The rapid emergence of Large Language Models (LLMs), such as ChatGPT, Gemini, and Copilot, in the past two years has profoundly changed several aspects of educational practice in schools. Teachers, stakeholders, and governments worldwide (and across Europe) face an unprecedented challenge: effectively implementing the power of artificial intelligence (AI) in education. Several tools have been developed to assess teachers' AI competencies, views, and attitudes (Ferikoğlu and Akgün 2022; Ng et al., 2022; Biagini, G., Cuomo, S. & Raineri (2023); Laupichler et al., 2023b). According to recently published studies, neither teachers nor student teachers are ready to integrate AI into their work. Several doubts and fears exist among teachers regarding the introduction and spread of AI applications in education (Simul et al., 2024). 

These include, for example:

(1) Artificial intelligence taking over the role of teachers (Selwyn, 2019; Gentile et al., 2023); (2) Reducing students' creativity;

(3) Discouraging students from thinking;

(4) Enabling uncritical and unethical use by students (cheating, plagiarism, copyright infringement); and others.

The aims of this research were to investigate and explore teachers' attitudes and views about artificial intelligence and to examine the differences between teachers in schools and younger student teachers in Hungary. (A review of recent literature shows that most European countries are also facing similar challenges in the educational use of artificial intelligence (for example, Chounta, I-A., Bardone, E., Raudsep, A. & Pedaste, M. (2022); Luckin, R., Cukurova, M., Kent, C., du Boulay, B. (2022); Carolus, A., Koch, M., Straka, S., Latoschik, M., & Wienrich, C. (2023)).

The main research questions were as follows:

1. What factors influence the level of teachers’ AI competence? Are there any gender differences in this regard and in views on AI?
2. Is the SNAIL questionnaire we selected suitable for measuring teachers' AI competence?
3. How does teachers' prior professional experience influence their views on AI?

The research in this paper is based on quantitative data gathered from Hungarian teachers in January 2024 and from Hungarian teacher students in April–May 2024.

Data were collected through an online questionnaire survey conducted with a convenience sample of teachers and teacher students who participated anonymously and voluntarily. The sample comprised 201 teachers, of whom 74.6% were female, 24.9% male, and 0.05% did not state their gender. The mean age of the participants was 50.36 years (SD = 10.035), with an average of 24.5 years (SD = 11.81) of teaching experience. The youngest participant was 24 years old, and the oldest was 77 years old.

The teacher-student sample consisted of 238 students, 77% of whom were female. The mean age of the participants was 20.35 years (SD = 1.68).

Based on a comprehensive review of recent literature, we selected the SNAIL (“Scale for the Assessment of Non-Experts’ AI Literacy”) questionnaire (Laupichler et al., 2023a; 2023b) as the most suitable tool for our investigation. (Several frameworks, models, and questionnaires also exist (Ferikoğlu, D., & Akgün, E. (2022); Wang et al. (2023); Simul et al. (2024)). The survey tool consisted of three sections: eight demographic questions, eight questions about beliefs and perspectives, and 30 ten-point Likert scale items adapted from the SNAIL questionnaire, with one item removed after consultation with Hungarian educational experts. The survey took approximately 15–20 minutes to complete. We performed an exploratory factor analysis (EFA) to better understand the findings and assess the questionnaire's construct validity. EFA was used to examine the underlying structure of the questionnaire and identify the interrelationships among the items. To evaluate the questionnaire's internal consistency, a reliability analysis was conducted using methods such as Cronbach's Alpha and/or McDonald's Omega. In both cases, we obtained reasonable results. It is important to acknowledge some potential limitations that emerged in our analysis. The relatively small sample size for the exploratory factor analysis (according to the literature) warrants careful consideration, as a larger sample size of more than 300 participants would be ideal. However, the high values of the KMO and other relevant indices suggest that the exploratory factor analysis may still be reliable and valid. Additionally, one of the factors had only two components, which requires further consideration for better adaptations. The detailed results of the two surveys will be showcased at the conference.

