Identifying challenges and best practices for implementing AI additional qualifications in vocational and continuing education: a mixed methods analysis

As artificial intelligence (AI) increasingly integrates into various industries and educational sectors, there is a growing need for an AI-ready workforce. This study, focusing on a novel AI additional qualification in Germany, investigates the underexplored challenges and best practices regarding the learning software, the learning contents, and the didactic design. Addressing the research question 'What are the teaching and learning experiences in AI additional qualifications, and to what extent do these experiences align?', we adopted a mixed methods analysis. This entailed interviews with four educators and self-reported questionnaires with 73 learners, triangulating both groups' findings for comprehensive insights. Key outcomes from this study demonstrate a significant alignment in teaching and learning experiences, particularly in terms of shared challenges. The findings significantly contribute to AI curriculum development and continuing vocational education and training practice. They underscore the necessity for robust and visually engaging learning software, as well as learning content that primarily covers AI basics with practice-oriented examples. Another best practice emerges for blended learning formats, favouring a greater emphasis on face-to-face instruction complemented by teacher-supported self-directed learning. Future research should focus on balancing theoretical and practical knowledge in AI curricula, preparing teachers for AI content delivery, and optimising teacher-learner interactions.