Conference Agenda
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10 SES 12 E: Digital Competence, Artificial Intelligence, and Teacher Education
Paper Session | ||
| Presentations | ||
10. Teacher Education Research
Paper Differences in Digital Competence Between Beginning and Advanced Pre-Service Teachers University of Jyväskylä, Finland Presenting Author:The digital landscape is constantly changing, and consequently, needs for digital competence are also evolving. Formal education plays a crucial role in ensuring that everyone has equal opportunities to develop digital competencies. However, ways of using digital technologies in schools are still rather narrow: basic productivity applications and purposes such as presenting and searching for information dominate the use, while more applied and creative use plays a very small role (e.g., Fraillon, 2024). The present study pertains to the Finnish context, where several studies have highlighted gaps in creative and collaborative uses of digital technologies in schools (Fagerlund et al., 2024; Häkkinen et al., 2025; Oinas et al., 2023). Teacher education is in a key role in equipping future teachers for fostering their students’ digital competence and in encouraging them to apply digital technologies in meaningful ways. Pre-service teachers’ (PSTs’) digital competence has been found to be affected both by their own technology-related characteristics, such as attitudes and perceived ease of technology use, and the support of the teacher training institute (Tondeur et al., 2018). While it appears to succeed in offering PSTs opportunities to familiarise themselves with digital technologies (Hämäläinen et al., 2021), more effort needs to be put on ensuring that teacher training contributes to PSTs’ professional digital competence in an authentic and relevant way (e.g., Gudmundsdottir & Hatlevik, 2018). Course modules that specifically focus on digital competence are necessary, but at the same time, it would be important for these competences to be part of teacher training on a broad scale in order to foster their integration into the PSTs’ pedagogical understanding and support experimentation and reflection (e.g., Gudmundsdottir & Hatlevik, 2018; Tondeur et al., 2018). Collaborative ways of learning, connecting theory and practice, as well as teacher educators serving as role models are essential in making the learning of digital competence meaningful (Reisoğlu & Çebi, 2020; Vesisenaho et al., 2024). This study addresses PSTs at two different phases of studies: beginning (first year) and advanced (4th or 5th year). This proposal focuses on the following research questions: 1. How do PSTs evaluate different aspects of a) their digital competence and b) their stance towards digital technologies in education? 2. Do these evaluations differ between beginning and advanced PSTs? We used a questionnaire that was informed by digital competence studies and frameworks such as ICILS (Fraillon et al., 2020), DigComp (Vuorikari et al., 2022), and the Finnish Framework for Digital Competence (EDUFI, n.d.). Data collection took place at the beginning of compulsory course modules on digital competence: “Introduction to Digital Technologies for Learning” for beginning PSTs and “Applying Digital Technologies for Learning” for advanced PSTs. The data were collected at the very beginning of these courses in order to orient the PSTs to the topic while also avoiding recency bias stemming from the course contents. We wanted to gauge their digital competence more generally rather than examine the immediate impact of the digital competence focused course modules. Methodology, Methods, Research Instruments or Sources Used The questionnaire consisted of 33 self-assessment statements about digital competence and 11 statements about the PSTs’ stance related to digital technologies. Responses were recorded using a five-point Likert scale. Background information (e.g., gender, year of birth, earlier experience with digital technology) was also collected. Respondents included 324 PSTs in primary education from a Finnish university: 195 (60.2%) were beginning PSTs (BE) and 129 (39.8%) were advanced (AD). Data were collected in two academic years: from BE in the autumn terms 2023 and 2024, and from AD in the spring terms 2024 and 2025. Altogether 260 participants (80.2%) identified as women, 61 (18.8%) as men, and three (0.9%) as other or preferred not to say. This is a representative gender distribution for Finnish PSTs (Vipunen – Education Statistics Finland, n.d.). Median age was 22 years (BE: Mdn = 20 years; AD: Mdn = 23 years). Data analysis was conducted using SPSS version 30. Based on an exploratory factor analysis (EFA), four