Description§
This course teaches students to be informed, effective, and ethical users of software tools that fall under the broad heading of Generative Artificial Intelligence (shortened to GenAI or just AI). When one uses a tool like ChatGPT, Gemini, or Claude one is accessing a bewildering array of technologies that can feel like accessing a magical, superintelligent oracle. Our goal is to demystify these tools, to understand how they work, where they go wrong, and how they can be integrated into existing workflows.
Note that this course is not about creating our own version of GenAI. That would require more programming than is expected for this course. Students interested in that topic can become computer science majors or minors, learn how to program, and take the Deep Learning course.
Thus this course is focused on a general audience; any student interested in how these tools work and how to use them better is welcome. There will be some technical information but it will be approachable and bounded; we will generate some code but students are not expected to learn how to program. In the broader job market, knowledge workers are increasingly being asked to demonstrate some idea of appropriate use of GenAI; this course is designed to meet that need for students.
Students are required to use GenAI for this course, in the sense that multiple activities and assignments involve using a GenAI tool and either evaluating the output or using the output in some way. If students are fundamentally opposed to using GenAI, they should not take the course. Students who are suspicious of these tools or worried about their impact on society and/or the environment absolutely should take this course — we will explore these issues and more.
A few themes you will find woven through the course:
Big Idea: Effective use of generative AI requires both critical thinking and expertise to judge and validate its output.
Critical Questions:
- Before any task that might use generative AI:
- Can we automate it?
- If yes: Should we? What is the cost? What are the alternatives?
- During the use of AI:
- How do you use it effectively?
- How do you design your input/prompt/usage to be most likely to get the result you want?
- After getting an output:
- Is it right?
- Can you tell? Are you sure?
For an idea of the specific topics covered in the course, see the rough schedule for the semester.
Catalog Description§
Generative AI has exploded in recent years, receiving huge amounts of attention, investment, and hype. In this course students will learn how these systems work, gain skills and experience with current AI tools (especially large language models), and explore their capabilities, limitations, costs, and broader impacts. Open to all students and requires no technical background.
Units: 0.5
Workload: The expected average workload for students is 10-12 hours per week per course unit. For this course this means 5-6 hours per week, of which 2 are in class and 3-4 hours are work outside of class. While some variation is to be expected, if the amount of time you are spending on this course is far outside this range please let the instructor know immediately.
Prerequisites: None
Other Pages§
Semester schedule — tentative; see Canvas for up-to-date details.
Canvas — assignments, announcements, and other online resources will be here.
Details§
When/Where:
TR 10:50-11:40am / CNS E204
Instructor: Mark Liffiton
Office: CNS C207B (2nd floor CNS, in middle of building over atrium)
Office Hours:
- Monday 11am-noon
- Tuesday 10-10:40am; 3-4pm
- Wednesday 11am-noon
- Thursday 10-10:40am; 3-4pm
During my scheduled office hours, I will be in my office and happy to meet with you about just about anything.
If your schedule prevents you from attending my set office hours or you would like to meet sooner than the next office hours you can attend, you have some options: 1) please feel free to drop in any time my door is open, and 2) I’m very happy to set up alternate meetings if you ask at least a day in advance.
Contact (you→me): We will use Piazza for most course- and content-related questions. For anything specific to you, feel free to talk to me before or after class, meet me in office hours, stop by my office, or email (for email, please start the subject with “CS 170:”).
Contact (me→you): I will post course announcements through Canvas, and they will be sent to your IWU email accounts. Check your IWU email frequently or have it forwarded to an address you do check.
Textbook§
There is no required textbook. We will use a variety of freely-available online resources and others I provide.
Grading§
The final grade will be based roughly on the following breakdown:
| Assignments | 10% |
| Labs | 15% |
| Quizzes | 25% |
| Final Project | 40% |
| Engagement | 10% |
Assignments & Labs§
Assignments and labs will be posted on Canvas. Assignments will be individual, independent work, while labs will be completed in pairs.
Late Policy§
Assignments will be due at set times; they will be considered late at any point after that time. An assignment will lose 10% of the total possible points for every day or partial day it is late, and after five days it will not be accepted.
Assignments can’t be accepted at all after solutions have been handed out or the graded work has been returned to the class.
Grace Tokens§
Every student has two “grace tokens” that they may use for extensions in instances where they are unable to complete work by the assigned deadline. To use a grace token on an assignment, send me an email before the assignment deadline, explain why you need an extension, and we will determine an appropriate extension, which will be granted with no grade penalty. Some assignments may not be eligible for grace tokens due to immediate use of or feedback on the submitted work, but most will be.
