1. Introduction to Artificial Intelligence: o Definitions and scope of AI. o Overview of AI applications in diverse industries. 2. Machine Learning Basics: o Core concepts and examples of machine learning in practice. 3. AI in Information Science: o Applications of AI in library systems and knowledge organization. o Exploration of AI-driven tools for research and information retrieval. 4. Practical AI Tools and Techniques: o Hands-on introduction to popular AI platforms. o Case studies utilizing the "Explore GPTs" feature in ChatGPT. 5. Ethical Considerations in AI: o Privacy concerns and data security. o Addressing bias and promoting fairness in AI algorithms.

Temps présentiel : 17.5 heures

Charge de travail étudiant : 75 heures

Méthode(s) d'évaluation : Examen final