CT380
AI-Assisted Design
Spring 2025
Section
65A
Date & Time
Tuesday 6:30 PM – 8:30 PM (in-person), blended portion (asynchronous online, 2 hours)
Professors
C.J. Yeh, Christie Shin
Classroom
C315
Pre-requisite(s)
None (College-wide elective course)
Credits/Hours
3 credits; 2 lecture and 2 lab hours
School
School of Art & Design
Major
NA
Minor
NA
Office Hours
Monday 1 to 3, Wednesday 2 to 3, Thursday 5 to 6
Office at FIT
D317 (email to schedule a remote meeting)
chinjuz_yeh@fitnyc.edu
christie_shin@fitnyc.edu
Course Description
This course introduces the use of artificial intelligence (AI) in visual art and design. Topics include AI ethics, copyright considerations, social impact, generative design, and AI-assisted creative workflows. Students will explore how AI tools can facilitate creative processes such as content generation, automating design tasks, streamlining workflows, and making data-driven design decisions. Each session includes hands-on exercises with AI tools. A compilation of students’ workshop mini-projects and exercises will be evaluated for the final grade.
Course Goals and Objectives
This course aims to:
Provide a survey of artificial intelligence (AI) models used in design.
Introduce industry-standard AI tools and their applications.
Develop an understanding of the capabilities and potential of AI in the creative field.
Guide students in practical use through hands-on workshops and hybrid online learning materials.
Encourage students to apply AI technologies to their personal study and career goals.
Suggested Software:
LLM: ChatGPT, Gemini, Claude
MidJourney
Runway
Adobe Firefly
Note: The software list is based on available technologies at the time of writing and should be updated each semester to reflect industry advancements.
Student Learning Outcomes
Upon successful completion of the course, students will be able to:
Understand the principles of AI technology and its applications in visual art and design.
Analyze the ethical, copyright, and social implications of AI tools.
Implement AI-assisted design workflows.
Use machine learning, natural language processing, and computer vision technologies to develop a personalized AI-powered creative process.
Apply AI to address design challenges effectively.
Make informed, data-driven decisions in art and design practices.
Projects & Evaluation
1. Midterm Project: 30 points
AI Hackathon 2025 – The Age of CI: The Rise of Creative Intelligence in the Age of AI
This AI Hackathon invites participants to explore narrative design through multimodal AI. Students will discover how generative AI can serve as a creative partner in storytelling.
Key Dates & Times:
Tuesday, October 21, 2025, 12–2 PM – Panel Discussion & Hackathon Launch
Sunday, October 26, 2025, by 10 PM – Hackathon Submission Deadline
Tuesday, October 28, 2025, 12–2 PM – Showcase & Final Presentation
Duration: 1 week
2. Final Project: 30 points
Major-Specific Project Using AI Assistance
Students will select one or more AI tools to develop a unique creative project aligned with their major. The goal is to explore how AI can support ideation, prototyping, content generation, visual design, or user interaction—extending creativity through AI-assisted design.
Project Requirements:
Choose one or more AI tools (e.g., ChatGPT, Flora, Google Deepmind, Midjourney, Figma AI, etc.)
Define a clear problem or concept to explore or solve using AI
Document your creative process: research, tool selection, experimentation, iteration
Present your final outcome with supporting visuals or prototypes
Reflect on your experience: How did AI influence your design thinking or execution?
Deliverables:
10-minute presentation
Final project outcome (visual, interactive, or conceptual)
Process documentation (workflow)
Evaluation Criteria:
Innovation and originality
Clarity of concept and execution
Effective use of AI tools
Depth of reflection and process
Duration: 4 weeks
3. AI Journal & Weekly Online Learning: 20 points
Throughout the course, students will maintain an AI Journal to document their experiences with in-class activities, hands-on exercises, and tool explorations. This journal will serve as a record of experimentation, insights, and personal growth.
For the final presentation, students will curate and present selected journal entries to demonstrate their understanding of how AI can enhance creative practice.
Deliverables:
AI Journal: A detailed and organized compilation of in-class activities, online exercises, and weekly reflections (Week A: In-person; Week B: Online learning summary)
Students must upload notes and exercises from each session (both in-person and online) to the class Google Drive. Submissions will be reviewed periodically throughout the semester.
4. Professionalism: 20 points
Assessed based on:
Attendance and punctuality
Active participation in class
Presentation quality
Overall engagement and responsibility
* You must submit all your projects for the final grade no later than the last day of class
A/A-: 90% or above (A- 90-94 points, A 95 points above)
B+/B/B-: 89% – 75% (B+ 89-85 points, B 84-80 points, B- 79-75 points)
C+/C/C-: 74% – 60% (C+ 74-70 points, C 69-65 points, C- 64-60 points)
D: 59% – 51%
F: 50% or below
Weekly Outline
* Weekly outline is subject to change according to the pedagogical needs.