composite variables were created for digital competence: “basic use of productivity software” (5 items, α = .754), “basic audiovisual skills” (3 items, α = .749), “safe and responsible use” (6 items, α = .805), and “advanced creative use” (6 items, α = .860). The first two variables represent basic digital skills: using office applications and doing simple image and video editing. Safe and responsible use covers aspects ranging from device and data security to digital literacy and ethical questions. Advanced creative use covers topics such as programming, robotics, animation, game design, and using virtual or augmented reality. EFA was also used to form two composite variables for the respondents’ stance towards digital technologies in education: “importance of teaching digital competence” (3 items, α = .871) and “value of applying digital technology” (4 items, α = .733). The former includes general attitudes towards teaching digital competence at different education levels, and the latter comprises views related to the potential of digital technologies for learning and applying them as a teacher. After creating the composite variables, Mann-Whitney U tests were conducted to compare the digital competence variables and the stance variables between phases of studies and genders. Conclusions, Expected Outcomes or Findings PSTs perceived their competence regarding safe and responsible use as good (M = 4.01, SD = 0.53) and advanced creative use as poor or very poor (M = 1.61, SD = 0.60), with basic use of productivity software (M = 3.54, SD = 0.56) and basic audiovisual skills (M = 2.61, SD = 0.79) falling in between. Mann-Whitney U tests showed that AD scored higher than BE in the basic use of productivity software (z = 5.134, p < .001) and advanced creative use (z = 6.219, p < .001), with no significant differences in safe and responsible use and basic audiovisual skills. Although advanced creative use appears to become slightly more familiar during teacher training, this area should be given particular focus to help future teachers support their students’ competencies. Importance of teaching digital competence (M = 4.63, SD = 0.49) was evaluated higher than the value of applying digital technology (M = 3.73, SD = 0.64). AD scored higher than BE both in the former (z = 2.443, p = .015) and the latter (z = 2.248, p = .025), which may reflect increased understanding of the multifaceted aspects of digital pedagogy as well as growing personal relevance before transitioning from pre-service to in-service teachers. Men scored higher than women in advanced creative use (z = 2.82, p = .005). No significant gender differences were found in any other competence or stance variables. Advanced creative skills may be related to using digital technology in leisure activities which is less common among women (cf., Tondeur et al., 2018). This finding supports the need to strengthen all PSTs’ competence and confidence in the more advanced areas. This work will continue with qualitative data collection where beginning and advanced PSTs reflect on their meaningful learning experiences related to digital competence. References EDUFI (Finnish National Agency for Education) (n.d.). The Finnish Framework for Digital Competence - support to implementation of national core curricula. ePerusteet. https://eperusteet.opintopolku.fi/#/en/digiosaaminen/8706410/tekstikappale/8709071 Fagerlund, J., Leino, K., Niilo-Rämä, M., Puhakka, E., & Markkanen, I. (2024). Kohti digiosaamisen strategista kehittämistä: Kansainvälinen monilukutaidon ja ohjelmoinnillisen ajattelun tutkimus (ICILS 2023). Fraillon, J. (Ed.). (2025). An International Perspective on Digital Literacy: Results from ICILS 2023. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-87722-3 Fraillon, J., Ainley, J., Schulz, W., Friedman, T., & Duckworth, D. (2020). Preparing for Life in a Digital World: IEA International Computer and Information Literacy Study 2018 International Report. Springer International Publishing. https://doi.org/10.1007/978-3-030-38781-5 Gudmundsdottir, G. B., & Hatlevik, O. E. (2018). Newly qualified teachers’ professional digital competence: Implications for teacher education. European Journal of Teacher Education, 41(2), 214–231. https://doi.org/10.1080/02619768.2017.1416085 Häkkinen, P., Ilen, E., Näykki, P., Lehtinen, A., Lieska, A., & Lerkkanen, M.-K. (2025). Digitalisaation vaikutukset oppimiseen, osaamiseen ja hyvinvointiin: tutkimuskatsaus suomalaiseen varhaiskasvatukseen, esi- ja perusopetukseen sekä vapaaseen sivistystyöhön. Opetushallitus. Raportit ja selvitykset, 2025:4a. https://www.oph.fi/fi/tilastot-ja-julkaisut/julkaisut/digitalisaation-vaikutukset-oppimiseen-osaamiseen-ja-hyvinvointiin Hämäläinen, R., Nissinen, K., Mannonen, J., Lämsä, J., Leino, K., & Taajamo, M. (2021). Understanding teaching professionals’ digital competence: What do PIAAC and TALIS reveal about technology-related skills, attitudes, and knowledge? Computers in Human Behavior, 117, 106672. https://doi.org/10.1016/j.chb.2020.106672 Oinas, S., Vainikainen, M.