture, science, engineering, business, education, transportation, public service, and creative work |
Guest speakers or interdisciplinary case studies; project proposal |
Project proposal |
| Week 14 [dates] |
AI, Work, and the Future Human–AI collaboration, jobs, governance, career pathways, open problems, and AGI debates |
Project studio and peer review |
Project checkpoint |
| Week 15 [dates] |
AI for My Field Showcase Project presentations, cross-disciplinary discussion, course synthesis, and next steps |
Final presentations and individual reflection |
Final project |
Practical Assignments
No-Code Laboratory 1: Teach a Model from Examples
Students use an accessible visual tool to train a simple model. They examine how examples, data quality, and class balance affect the model’s behavior.
No-Code Laboratory 2: Use and Audit Generative AI
Students use a generative-AI tool for a clearly defined task, revise their instructions, verify the output using reliable sources, and document errors, limitations, and improvements.
No-Code Laboratory 3: Design a Responsible Human–AI Workflow
Students design a workflow showing which tasks should be completed by AI, which require human judgment, how outputs should be checked, and what safeguards are necessary.
Final Project: AI for My Field
Working individually or in a small interdisciplinary team, students identify a meaningful problem related to an academic field, profession, creative activity, or community need. They develop an AI application concept, investigation, demonstration, or no-code prototype.
The project addresses:
- the problem and intended users;
- the proposed role of AI;
- the information or data required;
- expected benefits;
- how outputs would be evaluated;
- limitations and potential risks; and
- the role of human judgment and oversight.
The project includes a proposal, final product or demonstration, short report, presentation, and individual reflection. Programming is not required.
Expectations for Student Effort
Students should expect to devote approximately nine hours per week to this three-credit course. This includes approximately three hours of class participation and six hours outside class for readings, activities, laboratory assignments, project development, and assessment preparation.
Grading [add more lines if necessary]
| Type of Assignment (tests, papers, etc) | Points | Percent of Overall Grade |
|---|---|---|
|
Concept checks, quizzes, and short reflections |
100 each | 20% |
|
Three no-code laboratory assignments |
100 each | 30% |
|
Midterm assessment |
150 | 15% |
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Final “AI for My Field” project |
350 | 35% |
| Grade | Percent | Grade | Percent |
|---|---|---|---|
| A |
93–100% |
C |
73–76.99% |
| A- |
90–92.99% |
C- |
70–72.99% |
| B+ |
87–89.99% |
D+ |
67–69.99% |
| B |
83–86.99% |
D |
60–66.99% |
| B- |
80–82.99% |
F |
Below 60% |
| C+ |
77–79.99% |
[Provide information about how grades will be rounded (eg, if 89% earns a B+ and 90% earns an A-, what grade is given to a student with an 89.5?]
The final course percentage will be calculated using full precision and rounded once to the nearest whole number. A fractional portion of 0.50 or higher will be rounded up, while a fractional portion below 0.50 will be rounded down. For example, 89.50% will be rounded to 90%, while 89.49% will be rounded to 89%.
Course-Specific Use of Artificial Intelligence
Using AI thoughtfully is an important part of this course. Students may use designated AI tools for activities and assignments when permitted by the instructor. Submitted work involving AI assistance must include a brief disclosure identifying:
- the tool used;
- how it was used;
- how its output was checked; and
- what the student changed or contributed.
Students remain responsible for the accuracy, evidence, originality, and quality of their work. Students must not enter confidential, personally identifiable, proprietary, or otherwise sensitive information into external AI systems. Undisclosed or prohibited AI use may be treated as an academic-integrity violation.
Attendance and Make-Up Policy
Students should make all reasonable efforts to attend all class meetings. However, in the event a student is unable to attend a class, it is the responsibility of the student to inform the instructor as soon as possible, explain the reason for the absence (and provide documentation, if appropriate), and make up class work missed within a reasonable amount of time, if allowed. Missing class meetings too often may result in reducing the overall grade in the class. Additional accommodation requests (e.g., make-up exams) must go through Access Center.
Academic Integrity Statement
You are responsible for reading WSU's Academic Integrity Policy, which is based on Washington State law. If you cheat in your work in this class you will:
-Receive zero credit
-Be reported to the Center for Community Standards
-Have the right to appeal my decision
-Not be able to drop the course of withdraw from the course until the appeals process is finished
If you have any questions about what you can and cannot do in this course, ask me.
If you want to ask for a change in my decision about academic integrity, use the form at the Center for Community Standards website. You must submit this request within 21 calendar days of the decision.