Abstract:

The increasing importance of artificial intelligence (AI) in vocational education and training has been the subject of heated debates for several years. The discussion often primarily revolves around how AI-driven digital transformation is changing work and business models, and what implications these changes have for the skills and qualification requirements of (future) professionals. However, another critical factor in vocational education is the teaching staff, who are often somewhat neglected in the current discourse. The question arises as to how both prospective (pre-service) and current (in-service) teachers at vocational schools, as well as educators in companies, can be prepared for the changing world of learning and work.

The integration of AI-related content into the university education of prospective vocational educators is currently characterized by individual initiatives and projects. There is still a lack of comprehensive curricular integration.

This contribution presents a seminar concept designed to integrate AI content into the university education of prospective educators in the field of vocational training. The seminar aims to bridge the gap between initial and continuing education and focuses on both pre-service educators (directly) and in-service educators (indirectly). Using an interdisciplinary approach, students of business and economics education, business informatics, industrial engineering, and business administration work together on the complex question of how in-service educators in various disciplines can be trained to competently address the challenges arising from AI-driven digital transformation in vocational education. Using Open Educational Resources (OER), the students first learn about the fundamentals of AI and its potential applications, opportunities, and risks in education. From an educational perspective, students evaluate the extent to which the OER materials are suitable for continuing education concepts. Building on this, they develop said continuing education concepts in interdisciplinary teams for specific target groups of in-service educators. In doing so, they draw on the diverse subject-matter expertise of the individual team members. The seminar has already been conducted twice in this format and has been very popular among students. This contribution is intended to serve as a basis for transferring the approach to other disciplines in teacher education.

The seminar is accompanied by empirical research: Since the participants have had little to no formal training on the topic of AI to this point, it is difficult to draw reliable conclusions about the students’ knowledge, experiences, and attitudes toward AI. This contribution therefore examines how students assess their own AI competencies, what attitudes they hold, and what experiences they have already gained in using various AI applications. The data indicates that students tend to have a positive attitude toward AI, yet their knowledge and skills in this area still have room for improvement. Since students’ own knowledge and attitudes can serve as an indicator for future use, this data is considered highly relevant. Furthermore, the data provides insights for designing university courses tailored to the needs of the target audience. The contribution also discusses implications for the initial and continuing education and therefore the professionalization of pre-service and in-service educators in vocational education.

Keywords:

Vocational Education, Teacher Training, Artificial Intelligence, Higher Education.