-P., Asikainen, M., Gustavson, N., Halinen, J., Hienonen, N., Kiili, C., Kilpi, N., Koivuhovi, S., Kortesoja, L., Kupiainen, R., Lintuvuori, M., Mergianian, C., Merikanto, I., Mäkihonko, M., Nazeri, F., Nyman, L., Polso, K.-M., Schöning, O., Svedholm-Häkkinen, A., Vanhanen, S., & Hotulainen, R. (2023). Digitalisaation vaikutus oppimistilanteisiin, oppimiseen ja oppimistuloksiin yläkouluissa: Kansallisen tutkimushankkeen ensituloksia suosituksineen. Tampereen yliopisto. https://urn.fi/URN:ISBN:978-952-03-2780-4 Reisoğlu, İ., & Çebi, A. (2020). How can the digital competences of pre-service teachers be developed? Examining a case study through the lens of DigComp and DigCompEdu. Computers & Education, 156, 103940. https://doi.org/10.1016/j.compedu.2020.103940 Tondeur, J., Aesaert, K., Prestridge, S., & Consuegra, E. (2018). A multilevel analysis of what matters in the training of pre-service teacher’s ICT competencies. Computers and Education, 122, 32–42. https://doi.org/10.1016/j.compedu.2018.03.002 Vesisenaho, M., Kyllönen, M., Kukkonen, J., Valtonen, T., & Häkkinen, P. (2024). Teacher educators’ and pre-service teachers’ confidence toward the use of ICT in education. Seminar.net, 20(1), Article 4687. https://doi.org/10.7577/seminar.4687 Vipunen – Education Statistics Finland (n.d.). Students and degrees. https://vipunen.fi/en-gb/university/Pages/Opiskelijat-ja-tutkinnot.aspx Vuorikari, R., Kluzer, S., & Punie, Y. (2022). DigComp 2.2: The Digital Competence Framework for Citizens - With new examples of knowledge, skills and attitudes. Publications Office of the European Union. https://doi.org/10.2760/115376 10. Teacher Education Research
Paper Crafting Inspiring Disaster Education: Prospective Teachers' Journey in Designing Virtual Earthquake Museum Materials With Web 2.0 Tools 1: Mus Alparslan University, Turkey (Türkiye); 2: Mus Alparslan University, Turkey (Türkiye); 3: Mus Alparslan University, Turkey (Türkiye) Presenting Author:Considering that disaster pedagogy is one of the least studied topics in the fields of education and disaster (Preston, 2012 cited in Senanayake et al., 2023), it is predicted that the emerging disaster education and disaster literacy will respond to an important need. At this point people's understanding of decision-making is important. As a matter of fact, it is stated that there can be significant behavioral change in accelerating risk reduction if the systems are restructured in a way to encourage risk-informed behavior and decision-making (GAR, 2022). In structuring these behaviors, people need to be motivated and informed towards a culture of disaster prevention and resilience (UNISDR, 2005). This information dissemination requires education, training, participation and sharing (Parvin et al., 2022). It is known that disaster education is important in disaster risk reduction; it consists of a five-dimensional structure as knowledge, action, intervention, integration and participation (Subarno & Dewi, 2022). Disaster literacy in developing disaster awareness, which is the first step of trainings given to minimize the damage in disasters, can reduce losses and damages caused by disasters (Kesumaningtyas et al., 2022). Studies approaching disaster education in a specific disaster type are more prominent in the literature. Although there are studies (Guo, 2023) that try to present a new perspective specific to the flood issue within the scope of digital disaster education with visuals such as graphics and concept maps, it is seen that these studies are not carried out with a contextual and interdisciplinary approach to disaster education. As a result, as Noviana et al. (2022) stated, it is important to expand the use of digital learning tools in disaster education since disaster literacy for elementary level pre-service teachers will be used to build the conceptual structures of their students in the future. There are findings in the literature that the process including e-book, animation and game design contributes to cognitive behaviors in game-supported disaster education (Nakano &Yamori, 2024) and digital game-supported disaster education (Lin et al., 2013). In addition to all these, there are suggestions for the use of museums in disaster education (Okay, 2021) and it is stated that museums will make a positive contribution in this respect. In the studies conducted, it