This term, we will explore visual generative AI through a hands-on and critically engaged investigation into how artists and designers can implement artificial intelligence within their own unique media and methodologies. Moving beyond off-the-shelf tools and pre-trained models, the course focuses on demystifying AI technologies and reclaiming artistic agency in a rapidly evolving media landscape increasingly shaped by algorithmic systems.

Rather than treating AI as a black-box tool for image generation, we will unpack its structures, interrogate its biases, and question its assumptions. Students will learn how to train their own AI systems—developing custom datasets, working with neural networks, and understanding how choices in data and model design impact creative outcomes.

The course asks: What does it mean to collaborate with AI as a medium, not just a tool? How can we embed AI in personal, situated practices rather than adopting generic aesthetics or workflows? And how can artists remain critical and inventive when working with technologies designed to automate and optimize?

Each participant will engage with AI in relation to their own media—be it architecture, video, sound, drawing, performance, or hybrid practices. Through a combination of coding, data creation, and theoretical reflection, students will explore how AI systems can be shaped by and embedded in their individual processes.

By the end of the course, students will have developed a personal AI-based project that reflects their artistic language while engaging critically with the potentials and limitations of AI. The aim is not only technical fluency, but a deeper understanding of how creative practices can resist, redirect, and redefine AI’s role in contemporary culture.

The examination will be in form of a presentation