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Syllabus⇝

This course offers designers and makers a hands-on introduction to generative AI through physical computing. Participants will learn to build agentic systems that bridge the digital and physical worlds by connecting sensors and actuators to AI agents.

Course Contents: 1. Showcase of Salient Projects: The instructors will demonstrate projects that combine generative AI with physical computing and interactive systems.

  1. Introduction to Generative AI and MCP: Participants will learn the fundamentals of generative AI and the Model Context Protocol (MCP), understanding how AI agents can interact with the physical world through connected devices.

  2. Building with Embedded Systems: Students will work with wifi-enabled microcontrollers (such as Raspberry Pi Pico W) to create local MCP servers that expose sensors (temperature, light, sound, humidity) and actuators (LEDs, motors) to AI agents.

  3. Prototyping Agentic Systems: Using Langflow, participants will design and prototype AI agents that can sense and respond to their environment, creating interactive, intelligent physical systems.

  4. Design Considerations and Ethics: The course addresses responsible AI usage, privacy concerns in sensor data collection, and ethical principles for designing AI systems that interact with physical spaces.

By the end of this course, participants will have built their own AI-powered physical computing project, gaining practical skills to create intelligent, responsive systems that blend generative AI with the physical world.

Keywords: Generative Artificial Intelligence, Agentic Systems, Rapid Prototyping

Learning Objectives⇝

Learning objectives:

  1. Gain a clear understanding of the concept of generative AI, its principles, and its applications.
  2. Learn about algorithms, models, and techniques used in generative AI.
  3. Develop practical skills in agentic systems and tools via MCP for generative AI projects.
  4. Understand the ethical considerations and responsible usage of generative AI.
  5. Develop a solid foundation in generative AI for future design and making endeavors.

Structure and Phases (Schedule)⇝

The course unfolds across three intensive 3-hour sessions.

Day 1 - Introduction and projects showcase - Introduction to the class assignment - Starting group making and hardware setup

Day 2 - The evolution of LLMs and agentic systems - Introduction to the anatomy of a MCP server and AI tool usage - Follow-up support for the class assignment

Day 3 - Project Execution in Group Work - Students presentation

Methodological Strategies⇝

  • Introductory lectures to build an understanding of the problem space
  • Group project execution phase to apply learnings on a chosen topic
  • Academic understanding
  • Hands-on/ tactile experience
  • Learning by application
  • Collaborative project execution
  • Iterative Design, Design Thinking

Deliverables⇝

A fully functional physical computing prototype that connects sensors and actuators to an AI agent through MCP, demonstrating meaningful interaction between the digital and physical worlds. 15-minute presentations showcasing the conceptual foundation and live demonstration of the working system. Course documentation on students' blogs summarizing the project outcome, technical implementation, and personal reflection on designing agentic systems for physical spaces.

Grading Method⇝

Percentage Description
20% Participation
30% Prototype and Conceptual Quality
30% Presentation
20% Reflection

European Credit Transfer and Accumulation System (ECTS)

3 ECTS

Additional Resources⇝

Materials Needs⇝

Student’s own computer with a web browser and a python development environment.

Faculty⇝

Christian Ernst

Christian Ernst

AI Expert

Christian Ernst is a creative technologist with a background in UX design. After finishing degrees at Berlin University of Applied Sciences (HTW), he studied the Master of Design for Emergent Futures at the Institute of Advanced Architecture of Catalonia and subsequently at ELISAVA Barcelona. Through his speculative practice he approaches technology critically and question it through different lenses. Projects are ranging from technological investigation into AI to speculative furniture design and multimedia installations. His works and live in Barcelona.

Pietro Rustici

Pietro Rustici

AI Expert

Pietro Rustici is a computer scientist with a background in robotics and design. After finishing degrees at Delft University of Technology (TU), he studied the Master of Design for Emergent Futures at the Institute of Advanced Architecture of Catalonia and subsequently at ELISAVA Barcelona. Through the speculative practice his approach technology critically and question it through different lenses. Projects are ranging from technological investigation into AI to speculative furniture design and multimedia installations. He works and live in Barcelona.