Session 1

AI Composition Tools in the Classroom

Objective: Introduce students to leading AI composition platforms and workflows.
Key Points:

• Platforms like Suno, Aiva, Magenta, Boomy, Riffusion

• Teaching prompt design for music (genre, mood, tempo, instrumentation)

• Using AI as a sketchpad vs. final production

​​

Potential Demonstrations: Live demo creating a 30-second track from scratch with prompts.
Faculty/Industry Guests: Professors from Berklee, USC Thornton; founders of AI music start-ups.
Takeaway: Students leave with practical knowledge of tools and how to integrate them into creative projects.

Session 2

Ethics of Sampling & Training Data

Objective: Equip educators and students with a framework to discuss copyright, ethics, and cultural implications.
Key Points:

• The blurred line between sampling and “training” data

• Lawsuits in progress (e.g., Universal Music vs. AI start-ups)

• Teaching fair use and cultural sensitivity

Classroom Applications: Debate exercises, mock copyright case studies.
Potential Panelists: Entertainment lawyers, music ethicists, artists affected by AI datasets.
Takeaway: Educators gain strategies to help students navigate the gray areas responsibly.

Session 3

AI in Music Production & Post-Production

Objective: Explore AI’s impact on recording, mixing, and mastering.
Key Points:

• AI tools for auto-mixing (LANDR, iZotope Neutron, Sonible)

• Mastering workflows with AI vs. human engineers

• AI as an “assistant” in DAWs (Ableton, Logic, FL Studio)

• Opportunities for teaching production at scale (students learn faster with feedback loops)

Hands-On Lab: Compare the same track mastered by AI vs. by a professional engineer.
Takeaway: Faculty can better prepare students for an industry where hybrid workflows will dominate.

Session 4

Interactive Installations & AI-Driven Performance

Objective: Show how AI can extend live performance and interdisciplinary projects.
Key Points:

• Generative audio responding in real-time to movement, visuals, or biometrics

• Using AI for improvisation in jazz/experimental contexts

• Teaching students to collaborate with machines in performance art

Demo Idea: An AI system generating soundscapes based on audience interaction (motion sensors, visuals).
Potential Speakers: Artists like Holly Herndon & Mat Dryhurst, Catalyst Berlin faculty, creative coders.
Takeaway: Students learn how to think beyond composition/production and into immersive experiences.