Assessment Methods

Students’ learning outcomes in this programme will be assessed using a variety of methods, enabling their skills to be evaluated comprehensively and accurately, in accordance with the requirements of the degree or professional qualification awarded. These methods include, but are not limited to:

  • Case studies: Students will be required to analyse complex real-world situations and propose innovative solutions.
  • Exams and written assignments will assess students’ understanding of theoretical concepts and their ability to apply them to practical problems.
  • Individual presentations and group discussions will be used to assess students’ communication, collaboration and critical thinking skills.
  • Immersive simulations will enable students to put their skills into practice in realistic environments.
  • Workplace experience: Projects and work placements will enable students to apply their knowledge and skills in real-world professional contexts.

Other assessment methods

In addition to the assessment methods mentioned above, other approaches may be used depending on the specific needs of the programme and the skills to be assessed. These approaches may include portfolios, self-assessment questionnaires, peer assessments and assessments by industry professionals.

The use of a variety of assessment methods provides a more comprehensive and objective evaluation of students’ learning outcomes. It also takes into account students’ different learning styles and gives them the opportunity to demonstrate their skills in different ways.

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 to 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 encourages 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.