Week 1A: 8/27/25
Course introduction
Course project scope and expectations
Online Self-Paced Learning
Slack, Google Drive, Figma Set up
AI Journal (Week A & B)
Survey
[Lecture] CML AI Use Case: Professor C.J. Yeh
Labor Day - 9/1
Week 1B (Online, self-paced learning)
TBA
Week 2A: 9/3/25
[Guest speaker] LLM Overview: Professor Calvin Williamson, Science and Math Large Language Models, Artificial Intelligence and Data Science
Week 2B (online, self-paced learning)
TBA
Week 3A: 9/10/25
[Guest speaker] Midjourney: Darren Yao
Week 3B (Online, self-paced learning)
TBA
Week 4A: 9/17/25
[Guest speaker] Flora Session 1: Weber Wong
Week 4B (Online, self-paced learning)
Flora Tutorial
Rosh Hashanah - 9/24
Week 5B (Online, self-paced learning)
Flora Tutorial
Yom Kippur - 10/2
Week 6B (Online, self-paced learning)
Flora Tutorial
Week 5A: 10/8/25
[Guest speaker] Flora Session 2: Justine Jung, Michelle Ma
Week 6A: 10/15/25
[Guest speaker] Flora Session 3: Weber Wong
Week 8B (Online, self-paced learning)
TBA
AI Hackathon - 10/21/25
Week 3A: 9/10/25
TBA
Week 3B (Online, self-paced learning)
TBA
Week 3A: 9/10/25
TBA
Week 3B (Online, self-paced learning)
TBA
Week 3A: 9/10/25
TBA
Week 3B (Online, self-paced learning)
TBA
Week 3A: 9/10/25
TBA
Week 3B (Online, self-paced learning)
TBA
Week 3A: 9/10/25
TBA
Week 3B (Online, self-paced learning)
TBA
Week 3A: 9/10/25
TBA
Week 3B (Online, self-paced learning)
TBA
Week 3A: 9/10/25
TBA
Week 3B (Online, self-paced learning)
TBA
Week 3A: 9/10/25
TBA
Week 3B (Online, self-paced learning)
TBA
Week 3A: 9/10/25
TBA
Week 3B (Online, self-paced learning)
TBA
Week 3A: 9/10/25
TBA
Week 3B (Online, self-paced learning)
TBA
Week 3A: 9/10/25
TBA
Week 3B (Online, self-paced learning)
TBA
Creative Technology & Design (CT&D) Attendance Policy
Attendance is not optional. If you are going to miss a class, you must contact me via email ASAP. Due to the quantity of material covered in the course, I will not be able to spend class time explaining missed assignments or redo lectures. If a class is missed, it is your responsibility to get information regarding missed assignments and lectures from one of your classmates.
Students are required to attend all classes, be on time, and remain for the entire class.
Students who miss three classes for classes meeting once a week or four classes for classes meeting twice a week will receive a grade of “F.”
The student who arrives 10 minutes after the start of the class will be considered late.
Two late occurrences = one absence
A student who arrives over 30 minutes late or not returning from the break will be considered absent from the class.
Working on projects for another class or using digital devices for socializing (texting, social media…etc.) or gaming during class time will be recorded as an absence.
An excused absence is still recorded as an absence. The difference is an excused absence won’t impact your grade for professionalism and class participation.
Additional Course Information:
Grade Appeals: Include information on the grade appeal process. See Grade Appeal for more information.
Department Policy on Plagiarism
Plagiarism and other forms of academic deception are unacceptable. Each instance of plagiarism is distinct. A plagiarism violation is an automatic justification for an “F” on that assignment and/or an “F” for the course. A student found in violation of FIT’s Code of Conduct and deemed to receive an “F” for a course may not withdraw from the course prior to final grade assignments.
Use of AI tools
It is permissible to utilize AI tools in your creative process. However, you must identify which AI tool is being used at each stage of the process. You are required to fact-check AI output and avoid stereotyping and bias in your work. Finally, you are responsible for ensuring that the final creation is unique, ownable, and without any copyright issues.
Fact-checking AI output
AI tools are not infallible. They often generate incorrect or misleading information. It is your responsibility to fact-check any AI output before using it in your work. This includes checking the source of the information, evaluating the quality of the information, and considering the context in which the information was generated.
Avoiding stereotyping and bias
AI tools can be trained on data that contains stereotypes and biases. This can lead to AI output that is also biased. It is your responsibility to avoid the potential for bias in AI output. You should also be mindful of your own biases when using AI tools and take steps to mitigate them.
Ensuring the uniqueness and ownership of your work
You are responsible for ensuring that the final creation of your work is unique and ownable. This means that you must not plagiarize the work of others, including submitting works done solely by AI tools without meaningful improvement and input from you.
Penalty for violation
Violation of this policy may result in a grade reduction or suspension from the class.