Assessment criteria

  • Case study: Based on a business case study detailing the company’s sector of activity and future challenges, the candidate will supplement the sector analysis using external sources and propose various strategic options for the use of AI and big data within the company.
  • Practical case study: Based on an AI project currently being rolled out within a real or fictional company, the candidate presents a policy for addressing the human, organisational and environmental impacts of AI use and proposes remedial solutions.
  • Simulated professional scenario (structuring a business model): using a set of raw data from various sources, the candidate must analyse the various components of the organisation’s data value chain using a range of technologies and methodologies to convert raw data into usable data, and propose different options for leveraging this data for the company.
  • Practical case study: Based on a real-life AI project, the candidate will be required to produce a report containing an in-depth analysis of the issues and constraints surrounding data usage, as well as the ethical implications of using AI.

Teaching Methods

The programme draws on a variety of teaching methods designed to promote active, engaging and stimulating learning for students. These teaching methods include, but are not limited to:

  • Conceptual and theoretical foundations: lectures and classroom presentations enable students to acquire fundamental theoretical knowledge. These sessions are delivered by experts in the field and draw on real-world examples and case studies.
  • Group work: Groupwork enables students to develop their collaboration, communication and problem-solving skills. It also encourages the exchange of ideas and peer learning.
  • Professional scenarios (AI Clinic): Simulations, role-plays and real-world projects enable students to put their knowledge and skills into practice in realistic professional contexts. This helps them develop their analytical skills, creativity and decision-making abilities.
  • Flipped classrooms: In flipped classrooms, students prepare assignments, case studies and research work before the lesson. In class, they discuss, share their ideas and work together to deepen their understanding and solve problems. This approach promotes active, student-centred learning.

Other teaching methods

In addition to the teaching methods mentioned above, other approaches may be used depending on the specific needs of the programme and the learning objectives. These approaches may include guest lectures, workshops, company visits and research projects.

Choice of teaching methods

The choice of teaching methods will be guided by the programme’s educational objectives, the characteristics of the learners and the available resources. The aim is to create a dynamic and stimulating learning environment that fosters the acquisition of the skills and knowledge students need to succeed in the field of AI/Data.

The importance of diversity in teaching methods

The use of a variety of teaching methods enables us to cater for students’ different learning styles and keep them engaged in their learning. It also helps to create a richer and more stimulating learning environment that fosters critical thinking, creativity and problem-solving.