Artificial intelligence

• Tools to optimize crowdfunding campaigns
• Platforms and strategies
• Case studies 

• Design with GenAI
• Practical examples and guided experimentation
• Integration of chatbots, image generators and mutimodal tools

AI can be useful for many purposes, in the building sector: for instance, it may be used to identify correlations in complex data as well as to define clusters when working on a building stock or to achieve best intervention configurations through multi-objective optimization algorithms, just to mention a few. This lecture will show some examples of use of AI in the building sector, applied to any scale, from large groups of buildings to single HVAC (Heating, Ventilation and Air-Conditioning) systems.

This theme is designed to provide a comprehensive understanding of the fundamental principles of the General Data Protection Regulation (GDPR). Emphasis will be placed on key data protection terminology, core principles and legal bases for processing, the concept of privacy by design, and the management of data subjects’ rights from the data controller’s perspective. The course will also explore real-world examples from research and academia and examine the evolving role of GDPR in the context of artificial intelligence. 

The AI-Assisted Image Processing for Digital Archives course introduces the application of artificial intelligence (AI) techniques in the processing and preservation of digital images within cultural and scholarly archives. Students will explore methods for enhancing, analyzing, linking and interpreting digitized materials, such as manuscripts, old books, photographs, watermarks, and artwork, using modern AI-driven tools alongside traditional image processing approaches. The course combines theoretical foundations with hands-on projects, equipping students with practical skills for improving digital heritage workflows and supporting advanced research in different fields like humanities or information sciences. Topics included in the course are: fundamentals of digital image processing; image enhancement methods (denoising, sharpening, color correction…); automated segmentation and feature detection (inter column, marginalia, header/footer…) in manuscripts and old books; metadata and watermark extraction for archival research and repository linking; ethical issues in AI-driven restoration.

The Prompt Engineering course explores the practice of designing and refining prompts to effectively interact with generative AI systems. Students will be introduced to different strategies for structuring queries, guiding outputs, and evaluating responses for text and image generation. Through practical exercises and case studies, the course emphasizes critical understanding of AI capabilities and limitations, equipping students with skills to correctly apply prompt engineering in research, creative work, and professional contexts. Topics covered within the course are principles of effective prompt design (clarity, context, constraints);  prompting types: zero-shot, few-shot, chain-of-thought, role,  multi-turn, instruction-based, contextual, reflexion/self-correction, tool-augmented; prompting for text vs image generation; evaluating and refining AI responses (bias, accuracy, creativity); applications in education and research.

This course explores the intersection of business modelling and artificial intelligence, focusing on how emerging technologies reshape traditional approaches to strategy and value creation. Participants are introduced to the fundamentals of business modelling while examining how AI can enhance decision-making, optimize operations, and open new pathways for innovation. Through a combination of theoretical insights and real-world examples, the course highlights how organizations can design sustainable and competitive business models by integrating AI into their processes. The program also emphasizes practical applications, showing how data-driven methods support strategic planning, customer engagement, and the development of new revenue opportunities. By the end of the course, learners will understand how to evaluate and implement AI-driven approaches to business modelling in a rapidly changing digital environment.

This hands-on course empowers students to use AI tools and data literacy strategies to study smarter, not harder. As students face increasing amounts of information, tools like ChatGPT, Notion AI, and others can become powerful allies if used wisely. Through interactive activities, real-world examples, and ethical reflections, students will learn how to leverage AI for reading, writing, organising, summarising, and making better academic decisions. The course emphasises critical thinking, responsible tool use, and practical application in both academic and everyday contexts.

This short course on «AI-Supported Qualitative Data Analysis: New Horizons in Research», explores how AI-powered tools are transforming qualitative analysis. We’ll show how software using AI Assistant can accelerate thematic analysis and help researchers identify key patterns and uncover hidden connections in their data. While these tools offer significant benefits, we will also address ethical considerations. The focus is on using AI as a powerful research companion, not a replacement for human insight. Attendees will learn how to leverage these tools for more efficient and reliable analysis, all while keeping control of their research narrative.

This course introduces students to the theory and practice of non-market valuation methods, with a special focus on their application in environmental economics. The course covers traditional approaches such as contingent valuation, choice experiments, and hedonic pricing, while exploring how artificial intelligence can enhance data collection, processing, and analysis. Through lectures and case studies, students will learn how AI tools, ranging from natural language processing to machine learning, can improve the accuracy and efficiency of valuing environmental goods and services that lack market prices.

Artificial Intelligence raises unprecedented questions in the field of intellectual property rights. This session examines legal frameworks and strategic approaches to protecting algorithms, models, and training data. Particular emphasis will be placed on balancing innovation incentives, technology transfer, and ethical considerations in the evolving AI landscape.