In recent years, significant progress has been made in the development of generative AI-based tools, driven by improved computing power that enables the training of large language models (LLMs) with multiple terabytes of text. This has led to the emergence of free and commercial applications accessible to the general public, such as OpenAI’s chatbot ChatGPT, integrated LLM-based tools like GitHub Copilot, and AI-driven learning feedback systems such as AI Fiete.

As a result, AI-based tools are already being used for learning by students and for teaching by educators in schools and universities. In order to leverage the learning through AI-based tools in a didactically meaningful way and to be able to competently use AI tools for planning, conducting, and reflecting on teaching, it is important that both students and teachers acquire the necessary competences. This paper focuses on fostering AI competence in future vocational teachers specializing in the fields of mechanical, electrical, and vehicle engineering.

Based on the authors' teaching experience with students from these specific fields, as well as a cross-disciplinary survey of all teacher education students at anonymous University, it is known that students frequently use AI tools for self-study and exam preparation. They perceive these tools as an opportunity for learning, although they still place greater trust in human instructors than in AI. In an action research project, the authors examined how the use of AI tools in a subject-specific didactics course on lesson planning influences teacher education students’ intentions to use AI tools in their future teaching in vocational schools, and how these intentions evolve over time.

The study design is based on selected determinants model of the Unified Theory of Acceptance and Use of Technology (UTAUT) and the AI Acceptance Model (KIAM), which were integrated into a single model. To measure the indicators of usage intention the Receptivity to Instructional Feedback questionnaire, and additional self-defined scales were used at the beginning of the course (pre-test) and at the final session (post-test). Additionally, results from reflections during the final session were incorporated into the study.

The seminar was attended by five students in the first session and six students in the final session. Due to the small sample size, qualitative results were primarily analysed and supplemented with quantitative data wherever possible. From the teacher’s perspective, students viewed AI-generated feedback critically, as positive feedback often depends only on the presence of certain keywords in the learner’s response, with little consideration for contextual knowledge or argumentation strategies. However, students also expect this limitation to improve in the future and generally perceive individualized feedback positively, expressing a willingness to integrate it into their future teaching. Furthermore, students anticipate that their future vocational school students will have intrinsic motivation for AI—stemming from curiosity, enjoyment of AI usage, and a sense of self-efficacy with AI.

Keywords: Vocational Education, AI competence, AI Feedback, Generative AI, Lesson planning with AI.