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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

Christie Shin

Classroom

Pomerantz 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

Monday 1 to 3, Tuesday 12 to 1, Wednesday 4 to 6

Office at FIT

D317 (email to schedule a remote meeting)
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:

  • Adobe CC

  • Adobe Firefly

  • ChatGPT

  • DALL·E

  • MidJourney

  • Visualelectric

  • Runway

  • Google Gemini + Google DeepMind

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
✏️ Midterm Project: AI Tool Exploration and Presentation

Objective:

Encourage students to research and experiment with AI tools beyond those covered in class, fostering independent exploration and collaboration.

Project Description:

Students will form teams of four and identify AI tools not discussed in class. Each team will:

  • Research: Investigate the capabilities, features, and potential use cases of selected AI tools.

  • Explore: Each team member will experiment with one of the tools and create a design artifact or workflow example demonstrating its application.

  • Analyze: Evaluate the tool’s strengths, weaknesses, and relevance to creative workflows.

  • Present: Deliver a cohesive team presentation showcasing the tools’ capabilities, individual explorations, and potential use cases in the creative industry.

Duration: 3 weeks

Deliverables
Presentation: A 10-15 minute in-class presentation.

  • Overview of researched AI tools.

  • Individual demonstrations by team members showcasing tool explorations.

  • Discussion of potential applications and key insights.

  • Final takeaway summarizing the tools’ impact on design practices.


✏️ Final Presentation: AI Journal

Objective:

Encourage students to reflect on their learning journey, showcase their progress, and demonstrate their understanding of AI tools and their applications in creative design workflows.

Project Description:

Throughout the course, students will maintain an AI Journal documenting their experiences with in-class activities, hands-on exercises, and tool explorations. The journal will serve as a record of their experimentation, insights, and growth. For the final presentation, students will curate their journals to highlight key takeaways and present their understanding of how AI can enhance their creative process.

Deliverable:
1. AI Journal: A detailed and organized compilation of in-class activities, hands-on exercises, and reflections, showcasing the student’s engagement with AI tools and methodologies (from week1A to week13B).

Week A: In-person, Week B: Online learning summary

2. Presentation: A 10-minute in-class presentation where students:

  • Summarize your learning journey.

  • Highlight key activities and outcomes.

  • Discuss favorite tools and how they can be applied to future design practices.


Evaluation And Grading

  1. Professionalism (attendance, participation, presentation, etc.): 20 points

  2. Midterm Presentation: 20 Points

  3. Weekly Journal: 40 Points

  4. Final AI Journal Presentation: 20 Points (* you have an option to do a project instead of Journal Presentation)

* 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.

* There are 10 LinkedIn courses I have carefully selected for AI learning as part of Online Session (B). If you complete a course and submit the certificate, you can earn extra credit: 1 point per certificate, up to a maximum of 10 points.

* 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

Course Weekly Outline:

Weekly outline is subject to change according to the pedagogical needs.

Week 1A

  • Introduction - syllabus

  • – Guest Speaker –
    Calvin Williamson, Professor, Science and Math

Large Language Models, Artificial Intelligence and Data Science

  • – In-Class Activity –

  • – Homework –
    AI Journal

Week 1B
Week 2A

  • – Guest Speaker –
    Matt Owens, Athletics: Case studies using ChatGPT, Midjourney, Runway

  • – In-Class Activity –

  • – Homework –
    AI Journal

Week 2B

  • – Online Self-Paced Learning for This Week –
    LinkedIn: Adobe Firefly essentials (3h 48m) Part1

  • – Homework –
    AI Journal: Key takeaway for the course

Week 3A (2/11/25)

  • – Guest Speaker –
    Kris Kashtanova, Adobe
    Adobe Illustrator & AI Integration - Part 1

  • – In-Class Activity –

  • – Homework –
    AI Journal

Week 3B

  • – Online Self-Paced Learning for This Week –
    LinkedIn: Adobe Firefly essentials (3h 48m) Part 2

  • – Homework –
    AI Journal: Key takeaway for the course

Week 4A (2/18/25)

  • – Guest Speaker –
    Kris Kashtanova, Adobe
    Adobe Photoshop & AI Integration - Part 2

  • – In-Class Activity –
    Mid-term team forming.

  • – Homework –
    AI Journal

Week 4B
Week 5A

  • Singapore Conference
    Midterm AI tool Exploration.

  • – Homework –
    AI Journal

Week 5B

  • Singapore Conference

  • Midterm AI tool Exploration.

  • – Homework –
    AI Journal

Week 6A (3/4/25)

  • – Guest Speaker –
    Kris Kashtanova, Adobe
    Adobe Video/ Audio & AI Integration - Part 3

  • – In-Class Activity –

  • – Homework –
    AI Journal

Week 6B
Week 7A

  • – Guest Speaker –
    James Pearce, Technical Manager, Technology Development Team: ChatGPT+ Coding

  • – In-Class Activity –

  • – Homework –
    AI Journal

Week 7B
Week 8A

  • Midterm Presentation: Research on AI tools & their capability (Team)

  • – Homework –
    AI Journal

Week 8B

  • – Online Self-Paced Learning for This Week –
    Midterm Survey

Week 9A

  • Julio Soler: Midjourney, Runway

  • – Homework –
    AI Journal

Week 9B
Week 10A

  • Matthieu Lorrain, Google DeepMind

  • – Homework –
    AI Journal

Week 10B
Week 11A

  • Darren Yao: Midjourney, Visualelectric

  • – Homework –
    AI Journal

Week 11B
Week 12A

  • Darren Yao: Jitter

  • – Homework –
    AI Journal

Week 12B
Week 13A

  • AI use in real world project.

  • – Homework –
    AI Journal

Week 13B
Week 14A

  • Final Presentation - Part 1

Week 14B

  • Final Survey

Week 15A

  • Final Presentation - Part 2

Week 15B

  • Final Survey

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.