was found that disaster education in virtual museums contributed positively to learning (Devianti & Anggaryani, 2022), however teachers needed support for online museum studies (Daniela, 2020). It is important that future prospective teachers are the ones who will introduce the concepts related to disasters to children and be competent in their fields. Especially earthquakes, which are expressed as one of the most destructive natural disasters that can cause serious economic, social and environmental impacts among disasters (OECD, 2018), are defined as seismic hazards that can cause great destruction and losses in the shortest time among all natural disasters (Jimee, Upadhyay & Shrestha 2012). For this reason, this study focuses on earthquake awareness training in the dimension of disaster education and disaster literacy. Based on all these reasons, the problem statement of the research seeks an answer to the question, “What are the experiences of material design with web 2.0 tools of teacher candidates who designed materials for disaster education for virtual museum?” Methodology, Methods, Research Instruments or Sources Used While this study focuses on earthquake awareness raising trainings in the dimension of disaster education and disaster literacy, this study focuses on the experience of producing virtual museum contents based on the integration of web 2.0 tools with an interdisciplinary perspective. The research was conducted in a phenomenological design in accordance with the qualitative approach. The study group of the research consists of 34 (3 male, 31 female) pre-service teachers studying at Primary School Teacher Education, Science Teacher Education and Social Studies Education. The interview form developed by the researcher were used as data collection tools. While preparing the questions in the interview form, the opinions of two experts working in the field of disaster education and museum education were consulted. The questions, which were finalized after the expert opinion, were sent to the pre-service teachers via Google Forms file and the opinions of the pre-service teachers were taken. In this study, it is aimed to raise awareness of pre-service teachers about the importance of digital approach in earthquake awareness education and to gain Web 2.0 supported activity design skills. In this framework, with 40 hours of training, pre-service teachers experienced 13 different Web 2.0 tools (Inspiration, MapHub, Canva, Tiki Toki, Wizer.me, SmartDraw, Pixton, Powtoon, Audiocity, 123steps, Postermywall, Kotobee, Artstep) for earthquake education in group work, developed their own teaching materials within the scope of interdisciplinary disaster education and exhibited all materials in Artstep, a virtual museum design application. Qualitative content analysis was used to analyze the qualitative data of the study. Accordingly, based on the seven steps of qualitative content analysis identified by Kuckartz and Rädiker (2023), the first step was to first work with the text (reading, examining words and phrases, exploring the internal structure, etc.), and then the main categories were developed. The first coding cycle was completed by coding the data with these main categories. Subcategories were then developed inductively. This time, the data were coded with these subcategories and the second coding cycle was completed in this way. Themes were generated from the categories and these were analyzed to explore the relationships between thematic categories and subcategories. While analyzing within the main categories, the relationships between their subcategories were examined. MAXQDA 2022 software was used for data analysis. Conclusions, Expected Outcomes or Findings In the virtual museums created by pre-service teachers, concept maps with Inspiration, earthquake maps with MapHub, infographic and digital newspaper with Canva, history strip with Tiki Toki, worksheet with Wizer.me, fishbone with SmartDraw, concept cartoon with Pixton, digital story with Powtoon, audio downloading, editing and adding with Audiocity and 123steps, poster with Postermywall, E-Book with Kotobee were created and all these were exhibited in the online museum created by pre-service teachers in Artstep application. In the results obtained from the research, grouped under the following themes: perceptions, emotions, practices, futuore oriented plans, pedagogy, assessment. It was determined that pre-service teachers enjoyed designing materials with different web 2.0 tools and especially enjoyed exhibiting all the products they designed in a virtual museum. While designing a virtual museum, pre-service teachers expressed that they felt the feeling of building their own works