How to implement creative coding and AI in Visual Design Education?

Creative coding, long practiced within a niche of the design community, has become increasingly accessible, through high-level languages, libraries and now artificial intelligence. Through the use of libraries for creative coding and of Large Language Models, designers can emancipate themselves from their traditional software suites and focus on tools customized for their needs.
However, the use of these tools is still practiced only by a niche of the design world. Therefore, this thesis investigates didactic approaches that integrate classic computational graphics methods with AI-driven generative approaches, aiming to expand the creative workflow of design students, while also reinforcing their technical competencies and their critical engagement with AI generated content. In particular, one central concern is the risk of over-reliance on LLM outputs without sufficient technical understanding.
To explore the findings in practice, a workshop was conducted with 113 third-year students from the “Graphic and Communication” curriculum at IIS Andrea Palladio in Treviso over the course of five days. During eight hours, the students were introduced to basic algorithm concepts and machine learning foundation knowledge, as well as AI-assisted coding with p5.js, and prompt engineering with LLMs. More than 200 works were analyzed, revealing not only creative potentials of these tools, but also crucial gaps regarding digital literacy and English proficiency, trends that align with broader national data, and that prompt to reevaluate the needs of modern education.
This research concludes that while AI can greatly support creative workflows, its responsible and successful use requires above all digital literacy, English proficiency and active engagement, and only then algorithmic thinking and basic understanding of the technical concepts behind them.