4.1 Introduction to Module 4
Module 4 focuses on AI, vocational education and new skills requirements. This includes both the skills needed by vocational education and training students for working with AI and the skills and competence needed by teachers and trainers.
Unit 4.2 – Using AI and Big Data to Find out about New Skill Requirements – looks at how AI can be used to examine job adverts to find out skill needs. It suggests this approach may also be useful for students to find what more about what they need to learn. As the training sector is increasingly heavily influenced by AI, it is also being used for worker recruitment – for example searching and analyzing interviews.
Unit 4.3 asks what are the new skills needed for the AI and automation? These include, it says demand for advanced technological skills, increasing demand for key skills and competencies: social, emotional, and higher cognitive skills, such as creativity, critical thinking, and complex information processing, basic digital skills. While demand for physical and manual skills will decline it still will remain the single largest category of workforce skills in 2030 in many countries. Advanced IT skills and programming alongside complex information processing skills will also see a surge in demand.
The application and development of AI based technologies challenge the traditional boundaries of disciplines, knowledge and competence areas. Unit 4.4 looks at this issue with the decentralized intelligence linked to Industry 4.0 leading to an increased availability of data that is highly process-relevant to skilled workers. Simply amending occupational profiles will not be enough in the light of a massive amendment of process orientation in occupational profiles.
This leads to unit 4.6 on updating existing qualifications. The existing processes of awarding of qualifications will be challenged by using AI to decentralise these processes and reduce the role of humans and formal institutions. One example is the use of blockchain technologies in the assessment and recognition of competences and awarding of qualifications. The growing role of microcredentials is a good illustration of such trends.
Unit 4.9 asks how can AI and automation be integrated in school based VET programmes?
Previous modules have already explored the impact of AI on the world of work. But how can AI and automation be taken up in VET programmes in schools?
As is often the case, there is not one perfect solution. The methods and approaches depend on the VET system in different countries, but also on the subjects being taught and the professions being trained for.
Unit 4.10 follows this with an example from a German VET school. The title of the project was: “Deep Reinforcement Learning – Preparation of the topic ‘artificial intelligence’ and implementation of an agent in the game Sonic the Hedgehog”.
The task was to program a self-learning artificial agent in a computer game. The project was planned, implemented, and documented largely independently by a group of four students.
In an interview, the supervising teacher, explains more about the project context. Wilfried Meiners recommends that the students have a say in the selection of the topic. Students need motivation and perseverance to work in project groups, so it is an advantage if the project tasks are linked to the students’ interests
Unit 4.11 asks how can learning about AI and automation be integrated into work based learning programmes?
The goal of workplace-based vocational education and training is met in Germany through the dual vocational education and training system. The key element is cooperation between different learning locations. In practice, this is often more difficult than expected, as can be heard in part in the short video. However, cooperation and the exchange associated with it will in all likelihood continue to gain in importance. This requires the commitment and efforts of both schools and companies.
A further case study is the focus of unit 4.12 – Smart factory project. It looks at how besides knowledge about AI and programming skills, students can learn social and personal competences: working in a team, being creative and handling frustration.
Mechatronics trainees from a vocational school in Bremen, Germany, took part in a project on the topic of Industry 4.0 in cooperation with the future workshop of the Mercedes-Benz factory in the city. The apprentices produced a model of a Smart Factory, and built a robot car for an Erasmus+ project and filmed the production process.
We ask the teacher about his experiences, challenges and tips for other teachers.
In unit 4.13, asking how can flexible delivery ensure everyone has access to learning opportunities, we look again at the example of DuoLingo. This includes the use of AI for gamification. Other examples include the Babel and Memorise applications. AI is also increasingly being used in Massive Open Online Courses (MOOCs). The use of AI for analysing data from such programmes can improve understanding of learning leading to better courses.
Unit 4.14 focuses on the European Digcompedu Framework and the competences required for teachers and trainers. DigCompEdu sets out a common framework for educators with the aim of leading to regional and national training programmes. It covers all education levels and includes self-assessment schemes in six areas with twenty-two competences.
Unit 4.15 asks what new competences do teachers and trainers need for teaching about AI and automation? It is pointed out that for vocational education and training this includes competences related to both the changing world of work and the use of AI for teaching and learning
To counter the increased workload for teachers it is suggested that there needs further cooperation between teachers and trainers from different institutions and companies.