1. Introduction Module

1a. What is AI?

This MOOC introduces Artificial Intelligence (AI) without requiring any prior expertise in computer science. It begins by exploring the concept of intelligence, highlighting key abilities such as reasoning, learning, adapting, and communicating, and compares human and artificial forms. The presentation outlines the two main traditional approaches of AI: Symbolic AI, based on explicit knowledge representation and logical reasoning, and Connectionist AI, inspired by neural networks and capable of learning from large datasets. The historical evolution of AI is discussed, from its symbolic origins to modern success with deep learning and generative models such as large language models (LLMs). AI is now widely used across various fields, including healthcare, finance, robotics, and smart cities, profoundly impacting our daily lives. A hybrid approach, combining both symbolic and connectionist paradigms, is presented as a promising direction for building more robust and explainable AI systems. Finally, ethical considerations are emphasized, including algorithmic bias, data privacy, and the importance of regulatory frameworks like the EU’s AI Act to ensure safe and transparent AI deployment.

1b. The need for AI literacy for Humanists and Social Scientists

The second unit of the Introductory section of this MOOC focuses on the need for AI literacy for Humanists and Social Scientists. It explores what Artificial Intelligence can offer to the Social Sciences and Humanities (SSH), highlighting new pedagogical possibilities and the transformative potential of AI in research and education. The unit also addresses why AI literacy is essential for SSH scholars and discusses the evolving roles of humanists and social scientists in an increasingly AI-driven world.

1c. Educational and Social Issues

This unit discusses the social, educational, and ethical issues raised by AI. It examines the potential risks posed by AI technologies, and presents a vision for their ethical, human-centered, and critically informed use, in alignment with EU regulations and the guidelines of other global organizations. Finally, it highlights the critical role that the Social Sciences and Humanities (SSH) can play in shaping ethical AI tools and guiding their responsible application.

1d. Computational Linguistics

This course introduces the field of Computational Linguistics (CL), exploring how computers process and understand human language. It covers key applications such as machine translation, sentiment analysis, named entity recognition, and authorship attribution. Learners gain insights into the use of corpora (large text collections) for training AI models and conducting linguistic research. Emphasis is placed on the role of Large Language Models (LLMs) in the digital humanities and social sciences, highlighting both their capabilities and ethical considerations. The course provides a foundational understanding of how CL and corpus analysis empower modern AI and language research.

1e. Quiz on Module 1