like an architect. They also stated that they could use the materials they produced in different courses and subjects when they become teachers in the future. Pre-service teachers who stated that the use of virtual museums in disaster education is an effective method stated that they mostly encountered problems with the infrastructure of computers and internet infrastructure while designing virtual museums, and that they had difficulty in creating a unity and making a good design while presenting disaster-related content. Although pre-service teachers stated that virtual museum design is an opportunity for those who do not have access to museums, they also stated that the fact that virtual museum design applications are paid is a problem for them. In addition, pre-service teachers stated that their computer skills were not sufficient for virtual museum design and that they needed support in this regard. References Daniela, L. (2020). Virtual museums as learning agents. Sustainability, 12(7), 2698. https://doi.org/10.3390/su12072698 Devianti, W. & Anggaryani, M. (2022). Virtual Museum of Tsunami Project for Increasing Awareness of Disaster Risk Potential in Physics Class. Berkala Ilmiah Pendidikan Fisika, 10(3), 271-285 https://ppjp.ulm.ac.id/journal/index.php/bipf Global Assessment Report on Disaster Risk Reduction-GAR, 2022. Retrieved from https://www.undrr.org/publication/global-assessment-report-disaster-risk-reduction-2022 29.01.2024 Guo, Y., Zhu, J., You, J., Pirasteh, S., Li, W., Wu, J., & Dang, P. (2023). A dynamic visualization based on conceptual graphs to capture the knowledge for disaster education on floods. Natural Hazards, 1-18. Jimee, G.K., Upadhyay, B., & Shrestha, S. (2012). Earthquake Awareness Programs as a Key for Earthquake Preparedness and Risk Reduction: Lessons from Nepal. https://www.iitk.ac.in/nicee/wcee/article/WCEE2012_5202.pdf Kesumaningtyas, M. A., Hafida, S. H. N., & Musiyam, M. (2022).Analysis of disaster literacy on student behavioral responses in efforts to reduce earthquake disaster risk at SMA Negeri 1 Klaten. Earth and Environmental Science, 986 (012013), 1-9. doi:10.1088/1755-1315/986/1/012013 Kuckartz, U. & Rädiker, S. (2023). Qualitative content analysis: Methods, practice and software (2nd press). Sage Publication. ISBN 978-1-5296-0913-4 Lin, S. C., Tsai, M. H., Chang, Y. L., & Kang, S. C. (2013, March). Game-initiated learning: a case study for disaster education research in Taiwan. In 2013 AAAI Spring Symposium Series. Nakano, G. & Yamori, K. (2024). Cultural tuning of a disaster education tool: A comparative study of Japan, El Salvador, and Mexico, International Journal of Disaster Risk Reduction, 105 (104370), https://doi.org/10.1016/j.ijdrr.2024.104370 Noviana, E., Erlinda, S., Novianti, R., Sari, I. K., Mulyani, E. A., Zulkifli, N., & Permana, D. (2023, May). Theoretical study to design digital disaster learning resources for prospective elementary school teachers. In 4th International Conference on Progressive Education 2022 (ICOPE 2022) (pp. 647-653). Atlantis Press. 10. Teacher Education Research
Paper Student Teachers’ Artificial Intelligence Perceptions, Reported Use for Learning, and Learning Outcomes University of Tartu, Estonia Presenting Author:The rapid development and widespread use of artificial intelligence (AI) in recent years have had a profound impact on education, including higher education, affecting both teaching and learning (Rawas, 2024; Sova et al., 2024). The majority of university students use AI-based tools in their studies, and nearly one quarter report using them on a daily basis (Digital Education…, 2024). Artificial intelligence is thus becoming an integral part of higher education. While AI offers new opportunities for enhancing learning, it also raises significant challenges, including concerns related to academic integrity (Ahma & Kadriu, 2025), knowledge construction, and the evolving role of the teacher (Zawacki-Richter et al., 2019). The Digital Education Action Plan 2021–2027 (European Commission, 2020) emphasizes the importance of the responsible use of AI and the development of teachers’ digital competences. Student teachers play a key role here. Their perceptions and attitudes about AI influence how they use it in their own learning, and later, working in the field of education, they serve as role models whose practices and beliefs shape both the ways AI is used and the attitudes toward AI adopted by their students. For this reason, particular attention should be paid to student teachers as a target group. Previous research has shown that teachers’ beliefs and attitudes toward technology significantly influence its pedagogical integration (Ertmer & Ottenbreit-Leftwich, 