Learning outcomes

The course will enable students to :

  • understand the field of artificial intelligence including its technical, historical and human dimensions
  • adopt a critical vision in order to use AI wisely, reflectively and responsibly
  • adopt a scientific and reasoned vision of AI (benefits and risks)
  • be able to defend or oppose the use of AI in an organisation or society
Here is the list of teachers involved in this course: 
 
  • Agathe Picron
  • Aline Wilmet
  • Amélie Lachapelle
  • Anne-Sophie Lemaire
  • Anthony Simonofski
  • Benoît Frénay
  • Benoît Michaux
  • Benoît Vanderose
  • Bruno Dumas
  • Catherine Linard
  • Dominique Lambert
  • Élise Degrave
  • Isabelle Linden
  • Jean-François NISOLLE
  • Juliette Ferry-Danini
  • Katrien Beuls
  • Michel Ajzen
  • Michaël Lognoul
  • Michaël Lobet
  • Nathanaël Laurent
  • Nicolas Franco
  • Nicolas Ruffini-Ronzani
  • Sébastien Dujardin
  • Sophie Vanmeerhaeghe
  • Xavier Devroey

Content

The schedule below shows the content of the course (which may be adjusted slightly as the course will be given for the first time this year; the number of hours is given as an indication):

1) Foundations of AI

  • Preamble (1h): introduction, history of AI, course outline and assessment methods
  • Computer science basics (1 hour): anatomy of a computer, vocabulary, how software is developed,...
  • Fundamentals of AI (6 hrs): types of AI, logid and expert systems, neural networks, generative AI, LLM,...

2) AI and individuals

  • Users and AI (2 hrs): notions of user experience, design principles for AI, transparency and accessibility,...
  • Towards responsible AI (2 hrs): bias, responsibility, relationship with the world and reality, sustainable AI and energy demand,...

3) AI and organisations

  • Use of AI and managerial vision (2 hrs): decision support, automation, organisational applications, new technologies,...
  • Disciplinary applications of AI (4h = 8 x 30 minutes, different speakers ): geospatial AI, AI in history, impact on the profession of developer, AI in physics, AI in medicine,...

4) AI and society

  • Relationship between AI and society (2h): responsibility, impact on society (digital divide, inequalities, etc.), risks (crime, fake news, etc.),...
  • What does the law say about AI (2 hrs): regulations on AI, legal imperatives (copyright, plagiarism, privacy, etc.), impact of AI on law and justice,...
  • AI and education (2 hrs): impact of AI on education.

Assessment method

The assessment is divided into two parts:
 
1.    A report showing the use of AI on a case from the student's field of study (e.g. coding for computing, problem solving in physics, etc.) with a critical analysis: what is the quality of the proposed solution, what are its limitations, what problems have been encountered, etc.?  This part counts for 6/20 and will be assessed on the basis of the critical view taken of the case studied.
 
2.    An oral examination assessing the student's ability to mobilise the various concepts seen in the course concerning the different forms of AI.  This part counts for 14/20.
 

Language of instruction

French