AI LITERACY
Obiettivi formativi
The primary instructional goals of the AI Literacy course are to empower students with a foundational understanding of Artificial Intelligence (AI) and its diverse applications, enabling them to navigate an increasingly digital and technological landscape.
Risultati di apprendimento attesi
By the end of this course, students will be able to: - Grasp the fundamental concepts, principles, and evolution of Artificial Intelligence. - Recognize the applications of AI in everyday life and professional environments. - Critically evaluate the benefits, challenges, and risks associated with AI technologies. - Engage in informed discussions on the ethical and social implications of AI.
Contenuti Del Corso
The course is divided into two parts: a theoretical introduction and practical hands-on sessions. It begins with foundational AI concepts, including its subfields like machine learning and natural language processing, and continues with practical applications such as prompt engineering, automation with AI in productivity tools, multimedia generation, and multimodal data analysis. Students will also explore the societal and ethical implications of AI.
Testi Di Riferimento
There is no single textbook for this course. Key materials and references will be provided during the lectures and hands-on sessions.
Metodologie Didattiche
The course combines traditional lectures with practical hands-on sessions. Classes are entirely in-person, fostering direct interaction and collaborative learning. Theoretical concepts will be complemented by interactive activities, discussions, and AI tool-based exercises.
Modalità di verifica dell'apprendimento
Assessment will consist of daily exit questions at the end of each lesson, aimed at evaluating students’ understanding and engagement with the course content. At the end of the course, the completion of the course will be verified.
Criteri per l’assegnazione dell’elaborato finale
The final project or thesis will be assigned to students based on their demonstrated interest and engagement with the subject matter.
Settimana 1
The course introduces AI and its subfields, such as machine learning, deep learning, natural language processing, and generative AI. The evolution and history of AI will be explored, with an emphasis on major milestones and breakthroughs.
Settimana 2
Students will learn key AI techniques and algorithms, including supervised and unsupervised learning, neural networks, and natural language models. Practical examples will illustrate how these techniques are applied in real-world scenarios.
Settimana 3
This week focuses on AI applications in various domains, from business to creative industries. Students will examine case studies showcasing the impact of AI technologies.
Settimana 4
Discussion will center on ethical challenges and societal implications of AI, including issues like bias, privacy, governance, and regulatory frameworks. The potential future developments of AI and its role in shaping society will also be considered.
Settimana 5
Hands-on activities will introduce generative AI, focusing on question answering and text classification. Students will design and test prompts and strategies to create effective question-answering models and implement text classification.
Settimana 6
This week’s practical session involves AI integration into productivity tools such as Excel, Word, and PowerPoint. Students will automate data analysis, formula generation, and document formatting, and learn to create visually compelling AI-driven presentations.
Settimana 7
The focus will shift to multimedia content generation. Students will experiment with AI tools to create images, videos, and audio. Prompts for style specification and storyboard creation will be introduced, along with techniques for music composition.
Settimana 8
Students will engage in multimodal data analysis, learning to interpret visual data such as images, charts, and graphs using AI tools. Exercises will include data extraction, scene description, and visual data interpretation.
Settimana 9
The course concludes with hands-on sessions on web research and summarization. Students will practice optimizing search strategies, validating sources, and effectively summarizing research findings using AI tools.