2010; Tondeur et al., 2016). It is therefore essential to understand how student teachers perceive AI: whether they view it primarily as a learning-support tool, a substitute for the teacher, or a factor that may have negative effects on learning, and also how these perceptions are related to their use of AI. Artificial intelligence should not be understood merely as a technical tool; its use requires critical thinking and ethical awareness (European Commission, 2019). Therefore, teacher education need to foster student teachers’ knowledge, skills, and also attitudes related to the responsible and meaningful use of AI. Although perceptions of AI have been studied in previous research, less is known about how student teachers understand and make sense of AI in the context of their studies. Findings regarding AI-related perceptions have been mixed. For example, some studies have distinguished between positive and negative attitudes toward AI (Schepman & Rodway, 2023), while others have identified acceptance- and fear-related factors (Sindermann et al., 2021). More recently, Nazaretsky et al. (2025) proposed a four-component structure of AI perceptions, including perceived benefits and obstacles, AI readiness, and trust. This study is theoretically based in the Technology Acceptance Model (TAM) and its extensions (e.g., UTAUT), which emphasize the role of perceived usefulness, ease of use, attitudes in predicting individuals’ intentions to use technology and their actual usage behaviour (Davis, 1989; Venkatesh et al., 2003). The aim of this study is to provide an overview of first-year student teachers’ perceptions of artificial intelligence, their reported use of AI for learning, and the relationships between the perceptions, AI use and learning outcomes. The study has four research questions:
Methodology, Methods, Research Instruments or Sources Used The study involved 201 first-year student teachers enrolled in a compulsory Developmental Psychology course at a university in Estonia. Participants represented five teacher education curricula, ranging from early childhood to the lower secondary education. The sample included early childhood education student teachers (n = 81), primary school student teachers (n = 44), and lower secondary school student teachers for multiple subjects across three curricula (n = 92). Consistent with the gender distribution in the teaching profession in Estonia—where men constitute approximately 15% of teachers in general education and only 1.4% in early childhood education—the sample included a relatively small number of male participants (n = 17). Regarding teaching experience, 75% of the respondents had no prior teaching experience, 21% were currently working as teachers, and 4% had worked as teachers in the past. Data were collected using a self-report questionnaire at the end of the course. Participation in the study was voluntary, and informed consent was obtained from participants prior to completing the questionnaire. Students were informed about the purpose of the study and that their responses would be treated confidentially and used for research purposes in an aggregated form. Participation or non-participation in the study had no impact on students’ assessment in the course or final grade. Students’ perceptions of artificial intelligence were measured using a 15-item AI perceptions scale, which included statements reflecting both positive and negative aspects of AI use. The items addressed general perceptions of AI as well as perceptions specifically related to learning. Examples of the items: AI helps me save time, AI provides me with step-by-step explanations, Using AI does not support the development of my own thinking. Responses were recorded on a 5-point Likert-type scale. To assess the frequency of AI use for learning, participants were asked how often they had used artificial intelligence during the Developmental Psychology course. Response options ranged from no use at all, through occasional use in individual learning activities, to regular use across many learning activities. Learning outcomes were operationalised using the final course grade, awarded on a six-point grading scale. Data analysis was conducted using quantitative statistical methods. Confirmatory factor analysis (CFA) was used to validate the factor structure of the AI perceptions scale. Correlation analysis and multiple regression analysis were used to investigate the relationships among student teachers’ AI perceptions, the reported frequency of AI use for learning, and learning outcomes. Conclusions, Expected Outcomes or Findings The following section provides a brief overview of the preliminary findings and further plans of the