Teachers operate under conditions of increasing professional demands, including intensified workloads and the need to respond to diverse learner needs. In this context, artificial intelligence (AI) is frequently discussed as a potential means of supporting instructional practice. This is congruent with the growing number of teachers who are reporting an increased utilization of AI in the preparation of their lessons (Herm & Hagen, 2025). Nevertheless, the German School Barometer revealed that 62% of respondents expressed feelings of uncertainty or extreme uncertainty regarding the utilization of AI, and 31% reported not using AI at all (Herm & Hagen, 2025). These findings indicate a substantial lack of professional preparedness, underscoring the need to systematically address AI-related competencies in teacher training. Consequently, AI must become an integral component of teacher training, accompanied by empirical research on pre-service teachers’ (PSTs) AI use and the effectiveness of related pedagogical measures.

However, research on the use of AI for subject-specific materials development remains limited, and clear connections between AI literacy and concrete teaching practice are rarely made (Xin, 2024, p. 4). Moreover, studies focusing explicitly on PSTs remain scarce, as research often centers on students or in-service teachers (Huang et al., 2025, p. 2). 

Against this backdrop, this dissertation project aims to investigate the usage of AI among PSTs for English as a foreign language in secondary schools. The project's central theme is the design and analysis of a university seminar (comprising 11 sessions of 90 minutes each) in which generative AI is systematically employed for materials development. The present project examines how PSTs utilize AI in this context, the evaluation criteria they apply, and the changes observed in their practices and evaluative perspectives over the course of the seminar. The analysis examines patterns of interaction and adaptation that emerge in dealing with AI and focuses on how the critical justification of AI-based material revisions develops.

The following research questions are investigated:

• RQ1: How can a theory- and practice-based seminar be designed didactically, in which AI-supported materials development serves as a learning opportunity for the development of AI literacy?
• RQ2: In what ways do PSTs use AI before, during, and after the seminar?
  • RQ2a: What patterns of interaction and adaptation emerge?
  • RQ2b: How does the seminar change the critical assessment of the (pedagogical, linguistic, and ethical) quality of AI outputs, and what criteria determine the revisions? 

The seminar, therefore, combines existing didactic and materials-related theories with AI-specific content. For instance, the program includes sessions on AI algorithms, ethical considerations, and prompt engineering, and their implications for materials development. The theoretical underpinnings of the seminar are provided by the UNESCO AI Competency Framework for Teachers (2024) because it provides clear guidance for the AI content that is to be implemented in teacher training (Bai, 2025, p. 50). Materials development was chosen as the focal area, as it constitutes a recurring and authentic pedagogical practice in which AI-specific learning objectives and curricular guidelines can be operationalized. According to Edmett et al. (2024, p. 31), it is the most popular use of AI among teachers, with 57% of the surveyed teachers reporting that they use AI for this purpose. However, in the same study, more than half of those teachers disagreed or strongly disagreed with the statement 'I have received enough training to incorporate AI into my teaching' (Edmett et al., 2024, p. 36), which points to a clear misalignment between the frequent use of AI in teaching and teachers’ self-reported training experiences.

The seminar’s positioning at the intersection of didactics and AI education introduces a new dimension of professionalization, grounded in the systematic reflection on pedagogically justified AI use.

The present study utilizes a mixed-methods approach. At the beginning and end of the seminar, Ning's AI Literacy Questionnaire is utilized in order to self-assess PSTs’ individual AI literacy levels (Ning et al., 2025). The efficacy of the seminar is determined by evaluating the pre-post changes. Furthermore, three generated materials from each PST, staggered over time (beginning, middle, and end of the seminar), are analyzed. This serves to record their prompt interaction with AI and their continuous adaptation of the material. Throughout the seminar, PSTs record their individual experiences and complete various reflection tasks in reflection portfolios, which are effective tools for observing learning processes (Akleh & A.Wahab, 2020). The focus here is on PSTs’ experiences and applications of AI, with an emphasis on the evolution of these practices throughout the semester. Furthermore, the portfolios will facilitate the visualization of the reflection process, from the initial prompt to the final material, by requiring PSTs to substantiate their thoughts and adjustments. The prompts and reflection portfolios are analyzed and classified through qualitative content analysis. The objective of this triangulation is to unveil patterns of interaction and adaptation in the utilization of AI. The analysis examines which criteria guide the revision and whether the AI output is evaluated differently over the course of the seminar. Design-based research functions as an overarching framework (Reinmann et al., 2024). The implementation of the seminar in the summer semester 2026 (April-July) constitutes the initial phase of the iterative cycle. Following a thorough evaluation and subsequent refinement, the seminar will be offered once more in the winter semester. This process is expected to be repeated two or three times.

