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Artificial Intelligence and Digital Skills


Admission requirements

Students need to be registered for the minor AI and Society to follow this course.
Students of all faculties can register for the minor.


The best way to get familiar with a technology is to work with it rather than to talk about it. For this reason, this course provides a hands-on approach towards AI, aimed at equipping students with digital skills needed to understand and work with AI technologies. This course lets students experience with coding and developing simple algorithms - in the sphere of natural language processing. During hands-on sessions and "class adventures" students experience how the design and development of these technologies is structures, and they will benefit from working in multidisciplinary teams. Typical evaluation considerations, such as accuracy, different forms of bias and ways of addressing them are discussed. Students will learn in interaction from each other how different perspectives can be cast on technology development.

Depending on group size, the class may be split for some of the lab-sessions.

Course objectives

Objectives of the course

This project-oriented course aims to let students develop hands-on experience with text-based AI systems, to create insights on strengths and limitations, to connect those technical insights to societal impacts. At the same time, students should get some feeling for the platforms, processes, resources etc. involved in the production of AI-systems.

The course will contain group work. By combining different disciplinary backgrounds in each of the project teams, the course aims to foster interdisciplinary communication and understanding of technical and societal aspects of AI.

Achievement levels
The following achievement levels apply with regard to the course:

  • Students with no background in digital skills learn simple coding and reading (pseudo)code

  • Students with digital skills practice translating technical concepts to general audiences

  • Students can explain their disciplinary perspective on technical and societal aspects of AI to students from other disciplines

  • Students can combine different disciplinary perspectives on technical and societal aspects of AI

  • Students get a practical understanding of simple algorithmic decision tools and their societal implications


Check MyTimetable.

Mode of instruction


  • Number of (2 hour) lectures: 2

  • Names of lecturers: F. Dechesne, Masha Medvedeva,

  • Required preparation by students: see Brightspace

Other methods of instruction

  • Description: Lab sessions and final presentations

  • Number of (2 hour) instructions: 8-10

  • Names of instructors: Mathijs Westera (HUM), Friso Selten (FGGA), Masha Medvedeva (FdR), dr. Francien Dechesne (FdR), Jelena Prokic, to be confirmed

  • Required preparation by students: see Brightspace

Assessment method

Examination form(s)

  • Reflective journals (pass/fail)

  • In term assignments (100%) (including group work)

The reflective journals are mandatory to participate in the final group assignment.

Submission procedures

Areas to be tested within the exam
The material relevant to the examination elements consists of the required reading (literature) and practical exercises for the course, the course information guide and the subjects taught in the lectures, the seminars and all other instructions which are part of the course.

Reading list

Obligatory course materials See Brightspace.


Registration for courses and exams takes place via MyStudymap. If you do not have access to MyStudymap (guest students), look here (under the Law-tab) for more information on the registration procedure in your situation.


  • Coordinator: Dr. Masha Medvedeva

  • Work address: Kamerlingh Onnes Building, Steenschuur 25

  • Contact information:

  • Telephone number: +31 71 527 2727

  • Email:


  • Institute: Metajuridica

  • Department: eLaw

  • Telephone number secretary: 071 – 527 8838

  • Email: