The digital age has been upon us since 1989 (Stengel, 2017). Still, adapting to it is a continuous challenge across European countries (European Commission, 2021). In Germany, about one-third of 8^th graders only show rudimentary information and communications technology (ICT) competence levels (Eickelmann et al., 2019). German pre-service teachers hold less favorable attitudes than students from other programs (Schmid et al., 2017) and most pre-service teachers do not meet the basic requirements of ICT competence levels defined by experts (Senkbeil et al., 2020). Even though respective German experts largely share a consensus about the importance of empowering teachers professionally with digital competencies (vbw, 2017; SWK, 2022), 20% of higher education curricula do not consider digital competencies (Monitor Lehrerbildung, 2022).

AI has elevated the digital competence demands for effective teaching, as new frameworks like UNESCO's AI Competency Framework for Teachers (2024), the DigCompEdu supplement (Bekiardis, 2024), and AIComp (Ehlers et al., 2023) call for urgent adoption in the European research context. As an elaborate digital technology, basic digital competence is still a central premise for competent AI use. However, AI also revolutionizes and facilitates basic digital operations. Therefore, it provides opportunities to accelerate closure of the digital gap. To seize these opportunities and create new ones, it is important to determine pre-service teachers’ current state of adaptation to conceptualize effective steps to take. Therefore, this contribution provides empirical evidence for this purpose as its main objective. In more detail, the following research questions will be addressed:

1. How competent do pre-service teachers believe they are regarding their use of digital technology for teaching and learning?
2. What are pre-service teachers’ attitudes towards using AI for teaching and learning purposes?
3. To what extent do pre-service teachers use AI for different aspects of teaching and learning?
4. How competent do pre-service teachers believe they are regarding their use  AI for teaching and learning purposes?
5. How do pre-service teachers’ competence beliefs regarding the use of digital technology for teaching and learning and their competence beliefs regarding different aspects of AI use relate to each other?

As recent studies explore pre-service teachers’ attitudes and concerns for effective AI integration in education, they highlight the importance of the subject. Zhang et al. (2023) found that perceived usefulness and ease of use were primary factors influencing German pre-service teachers' intention to use AI-based applications. Guggemos et al. (2024) identified three distinct profiles of prospective teachers based on their attitudes towards ethical principles of AI use, with the main difference being their belief in AI's power over teachers and students. While these studies focused on Germany, research in other countries has shown similar trends. Nyaaba et al. (2024) reported generally positive attitudes towards AI among Ghanaian pre-service teachers, who use it as a learning buddy and teaching assistant.

To examine the research questions and to contribute to the main objective, data from a pre-post-cohort-study design is used. Pre-service teachers in Germany undergo an 18-month, practice-oriented second phase of teacher education, managed by the ministries of education, following their M.Ed. as final preparation for school teaching. The data was conducted as part of a funded project that spans from January 2024 to August 2025. It is structured in three cohorts who started their second phase 01.08.2023, 15.01.2024 and 01.08.2024. As of now, 918 pre-service teachers for different types of school from all over Rhineland-Palatinate participated in the pre-measurement, from which we will mainly derive our insights. They are 27.82 ± 3.65 years old, 75.56% female. 60.55% will teach primary school and the rest will teach secondary school. All the instruments used are based on a five-point Likert scale. The calculated Cronbach’s-alpha for every used scale is at least acceptable (< .7). For competence beliefs regarding the use of digital technology for teaching and learning, the instrument of Quast et al. (2023) is utilized, which measures aspects of the DigCompEdu by Redecker (2017). For attitudes, the perception of utility, interest, importance, cost and concerns of AI technology for teaching and learning purposes was conducted. Except for the concerns, the instrument is an adaptation of the digital attitude scales for teaching and learning by Rubach & Lazarides (2019). For the use of AI in teaching and learning context, we developed an instrument with items for potential purposes, i.e., to prepare lesson content, generate lesson plans, create teaching materials and assignments, and lesson sequencing. Additionally, we investigate whether they consider students' use of AI tools in lesson planning and if they ensure if their assignments regularly require the use of digital tools or AI. For competence beliefs, we created three items to rate the perception of their beliefs about different tools, prompting, and critical reflection of opportunities and risks concerning the use of AI. The preliminary analyses are based on descriptive calculations such as means, standard deviations and correlations. More refined analyses and interpretations will be explored and prepared until the presentation.

Preliminary analyses of available data regarding digital competence align with those observed in comparable samples (Quast, 2023). Overall, participants perceived their digital competence as moderate. While they expressed relatively higher confidence in areas such as administrative tasks, professional development, and lesson planning, their self-assessments were less positive when it came to evaluating student progress and handling privacy or legal concerns. Beyond digital competence, pre-service teachers showed a nuanced perspective on AI in education. They generally recognized its importance and saw potential for reducing workload, but their enthusiasm for integrating AI into teaching was more varied. Similarly, opinions on whether AI could enhance learning outcomes were mixed, while concerns about its impact on student effort and critical thinking were notable. The use of AI for teaching tasks remained rather limited, with consistently low reported engagement across various applications, including lesson planning, material development, and sequencing of instructional content. Confidence in AI-related competencies also varied. Participants felt relatively assured in their ability to critically assess AI's risks and opportunities but were less confident in practical skills such as using AI tools effectively and formulating appropriate prompts, highlighting a need for further training in this area. Moreover, digital competence and AI-related confidence showed a moderate correlation, suggesting some overlap between the two domains. Rather high standard deviations in AI-related constructs (> 0.8) suggest a greater heterogeneity, which is supported by significant differences in school forms across numerous domains. The findings of this contribution will be discussed in detail throughout the presentation, focusing on their practical implications for teacher education and classrooms.

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