The results of this study reinforce our preliminary assumptions that there is an urgent need to design and implement a program to improve teachers' and student teachers' knowledge about AI. Although the Hungarian government mentioned an educational action plan called "AI Challenge" in the AI Strategy of Hungary (2021), it is far from meeting the needs of the educational community (teachers, students, and parents). A more detailed analysis of the research reveals significant differences between genders and age groups, as well as gaps between school teachers and university student teachers. At the same time, it can serve as a foundation for more precisely defining the objectives of further training programs and developing their professional and pedagogical content. In summary, our preliminary findings suggest that the SNAIL questionnaire is an appropriate and suitable tool for investigating teachers' views and attitudes toward AI. The results of our study also underscore the importance of educating and empowering teachers and student teachers with relevant knowledge about AI.

Biagini, G., Cuomo, S. & Raineri (2023). Developing and Validating a Multidimensional AI Literacy Questionnaire: Operationalising AI Literacy for Higher Education. In Schicchi, D., Taibi, D. & Temperini, M. (Eds.), AIxEDU 2023 High-performance Artificial Intelligence Systems in Education. Proceedings of the First International Workshop on High-performance Artificial Intelligence Systems in Education co-located with 22nd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023) Carolus, A., Koch, M., Straka, S., Latoschik, M., & Wienrich, C. (2023). MAILS—Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change and meta-competencies. Computers in Human Behavior: Artificial Humans, 1. doi: 10.1016/j.chbah.2023.100014 Chounta, I-A., Bardone, E., Raudsep, A., & Pedaste, M. (2022). Exploring Teachers’ Perceptions of Artificial Intelligence as a Tool to Support their Practice in Estonian K-12 Education. International Journal of Artificial Intelligence in Education, 32 (3) 725–755. doi: 10.1007/s40593-021-00243-5 Ferikoğlu, D., & Akgün, E. (2022). An Investigation of Teachers’ Artificial Intelligence Awareness: A Scale Development Study. Malaysian Online Journal of Educational Technology, 10(3). 215–231. doi: 10.52380/mojet.2022.10.3.407 Gentile, M., Cittá, G., Perna, S. & Allegra, M. (2023). Do we still need teachers? Navigating the paradigm shift of the teacher's role in the AI era. Frontiers of Education, Section Digital Learning Innovations, 8 doi.: 10.3389/feduc.2023.1161777 Laupichler, M. C., Aster, A. & Raupach, T. (2023a). Delphi study for the development and preliminary validation of an item set for the assessment of non-experts' AI literacy. Computers and Education: Artificial Intelligence, 4. ISSN 2666-920X. doi: 10.1016/j.caeai.2023.100126. Laupichler, M. C., Aster, A., Haverkamp, N. & Raupach, T. (2023b). Development of the “Scale for the Assessment of Non-Experts’ AI Literacy”—An Exploratory Factor Analysis. Computers in Human Behavior Reports, 12 (1). doi: 10.1016/j.chbr.2023.100338 Luckin, R., Cukurova, M., Kent, C., du Boulay, B. (2022). Empowering educators to be AI-ready. Computers and Education: Artificial Intelligence, 3 100076, ISSN 2666-920X, doi: 10.1016/j.caeai.2022.100076. Selwyn, N. (2019). Should robots replace teachers? AI and the Future of Education. (1st ed.) Polity Press. Simuț, R., Simuț, C., Bădulescu, D. & Bădulescu, A. (2024): Artificial intelligence and the modelling of teachers' competencies, Amfiteatru Economic Journal, ISSN 2247-9104 26 (65), 181-200. doi: 10.24818/EA/2024/65/181. Wang, X., Li, L., Tan, S. E., Yang, L., & Lei, J. (2023). Preparing for AI-enhanced education: Conceptualizing and empirically examining teachers’ AI readiness, Computers in Human Behavior, 146, 107798, ISSN 0747-5632, doi: 10.1016/j.chb.2023.107798.