2026 · Fall · UT Dallas
MKT 4339.501 syllabus
Contents
- Instructor Information
- Prerequisites
- Course Description
- Learning Objectives
- Class Structure
- Semester Schedule
- Assignments
- Grading
- Attendance and Participation
- Academic Integrity
- Accommodations
This syllabus is subject to change at the discretion of the instructor.
Instructor Information
- Name: Behnam Mo
- Affiliation: Assistant Professor of Marketing, Jindal School of Management, UT Dallas
- Email: bxm180038@utdallas.edu
- Office Location: JSOM II 13.310
- Office Hours: Per appointment. Email me to schedule.
Prerequisites
- MKT 3300. (3-0) Y
- You must bring a personal laptop to every class session. This is non-negotiable as the course is entirely hands-on.
- iPads, tablets, phones, and Chromebooks are not acceptable. You need a full operating system (Windows, macOS, or Linux) to run the software we will use.
- Work laptops are not recommended. We will install software during the semester, and your machine must allow you to install applications without IT approval.
- Make sure your laptop is charged or bring your charger.
- No prior programming experience is required, but you should be comfortable navigating your computer’s file system and installing applications.
IMPORTANT: READ BEFORE ENROLLING
There is no textbook for this course. Instead, you will pay for API access and possibly subscriptions to AI tools used throughout the semester. These are required, not optional. Your total out-of-pocket cost will not exceed what you would spend on a typical textbook, but the exact amount depends on your usage.
These costs are your responsibility. As of now, the university does not cover them.
Course Description
This course teaches you how to use AI, specifically large language models (LLMs) and agentic AI systems like the mechanism used in Claude Code, to create marketing content, automate workflows, and build intelligent tools. The emphasis is on practical, hands-on skills using no-code and low-code platforms, particularly n8n for workflow automation. You will gain enough conceptual understanding of how these systems work (tokens, context windows, tool/function calling, agent architectures) to use them effectively and evaluate their output critically, without needing to understand the math behind the models.
Learning Objectives
By the end of this course, you will be able to:
- Explain, in practical terms, how large language models process text and generate responses.
- Write effective prompts using well-established techniques (system prompts, few-shot examples, chain of thought).
- Use AI to generate marketing content (text, copy, structured data) and critically evaluate the output.
- Understand what tool/function calling is and why it matters for connecting AI to real-world systems.
- Build automated workflows in n8n that integrate AI with marketing tools and data sources.
- Create AI agents that can reason, use tools, and complete multi-step tasks on your behalf.
- Identify risks of generative AI such as hallucinations, prompt injection, copyright issues, bias, and mitigate them.
- Incorporate AI tools into your personal workflow and professional practice.
Class Structure
- When: Mondays, 7:00 – 9:45 PM
- Where: JSOM 2.107
Each session is split into two halves with a break:
| Block | Time |
|---|---|
| Part 1 | 7:00 – 8:15 PM |
| Break | 8:15 – 8:30 PM |
| Part 2 | 8:30 – 9:45 PM |
Sessions combine instruction with live, hands-on work. You will follow along on your laptop during most sessions.
Semester Schedule
The schedule below lists every class session, assignment post/due dates, and holidays. Topics are tentative and may shift as the semester progresses.
First Half: Foundations
| Session | Date | Topic (tentative) | Assignments |
|---|---|---|---|
| 1 | Mon, Aug 24 | Course overview; AI landscape | — |
| 2 | Mon, Aug 31 | How LLMs work (tokens, context, temperature) | A1 posted |
| — | Mon, Sep 7 | NO CLASS — Labor Day | — |
| 3 | Mon, Sep 14 | Prompt engineering fundamentals | A1 due Sun, Sep 13; A2 posted |
| 4 | Mon, Sep 21 | Structured outputs and content generation | — |
| 5 | Mon, Sep 28 | Evaluating AI output; limitations and failure modes | A2 due Sun, Sep 27; A3 posted |
| 6 | Mon, Oct 5 | APIs and tool/function calling (conceptual) | — |
| 7 | Mon, Oct 12 | Mid-semester review | A3 due Sun, Oct 11 |
Midterm grades due: Saturday, October 17. Midterm grade = average of your scores from Assignments 1–3.
