Artificial intelligence (AI) currently has its active role in several fields. As being among those fields, education may benefit from AI as it holds potential for boosting teaching and learning practices in education offering new opportunities such as differentiated instruction, feedback, innovative approaches (Luckin et al., 2022), personalised content and curriculum (Chen, Chen & Lin, 2020). English language teaching, which uses a combination of different methods and techniques highlighting the communicational skills and cultural understanding (Wahyudin et al., 2024), may benefit from AI widely. Still, integrating AI-based tools into education may lead to such concerns as the lack of teachers’ AI readiness. The concept of AI readiness is described as the transition “from not understanding what AI is and what AI can do, to being able to understand, in non-technical terms, what AI is capable of achieving” (Luckin et al., 2022, p. 1). Literature poses important gaps for AI integration into education from the dimension of teachers’ AI readiness: There is a huge gap in knowledge about AI (Edmett et al., 2023) and an emerging need for training for teachers (Lindner & Berges, 2020; Kohnke, Moorhouse & Zou, 2023). Although studies focusing on the teachers’ AI Readiness do exist, they mostly focus on either in-service teachers or pre-service teachers (Sperling et al., 2024). Only a few studies include both pre- and in-service teachers regarding their AI readiness (e.g. Polly, Martin, & Byker, 2022); but the number is even low especially in the Austrian context.  To well prepare teachers for AI integration in education, both groups should be taken into consideration revealing their needs and perceptions regarding AI. In this regard, creating clusters to reveal a continuum from pre-service to in-service teachers may greatly help to design effective and efficient training programs.

The aim of the study is twofold: to explore both pre-service and in-service English Language teachers in Austria regarding their AI readiness and needs for integrating AI-based tools into education; and to create clusters to reveal a continuum from pre-service to in-service in order to design effective and efficient training programs. Addressing the gaps in literature, this study intends to answer the following research questions:

1. How do pre-service and in-service teachers identify their AI readiness and needs for integrating AI-based tools into education?
   1. What is the current level of English teachers’ AI readiness, in terms of technological, pedagogical, content, and ethical knowledge?
   2. What are the perceptions of pre- and in-service teachers regarding the opportunities, challenges, threats and obstacles towards the integration of AI-based tools into education?

For exploring AI readiness and needs of the teachers, the current study will adopt Technological Pedagogical Content Knowledge (TPACK) framework proposed by (Mishra & Koehler, 2006). It covers the dimensions of content, pedagogical, and technological knowledge; and the ethical dimension is also added by Celik (2023) since it is one of the major concerns in AI. Furthermore, Broadwell’s (1969) Conscious Competence Model (CCM) will be utilised to identify the teacher groups for creating the clusters for the continuum. The model has four stages: 1) unconsciously incompetence, 2) consciously incompetence, 3) consciously competence, and 4) unconsciously competence. 

This study will follow the mixed method sequential explanatory design, consisting of two phases: collecting quantitative data, and then collecting qualitative data (Creswell, 2003). The latter is used for explaining and/or elaborating the former (Ivankova, Creswell & Stick, 2006). Within the framework of the sequential explanatory approach, the study will collect the data in “QUAN → QUAL” way (Edmonds & Kennedy, 2017, p. 196) following one stage after another. The TPACK Scale (Celik, 2023) will be utilised to reach pre-service and in-service English teachers’ AI readiness; and thereafter interviews will be conducted to gather evidence about the teachers’ needs for integrating AI-based tools into education. The qualitative data and the quantitative data will support and justify each other in order to serve the purposes of triangulation and complementation. To enhance the validity of the study, “triangulation seeks convergence, corroboration, correspondence of results from the different methods” (Greene et al., 1989, p. 259). The context of the study covers pre-service teachers studying English language departments, and in-service teachers working in secondary level schools in Austria. For the quantitative phase of the study, the participants will be sampled in convenience sampling where willing and available participants join (Dörnyei, 2007) in order to reach an appropriate sample to supply the necessary amount of data. Later, 8 participants among those will be chosen to conduct the interviews. Their experience in the teaching field and their being pre-service or in-service will be taken into consideration when selecting the participants for interviews. The quantitative data of the study will be collected by using the Intelligent – TPACK Scale developed by Celik (2023). The qualitative data will be collected through semi-structured interviews. After collecting the data, the quantitative data will be analysed by statistical and descriptive analyses; and the qualitative data will utilise thematic and descriptive analyses.

The quantitative and qualitative data collected throughout the study will help to determine the pre-service and in-service English teachers’ AI readiness and their needs regarding the integration of AI-based tools into education. The data will shed light on the both groups’ commonalities and differences to create clusters to reveal a continuum from pre-service to in-service in order to design effective and efficient training programs. The conference presentation will report on the quantitative findings, focusing on the commonalities and differences of the pre- and in-service teachers highlighting their experience in the field.

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