Session 1

Prompt Literacy in the Classroom

Objective: Teach students how to “speak the language” of AI art platforms.
Key Points:

• Breaking down prompt structure (style, medium, lighting, era, emotion, camera angles, etc.)

• How multimodal prompts (text + image references) improve results

• “Negative prompting” to avoid unwanted outputs

• Prompt engineering as a transferable skill across platforms

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Hands-On Exercise: Students generate a piece from a vague vs. detailed prompt and critique the differences.

Potential Faculty/Industry Speakers: Creative directors who use AI in design pipelines; platform reps

Takeaway: Students develop prompt fluency as a core artistic literacy of the AI era.

Session 2

AI Aesthetics & Critique

Objective: Equip students to analyze and evaluate AI-generated visuals critically.
Key Points:

• Defining what makes AI art “good” or “original”

• Distinguishing between derivative mimicry vs. generative creativity

• Cultural and stylistic bias in AI models — who gets represented, who doesn’t

• Methods for class critiques: evaluating concept, execution, and originality, not just polish

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Exercise: Side-by-side critique of AI art and human art on the same theme.
Potential Speakers: Art critics, museum curators, theorists in digital aesthetics.
Takeaway: Students learn to think critically about AI art, not just produce it.

Session 3

Hands-On Studio Exercises

Objective: Integrate AI into traditional studio practices.
Key Points:

• Using AI outputs as sketchbooks for ideation

• Remixing and iterating on AI outputs through painting, sculpture, or mixed media

• Collaborative projects: human + AI + peer teams

• Assignments like “translate an AI image into a physical medium”

Workshop Idea: Students take the same AI output and re-interpret it in different        mediums (printmaking, sculpture, digital collage).
Takeaway: AI becomes a creative partner rather than a replacement for studio practice.

Session 4

Copyright, Authorship & Ethics

Objective: Prepare educators to guide students through the legal and ethical landscape.
Key Points:

• Ownership: who owns AI-generated images?

• Training datasets & appropriation: fair use vs. exploitation

• Classroom policy on AI use — when it’s allowed, when it’s not

• Ethical case studies: controversies in AI art competitions and galleries

Panel: Art-law attorney + practicing artist + ethicist in dialogue.
Takeaway: Students leave with a realistic understanding of boundaries and risks.