Ai Literacy

The course aims to provide a clear, accessible, and conceptually rigorous understanding of the fundamentals of Artificial Intelligence and generative AI, building a shared lexicon and intuitively explaining how machine learning models and language models work. The course illustrates the central role of data, vector representations, and embeddings, showing how text, images, and sounds are translated into computational structures. At the same time, it promotes a critical understanding of the structural limitations of generative AI, clarifying capabilities, constraints, and possible misunderstandings. It analyzes the main ethical and social risks, with particular attention to bias, discrimination, and misuse. The course also introduces the principles of explainability, transparency, and accountability, along with the main regulatory references and organizational best practices, ultimately guiding participants toward the informed use of generative AI tools through real-world cases, the development of prompt engineering skills, and the design of use cases consistent with their professional context.

Upon completion of the course, students will be able to define artificial intelligence and generative AI, distinguishing between symbolic rule-based approaches and systems that learn from data, understanding how generative models work. Students will be able to describe the role of data in machine learning processes, from collection and encoding to transformation into numerical representations, interpreting embeddings and vector spaces as tools for representing texts, images, and concepts in computational form. They will be able to understand at a conceptual level how neural networks, deep learning, and language models work, recognizing their statistical principles of prediction and generation, and will be able to critically analyze the structural limitations of models in terms of approximation, generalization, opacity, and dependence on training data. They will also be able to recognize biases and ethical risks, applying principles of explainability, transparency, and accountability, also considering regulatory references, and consciously use generative AI tools in professional and educational contexts. Finally, they will be able to define effective prompts and develop consistent and responsible use cases, integrating technical expertise, critical thinking, and ethical awareness for the strategic use of generative AI.