Introducing Artificial Intelligence to Vocational Schools in Europe

Thursday, February 18, 2021

MCAST, together with a number of vocational schools in Europe, is currently developing a number of courses related to Artificial Intelligence (AI) where the main goal is to develop AI skills of ICT teachers and pupils in the vocational educational and training (VET) sector. The project, titled ‘Introducing Artificial Intelligence to Vocational Schools in Europe’ (No. 2020-1-LT01-KA202-078015) and co-funded by the European Commission under Erasmus+ KA2 program, aims to:

  1. Develop lesson materials for an innovative study unit “Introduction to Artificial Intelligence” - a comprehensive course providing knowledge of machine learning processes for target group students.
  2. Integrate the study unit into each partner country’s formal education system, thus enabling the sustainability of the project’s results and ensuring the incorporation of AI qualifications into national and European VET systems.
  3. Provide competence development for ICT teachers, as key actors in the acquisition and transference of new digital skills.
  4. Disseminate the project’s intellectual output across VET and other educational sectors in and outside of partner countries.

This course mainly targets students involved in ICT-related study programmes who already gained basic programming skills. In order to respond to the demand of AI talent in the European labour market, this course also targets ICT teachers who currently teach at VET schools.

Courses Outline

 A total of three courses are currently being developed by MCAST and two other schools. The courses include: 

  1. Autonomous Driving with Computer Vision (20 hours)
  2. Machine learning agents in Game Design (20 hours)
  3. Robotic arm and Computer Vision (20 hours)

In the first course, an introduction to AI is given. The course aims at introducing the learners to computer vision and how it can be used to teach a car to ‘drive itself’ (autonomous driving). Important artificial intelligence techniques such as Convolutional Neural Networks are covered in this subject. In this course, the students are expected to train a model that is able to detect lanes, other cars, safety stops and other traffic signs.

In the second topic, the learners are introduced to reinforcement learning and how they can ‘teach’ an agent to intelligently perform ‘good’ moves in a computer game. The students are then introduced to Unity, which is a cross-platform gaming engine, to develop intelligent machine learning (ML) agents.

Finally, the last course aims to cover other important machine learning techniques and how a robotic arm can be programmed to interact with real-world objects (such as sorting LEGO parts). In this course, the students will learn how to build a model that detects different parts using a camera. Afterwards, they are expected to interface with a robotic arm and a conveyor belt to create an autonomous machine that is able to detect and sort real-world parts.

Conclusion

The project is currently in the course development phase whereby each partner is researching and developing the course material. The courses will be delivered to VET schools in Europe later this year.

For further information please address your email to thomas.gatt@mcast.edu.mt.

Article and image provided by MCAST. 

 

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