Second Half: Agentic AI and Workflow Automation
| Session | Date | Topic (tentative) | Assignments |
|---|---|---|---|
| 8 | Mon, Oct 19 | Introduction to n8n; your first workflow | A4 posted |
| 9 | Mon, Oct 26 | Building AI-powered n8n workflows | — |
| 10 | Mon, Nov 2 | AI agents: concepts, reasoning, tool use | A4 due Sun, Nov 1; A5 posted |
| 11 | Mon, Nov 9 | Building agents in n8n | — |
| 12 | Mon, Nov 16 | Advanced workflows and marketing applications | A5 due Sun, Nov 15; A6 posted |
| — | Mon, Nov 23 | NO CLASS — Fall Break | — |
| 13 | Mon, Nov 30 | Ethics, risks, and the business case for AI | A6 due Sun, Nov 29; A7 posted |
| 14 | Mon, Dec 7 | Course wrap-up and review | — |
| — | Sun, Dec 13 | — | A7 due |
There is no final exam. Assignment 7 is due Sunday, December 13 at 11:59 PM (during finals week).
Assignments
All assignments are individual work. There are no group projects.
There are 7 assignments total. A new assignment is posted approximately every two weeks. Assignments never overlap; one closes as the next opens. You are encouraged (and often required) to use AI tools to complete assignments. When you do, you must disclose which tools you used and provide your prompts or instructions.
Deadlines
Each assignment is due on Sunday at 11:59 PM, approximately two weeks after it is posted. Exact dates are listed in the schedule above. Late submissions receive a zero unless you have a documented excuse (illness, family emergency, etc.). Submit early; technical problems at 11:58 PM are not an excuse.
Resubmissions are not accepted.
File Upload
Upload your work to Canvas by the deadline. If the assignment is a website or app, zip all files. Include screenshots, prompts, and any other relevant materials. Name the file Assignment#_YourName.zip (e.g., Assignment1_SarahCooper.zip).
Grading Timeline
Grades are returned within 2 weeks of the submission deadline. Check Canvas for your scores and feedback.
Exception: Assignment 7 grades will be returned by the university’s final grade deadline (Monday, December 21).
Grading
There are no exams or quizzes. Your grade is based entirely on assignments.
Midterm Grade
Required by the university. Your midterm grade is the average of your scores from Assignments 1–3 (the three first-half assignments). Since there are exactly 3 assignments in the first half, all three scores count.
Final Grade
Your final grade is the average of your top 3 scores from Assignments 4–7 (the four second-half assignments). First-half assignments do not carry into the final grade.
Bonus Points
While attendance is not mandatory, approximately 3 times during the semester (unannounced), I will award a bonus point to every student who is present in class that day. Each bonus point adds 1 percentage point to your final numeric grade. For example, if your calculated final grade is 87% and you earned 3 bonus points, your final grade becomes 90%.
Grading Scale
Letter grades are assigned from the final numeric grade (after bonus points) using the following standard scale:
| Percentage | Grade | Percentage | Grade |
|---|---|---|---|
| 97–100% | A+ | 77–79% | C+ |
| 93–96% | A | 73–76% | C |
| 90–92% | A- | 70–72% | C- |
| 87–89% | B+ | 67–69% | D+ |
| 83–86% | B | 63–66% | D |
| 80–82% | B- | 60–62% | D- |
| 0–59% | F |
Attendance and Participation
Attendance is not mandatory and is not graded. That said, nearly all learning happens during class sessions. If you do not attend, you will likely struggle with the assignments.
Please arrive no later than 10 minutes after the scheduled start time. Write your name on a nametag or piece of paper and bring it to each class.
Academic Integrity
You are expected to use AI tools in this course, that is the point. However, you must always disclose what tools you used and how. Passing off AI-generated work as your own reasoning, or copying another student’s submission, is a violation of UT Dallas academic integrity policies and will be handled accordingly.
Accommodations
Students who need academic accommodations should contact the Office of Student AccessAbility (SAC) and provide their accommodation letter to the instructor at the start of the semester.