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CT380

AI-Assisted Design

Spring 2026

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

D510

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

Tuesday 12 to 1, Wednesday 4 to 5

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

  • Google AI Studio

  • Weavy

  • Rave

  • Flora.ai

  • Floyo

  • Anam

  • Adobe Firefly

  • Lummi.ai

  • Epidemic Sound

  • ElevenLabs

  • FigmaMake

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:

  1. Understand the principles of AI technology and its applications in visual art and design.

  2. Analyze the ethical, copyright, and social implications of AI tools.

  3. Implement AI-assisted design workflows.

  4. Use machine learning, natural language processing, and computer vision technologies to develop a personalized AI-powered creative process.

  5. Apply AI to address design challenges effectively.

  6. Make informed, data-driven decisions in art and design practices.

Projects & Evaluation

1. Midterm Project: 30 points
AI Hackathon 2026

Key Dates & Times:

  • Thursday, March 12, 2026, 6:30-8:20 PM – Hackathon Launch

  • Tuesday, March 17, 2026, by 11:59 PM – Hackathon Submission Deadline

  • Thursday, March 19, 2026, 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.

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.

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: 1/29/26

  • Course introduction

  • Course project scope and expectations

  • Online Self-Paced Learning

  • Slack, Google Drive, Figma Set up

  • AI Journal (Week A & B)

  • [Lecture] CML AI Use Case: Professor C.J. Yeh

Week 1B: Online, self-paced learning

  • Gemini exploration

Week 2A: 2/5/26

  • [Guest speaker session 2] Matthieu Lorrain

    Topic: Liquid Content + Nano Banana

Week 2B: Online, self-paced learning

  • Gemini exploration

Week 3A: 2/12/26

  • [Guest speaker] Julio Soler, MEDIA ET AL

Week 3B: Online, self-paced learning

  • Nano Banana & Veo exploration

Week 4A: 2/19/26

  • [Guest speaker] Michelle & Tarshaa & DJ, Flora

Week 4B: Online, self-paced learning

  • Flora exploration

Week 5A: 2/26/26

  • [Guest speaker] Rave

Week 5B: Online, self-paced learning

  • Rave exploration

Week 6A: 3/5/26

  • [Tentative Guest speaker] Katie Luo

Week 6B: Online, self-paced learning

  • Floyo exploration

Week 7A: 3/12/26

  • [Tentative Guest speaker] Floyo, Matt Shih

  • [Tentative Guest speaker] Anam

Week 7B: Online, self-paced learning

  • Hackathon Prep.

Week 8A: 3/19/26

  • AI Hackathon Finalist presentation
    Time: 12 to 2 PM
    No class

Week 8B: Online, self-paced learning

  • FigmaMake exploration

Week 9A: 3/26/26

  • [Tentative Guest speaker]
    Mirko Santangelo, FoF AI
    Topic: FigmaMake

Week 9B: Online, self-paced learning

  • FigmaMake exploration

Spring Recess
Week 10A: 4/9/26

  • [Tentative Guest speaker]
    Thor Schaeff, Google DeepMind
    Topic: Google AI Studio - Build



Week 10B: Online, self-paced learning

  • Google AI Studio (Build) exploration

Week 11A: 4/16/26

  • [Guest speaker]
    Hedy & David
    Topic: Vibe Coding

Week 11B:Online, self-paced learning

  • Google AI Studio (Build) & GitHub exploration

Week 12A: 4/23/26

  • Final Project Announcement
    In-class Workshop: Final Project Proposal

Week 12B: Online, self-paced learning

  • Final Presentation Prep.

Week 13A: 4/30/26

  • Final Presentation Progress Review
    In-class Workshop: Final Project Production

Week 13B: Online, self-paced learning

  • Final Presentation Prep.

Week 14A: 05/07/26

  • Final Presentation 1 of 2

Week 14B: Online, self-paced learning

  • Presenter comments/takeaway

Week 15A: 05/14/26

  • Final Presentation 2 of 2

Week 15B: Online, self-paced learning

  • Presenter comments/takeaway

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.

  1. Students are required to attend all classes, be on time, and remain for the entire class.

  2. 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.”

  3. The student who arrives 10 minutes after the start of the class will be considered late.

  4. Two late occurrences = one absence

  5. A student who arrives over 30 minutes late or not returning from the break will be considered absent from the class.

  6. 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.

  7. 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:
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.