Engagement / Attendance§
Class time will be complementary to the reading, and you will need both in order to learn all of the material in this class. Furthermore, each student benefits from the engagement of all others in the class. Ten points of your final grade will be based on that engagement. Attending every class period on time and prepared will earn a base of 7 points; points can be gained by constructive participation, in class or out, such as asking questions, answering them, responding in the forum, sharing insights or useful/interesting resources with the class (posting in the forum, for example), investigating concepts beyond the requirement in class, working on small independent learning projects, and in many other ways; points can be lost for excessive (more than 3) unexcused absences, disrupting class (e.g., regularly showing up late), dominating the conversation, and the like.
Absences can be excused with documentation or if arrangements are made with me a week in advance. In general, if you know you will be missing a class, let me know as soon as you can.
Regrading§
If you would like to request a regrade, submit a request to me in writing (via email) within one week of receiving the graded item. Indicate exactly which part you believe deserves a different score and why.
Office Hours§
If any concept, reading, or anything else related to this course is ever unclear to you, please come see me during my office hours. One-on-one, I can help you bridge the gap from what you do understand to what you want to understand. It will generally be the easiest, fastest way to clarify something.
If you cannot attend any of my regularly scheduled office hours, I’m more than happy to set up another time to meet. Just let me know.
Working with Others; Using External Resources§
In this course, collaborating with other students on assignments and using generative AI tools (like large language models) on assignments is allowed.
Important: Submitting any work done by someone or something else without clearly citing them as a source is still plagiarism. In every assignment, you must clearly and accurately state which parts are your own work and which are not, the type and scope of assistance you received, and who / what you received it from. If you used generative AI, you must share and link to the conversation(s) you used in the course of the work.
Also important: Doing the work and thinking with your own brain rather than offloading it is how you learn. If you do not do that work yourself, you will not learn much. That will be reflected in your grades and in your knowledge and skills after graduation. You are responsible here for ensuring you do the actual thinking and work to learn the material. Be mindful of that, and don’t let yourself take shortcuts that skip the learning and harm you later. If I believe you are overusing GenAI such that it is interfering with your learning, then I will let you know. I encourage you to take such warnings seriously, because we are all busy and it is an easy trap to fall into.
For details on the university’s policies regarding academic integrity, please read the University Policies Concerning Student Conduct & Academic Integrity. Academic misconduct can result in failing the course and a report to the associate provost. If you are ever unsure of whether something might be crossing that line, please err on the side of caution. You can also just ask me, and I’ll be happy to provide guidance.
Accommodations§
Illinois Wesleyan University strives to make all learning experiences as accessible as possible. If you anticipate or experience academic barriers based on a disability (including mental health and chronic or temporary medical conditions), it is your responsibility to self-disclose and provide documentation to the Office of Student Accessibility Services. Please note that accommodations are not retroactive and accommodations cannot be provided until I receive an email from Student Accessibility Services. Once the email is sent, please make arrangements with me as soon as possible to discuss your accommodations confidentially so they may be implemented in a timely fashion. For more information contact Student Accessibility Services by e-mailing accessibility@iwu.edu or stopping by their office in Ames Library 201B.
Diversity§
Our university’s mission statement includes, “The University through our policies, programs and practices is committed to diversity […]” Our school and this course are made stronger by the mix of people that come into it bringing a diversity of ideas, experiences, and backgrounds. I expect everyone in this course — instructor, TA, and student — to contribute to an inclusive atmosphere that respects the diversity of all others in it. Dimensions of diversity can include sex, race, age, national origin, ethnicity, gender identity and expression, intellectual and physical ability, sexual orientation, income, faith and non-faith perspectives, socio-economic class, political ideology, education, primary language, family status, military experience, cognitive style, and communication style. The individual intersection of these experiences and characteristics must be valued in our community. If you have related concerns about the class environment or behavior of any in it (including me), you are welcome to raise them with me, and I will do my best to address them. If you are not comfortable speaking with me about them, you may also bring concerns to the Associate Provost.
End-of-Syllabus Advice§
Bring it to class. Bring interesting news items about GenAI to class. Bring your questions about a reading, a lab, or just something you saw to class. Bring your interests to class. Bring your opinions to class. We’ll have many opportunities to share these sorts of things, and we all benefit from you sharing with us. So please: bring it to class.