The aim of this course is to explore how humans and artificial intelligence collaborate, communicate, and interact within business contexts as AI transitions from a backend predictive tool to a frontend generative partner. It emphasizes a practical, behavioral, and managerial approach drawing on theories and methods from management, computer science, and human-computer interaction (HCI). The course will combine frontal lectures, case analyses, lab simulations, and a hands-on field experiment.
The course will focus on: - Creating a clear understanding of how Large Language Models (LLMs) and Generative AI function. - Explaining how structural prompting and system instructions drive human-AI interaction. - Learning to analyze behavioral drivers of trust, algorithmic aversion, and anthropomorphism. - Exploring methods for controlled experimentation (A/B testing) to quantify AI productivity and output quality.
Prerequisiti
Basic programming/coding knowledge
Metodi didattici
Frontal lectures Case analysis Lab simulation Hands-on field exercises
Verifica Apprendimento
40% written exam 60% semester project
Extra points for class activities and lab challenges.
Students must pass the written exam to have other activities counted for their final grade.
Testi
Reading materials and case studies will be presented in class.
Contenuti
1. Fundamentals of AI Taxonomy: Narrow AI, Machine Learning, Generative AI, and Autonomous Agents 2. Mechanics of LLMs (Transformers, Tokens, Hallucinations) and Prompt Engineering techniques 3. Psychology of HCI: Cognitive friction, Algorithmic Aversion vs. Appreciation, and Anthropomorphism 4. Experimental Design Methodology and A/B testing in management workflows 5. Collaboration Architectures and AI in C-Suite decision making 6. Managing AI in customer experience, service recovery, and bot-to-human hand-offs 7. Ethical boundaries, intellectual property, workforce dynamics, and agentic workflows
