Digitalization is a keyword in the discourse of educational science, but it is often linked to technological challenges, although digital changes occur throughout society. Therefore, STEM teachers are required to cope with technological changes in the subject, the increasing and diverse education and training technologies, and the ever-changing paths of information and communication of adolescents in their role as members of a changing society and culture. The TPACK-model focuses educators’ professional knowledge based on teachers’ expertise concerning technological knowledge per se and the pedagogy and content of their subjects. In contrast, knowledge relevant to daily life and social and cultural interaction beyond this is not clearly included in the TPACK-model at present. This article proposes supplementing the TPACK-model with the knowledge components of digital cultural transformations (digitality) and, therefore, extending the TPACK-model to a DPACK-model, where D stands for digitality. Therefore, digital transformation in STEM teaching requires additional professional knowledge considering the transformation of communication, mediatization and society. Through this expansion, the focus should also be directed on the necessity that children and young people in the digitally shaped world must also be able to critically reflect on the processes of change and shape them in an ethically responsible manner. For this reason, teachers require professional knowledge to reflect, analyze, use and shape the digital transformation, which is regularly demanded of them by national and international educational standards. As a foundation of STEM teachers’ education and training, an integrated model combining these facets of knowledge and skills is provided for discussion, and, as a result, quickly found its way into the educational policy guidelines and educational science discourses in Germany. In order to integrate the sociocultural consequences of digitalization into TPACK, this paper proposes a new hemisphere, sociocultural knowledge, which extends the existing TPACK components.

Research on AI systems like ChatGPT indicates that there are not only obstacles but also potentials of using AI systems on the learner-level, e.g., for fostering learning processes. On the teacher-level, however, there is not much research yet and an important possibility to use AI systems like ChatGPT could be for, e.g., the development of complex tasks, which could help teachers in lesson planning processes. This study provides explorative and qualitative insights into such a task development process with the help of ChatGPT and for that purpose, n = 17 prospective teachers’ interactions with ChatGPT and post-interaction reflections were examined. They used ChatGPT with the assignment to develop complex tasks with the specific subject-matter parameters ‘Operative principle’, ‘Geoboard’ and ‘Multilingualism’. Methodologically, interpretative interaction analyses are conducted to gain abductive insights into the transcribed data and the analysis indicates that teachers, who were trained in the specific subject-matter as well as in prompt engineering, could use ChatGPT in a way that highly viable tasks could be generated whereas non-trained teachers mostly generated non- or less-viable tasks. This leads to the assumption of a threshold for the viable usage on teacher-level: Teachers who are subject-matter- and content-wise proficient and who know about effective prompting can use ChatGPT efficiently and evaluate it as a good tool for helping them in developing tasks they would otherwise not be able to develop, e.g., multilingual tasks, whereas teachers with less proficiency in both facets do not evaluate the usage of AI as useful. These results influence teacher training programs and the future development of AI tools for educational purposes in a two-fold way. Since teachers who know both how to prompt and what to teach are more capable of using AI systems like ChatGPT productively as support in developing learning environments, future teacher training programs should include prompting modules as part of their structure, e.g., following subject-matter training modules. This approach would help prospective teachers learn how to use AI systems like ChatGPT effectively and integrate these systems into their practices, e.g., for developing tasks.