This study proposes a novel approach to the integration of generative AI in teacher training. Preliminary studies have demonstrated that the degree to which individuals possess AI literacy, understood as the capacity for critical engagement with AI, can be augmented through university-based interventions (Tzirides et al., 2024, p. 9). The key challenge now lies in transferring these advances into contexts that are relevant to PSTs’ future professional practice. With its focus on the combination of AI with materials development, it is in precisely this context that the present study is situated. The results will contribute to understanding how AI is used by teacher training students of English and how AI can be put to meaningful use. It is reasonable to hypothesize that the development of AI literacy will be accompanied by the emergence of critical thinking, critical distance, and ethical considerations. Moreover, a university seminar concept will be formulated and subjected to rigorous evaluation and testing. Given the utilization of the UNESCO AI Competency Framework for Teachers, a global reference framework for competencies, it is conceivable that this seminar could be transferred to other universities or teacher training settings as well. It might also be possible to transfer the concept to other subjects, especially other foreign languages than English.

AI competency framework for teachers. (2024). UNESCO. https://doi.org/10.54675/ZJTE2084 Akleh, A., & A.Wahab, R. A. (2020). Effectiveness of Course Portfolio in Improving Course Quality at Higher Education. International Journal of Higher Education, 9(3), 39. https://doi.org/10.5430/ijhe.v9n3p39 Bai, J. (2025). Overview and summary of AI competency framework for teachers. Global Medical Education, 2(1), 47–51. https://doi.org/10.1515/gme-2024-0029 Edmett, A., Ichaporia, N., Crompton, H., & Crichton, R. (with Lightfoot, A.). (2024). Artificial intelligence and English language teaching: Preparing for the future (Version Second edition) [Application/pdf]. British Council. https://doi.org/10.57884/78EA-3C69 Herm, M., & Hagen, C. (2025, June 25). Lehrkräfte sorgen sich um die Kompetenzen der Generation ChatGPT. Robert Bosch Stiftung. https://www.bosch-stiftung.de/de/storys/lehrkraefte-sorgen-sich-um-die-kompetenzen-der-generation-chatgpt-0 Huang, T., Wu, C., & Wu, M. (2025). Developing pre-service language teachers’ GenAI literacy: An interventional study in an English language teacher education course. Discover Artificial Intelligence, 5(1), 163. https://doi.org/10.1007/s44163-025-00435-1 Kohnke, L., Zou, D., Ou, A. W., & Gu, M. M. (2025). Preparing future educators for AI-enhanced classrooms: Insights into AI literacy and integration. Computers and Education: Artificial Intelligence, 8, 100398. https://doi.org/10.1016/j.caeai.2025.100398 Manto, A. M., Señedo, G. E., Jauculan, F. G. E., Giangan, M. S. Q., & Alcantara, G. A. (2025). AI Literacy Among Future Math Educators: The Mediating Role of Digital Literacy in Mathematics Teaching. International Journal of Learning, Teaching and Educational Research, 24(7), 753–775. https://doi.org/10.26803/ijlter.24.7.37 Ning, Y., Zhang, W., Yao, D., Fang, B., Xu, B., & Wijaya, T. T. (2025). Development and validation of the Artificial Intelligence Literacy Scale for Teachers (AILST). Education and Information Technologies, 30(12), 17769–17803. https://doi.org/10.1007/s10639-025-13347-5 Reinmann, G., Herzberg, D., & Brase, A. (2024). Forschendes Entwerfen: Design-Based Research in der Hochschuldidaktik (1st ed., Vol. 7). transcript Verlag. https://doi.org/10.14361/9783839474242 Tzirides, A. O. (Olnancy), Zapata, G., Kastania, N. P., Saini, A. K., Castro, V., Ismael, S. A., You, Y., Santos, T. A. D., Searsmith, D., O’Brien, C., Cope, B., & Kalantzis, M. (2024). Combining human and artificial intelligence for enhanced AI literacy in higher education. Computers and Education Open, 6, 100184. https://doi.org/10.1016/j.caeo.2024.100184 Xin, J. J. (2024). Investigating EFL teachers’ use of generative AI to develop reading materials: A practice and perception study. Language Teaching Research, 13621688241303321. https://doi.org/10.1177/13621688241303321 Yong-In University, & Van Horn, K. (2024). ChatGPT in English Language Learning: Exploring Perceptions and Promoting Autonomy in a University EFL Context. Teaching English as a Second or Foreign Language--TESL-EJ, 28(1). https://doi.org/10.55593/ej.28109a8