AI Educational Overview

The way we do science is changing rapidly, as are the ways in which we engineer systems and services inside and outside academia.  Artificial Intelligence, data and the digitalisation of all aspects of life are driving such changes, and today’s students can expect to encounter all sorts of AI challenges in their later careers. Working with AI involves both knowing different techniques in the field and being able to apply these techniques in various engineering disciplines.  As the technology becomes more and more engrained in society, it increasingly faces and operates within complex environments and contexts. This makes providing society with skilled AI professionals a priority.

Curricular Education

The TU Delft educational programme in subjects related to AI, data & digitalisation is shaped by current developments and combines fundamental technology with domain-specific challenges. The subjects are strongly embedded in Bachelor Computer Science and Engineering, Master Computer Science (including the “Artificial Intelligence Technology” and “Data Science Technology” tracks) and Master Robotics. In addition, we offer Minor Robotics and Minor Computer Science – with a strong focus on data analytics and software – and a new Minor Engineering with AI. Numerous courses and modules also focus on AI, data and digitalisation in Bachelor and Master programmes across the different faculties.

The offering of AI, data and digitalisation education at bachelor and master level will be extended with a significant offering of ‘with-AI’ and ‘in-AI’ education via electives and master blocks (from September 2022). The following examples of electives courses are already developed and open to all TU Delft students:  

Non-curricular Education

TU Delft invests in offering non-curricular education in AI, data and digitalisation, with activities such as:

  • Developing courses and materials for alumni and professionals to continue their education. These prepare participants by reskilling and upskilling towards AI, data & digitalisation expertise, which will be required more and more in the labour market.
  • Developing a PhD-training programme to provide additional depth and breadth to participants’ knowledge base, and to help staff to connect their knowledge on education ‘in’ and ‘with’ AI, sharing opportunities and challenges. 
  • Developing a teach-the-teacher programme to support teaching staff in their development, the role of AI in their domain, and how to support students in their own domains with AI.
  • Introducing a repository of shared open educational resources to make it easier to use each other’s materials in education. A new learning community will be formed around these materials.