study. Regarding the frequency of AI use, 20% of respondents reported not using AI during the course, while 52% reported using AI in individual learning activities, and 27% reported occasional use across different tasks. According to self-reports, AI was most frequently used to gain clearer explanations of concepts or topics, generate ideas, and support deeper understanding when the topic was difficult to understand. Confirmatory factor analysis supported a four-factor structure of AI perceptions, including general attitudes toward AI, perceived usefulness for learning, general obstacles, and learning-related obstacles. This multidimensional structure suggests that student teachers’ perceptions of AI are more nuanced than simple positive–negative dichotomies. The expected outcomes focus on examining how student teachers’ AI perceptions predict the reported frequency of AI use, as well as how AI use frequency and the range of learning activities supported by AI correlate with learning outcomes. It is anticipated that perceived usefulness will positively predict AI use, whereas perceived learning-related obstacles may limit both frequency and diversity of use. Overall, the study contributes to teacher education by helping to understand how future teachers understand the benefits and limitations of AI and how these perceptions are related to their own use of AI for learning. By framing AI as an integrated component of teacher competence rather than a merely technical issue, the findings support a broader rethinking of the teacher’s role in the age of artificial intelligence. The Estonian context offers a valuable reference point for European and international discussions on AI integration in teacher education. References Ahma, G. & Kadriu, A. (2025). Harnessing artificial intelligence to transform education: challenges and opportunities. SEEU Review, 20(1). doi: 10.2478/seeur-2025-0020 Davis, F. D. (1986). Technology acceptance model for empirically testing new end-user information systems: Theory and results [Ph.D. Thesis, Massachusetts Institute of Technology]. DSpace@MIT. Retrieved May 16, 2024, from http:// hdl. handle. net/ 1721.1/ 15192 Digital Education Council Global AI Student Survey 2024. Available: https://www.digitaleducationcouncil.com/post/digital-education-council-global-ai-student-survey-2024 Ertmer, P., Ottenbreit-Leftwich, A.T. (2010. Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42 (3), 255–284. European Commission. (2020). Digital Education Action Plan 2021–2027: Resetting education and training for the digital age. Available: https://education.ec.europa.eu/sites/default/files/document-library-docs/deap-communication-sept2020_en.pdf. European Commission - High-Level Expert Group on Artificial Intelligence. (2019). Ethics guidelines for trustworthy AI. Available: https://ec.europa.eu/futurium/en/ai-alliance-consultation.1.html Nazaretsky, T., Mejia-Domenzain, P., Swamy, V., Frej, J., & Käser, T. (2025). The critical role of trust in adopting AI-powered educational technology for learning: An instrument for measuring student perceptions. Computers and Education: Artificial Intelligence, 8, 1-16. doi: 10.1016/j.caeai.2025.100368 Rawas, S. (2024). ChatGPT: Empowering lifelong learning in the digital age of higher education. Education and Information Technologies, 29(6), 6895–6908. doi: 10.1007/s10639-023-12114-8 Schepman, A. & Rodway, P. (2022). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory Validation and Associations with Personality, Corporate Distrust, and General Trust. International Journal of Human–Computer Interaction, 39(13), 2724-2741. doi: 10.1080/10447318.2022.2085400 Sindermann, C. et al. (2021). Assessing the Attitude Towards Artificial Intelligence: Introduction of a Short Measure in German, Chinese, and English Language. KI - Künstliche Intelligenz, 35, 109-118. doi: 10.1007/s13218-020-00689-0 Sova, R., Tudor, C., Tartavulea, C. V., & Dieaconescu, R.I. (2024). Artificial Intelligence Tool Adoption in Higher Education: A Structural Equation Modeling Approach to Understanding Impact Factors among Economics Students. Electronics, 13(18), 3632. doi: 10.3390/electronics13183632 Tondeur, J., van Braak, J., Ertmer, P. A., & Ottenbreit-Leftwich, A. (2016). Understanding the relationship between teachers’ pedagogical beliefs and technology use in education. Educational Technology Research and Development, 65, 555–575. Venkatesh, V., Morris, M.G., Davis, G.B, & Davis, P.D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478. doi: 10.2307/30036540 Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education - where are the ecucators? International Journal of Educational Technology in Higher Education, 16(39). | ||