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10 SES 14 D: Artificial Intelligence and Digital Transformation in Teacher Education
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10. Teacher Education Research
Paper From Knowing to Acting: A Critical Thematic Review of Culturally Responsive Artificial Intelligence in Teacher Education Ağrı İbrahim Çeçen University, Turkey (Türkiye) Presenting Author:The growing integration of artificial intelligence (AI) into education has significantly influenced how knowledge is produced, interpreted, and mobilized within teaching and learning processes (Luckin et al., 2016; Holmes, Bialik, & Fadel, 2019). In the field of teacher education, AI is predominantly discussed in relation to digital competence, instructional efficiency, and personalized learning environments, often emphasizing teachers’ preparedness to operate within increasingly data-driven and automated educational systems (Redecker, 2017; Williamson, Eynon, & Potter, 2020). While this body of research highlights the transformative potential of AI, it largely conceptualizes knowledge as a technical and instrumental resource, frequently detached from ethical, cultural, and social justice considerations (Selwyn, 2019; Akgun & Greenhow, 2021). As a result, the relationship between knowing AI and acting in culturally responsive and equitable ways remains under-theorized and insufficiently examined in teacher education research (Kizilcec & Lee, 2020). Critical scholarship has increasingly problematized the assumption that AI technologies are neutral or value-free. Studies demonstrate that algorithms and data-driven systems frequently reproduce existing power relations, cultural hierarchies, and structural inequalities embedded in their design and training data (Benjamin, 2019; Noble, 2018). Despite these concerns, teacher education research tends to prioritize AI literacy and technological proficiency, with limited attention to how teachers are prepared to engage critically with AI as a cultural and ethical phenomenon. This creates a significant gap between knowledge production in AI-related teacher education research and the pedagogical actions required to promote equity and inclusion in diverse educational contexts. Culturally responsive pedagogy offers a powerful framework for interrogating this gap. Grounded in the recognition of learners’ cultural identities and the moral responsibilities of teaching, culturally responsive pedagogy positions educational practice as inherently value-laden and action-oriented (Gay, 2018; Ladson-Billings, 1995). However, existing literature suggests that even when issues of diversity and equity are acknowledged, they are rarely integrated into discussions of emerging educational technologies such as AI. Consequently, the literature reflects a persistent knowing–acting divide: while research increasingly recognizes the risks and biases associated with AI, it offers limited guidance on how such knowledge can be translated into culturally responsive pedagogical action. The ECER 2026 theme, *“Knowing and Acting: The changing conditions and potentials of education research,”* provides a timely lens for examining this disconnect. In the context of AI and teacher education, this theme invites critical reflection on how educational research frames knowledge, whose knowledge is privileged, and how research contributes—or fails to contribute—to transformative educational practice. Yet, to date, no comprehensive review has systematically examined how AI-related teacher education literature conceptualizes the relationship between knowing and acting from a culturally responsive and ethical perspective. Addressing this gap is essential for advancing both theory and practice. A critical synthesis of literature can illuminate dominant epistemological assumptions, identify silences and omissions, and foreground the conditions under which AI-related knowledge may support culturally responsive and socially just educational action. Such an analysis is crucial not only for teacher education research but also for broader debates about responsibility, equity, and agency in the age of artificial intelligence. Methodology, Methods, Research Instruments or Sources Used This study employs a critical thematic literature review design to examine how research on artificial intelligence in teacher education conceptualizes the relationship between knowledge (knowing) and pedagogical action (acting) in relation to cultural responsiveness, equity, and ethics (Braun & Clarke, 2006; Grant & Booth, 2009). Rather than aiming to quantify trends or evaluate effectiveness, the review seeks to critically synthesize and interpret existing scholarship to reveal dominant themes, underlying assumptions, and conceptual gaps, an approach particularly suited to examining emerging and interdisciplinary fields such as AI in education (Booth, Sutton, & Papaioannou, 2016; Snyder, 2019). By adopting such a critical stance, the review moves beyond descriptive mapping to interrogate how knowledge is framed, whose perspectives are privileged, and how research contributes to—or constrains—transformative pedagogical action (Gough, Oliver, & Thomas, 2017). The literature search was conducted across major academic databases commonly used in educational research, including Scopus, Web of Science, and ERIC. Key search terms included combinations of *artificial intelligence*, *teacher education*, *preservice teachers*, *AI literacy*, *culturally responsive pedagogy*, *equity*, *ethics*, and *teacher agency*. Peer-reviewed journal articles published in English were considered, with a focus on studies situated within teacher education contexts. Inclusion criteria were defined to capture studies that explicitly addressed AI in relation to teacher education or preservice teacher preparation. Studies focusing solely on technical system development or student outcomes without pedagogical or educational implications were excluded. Following the screening process, the selected studies were subjected to an iterative thematic analysis. The analysis proceeded in three stages. First, studies were examined to identify how AI-related knowledge was conceptualized (e.g., technical, pedagogical, ethical). Second, attention was given to how—or whether—issues of cultural responsiveness, equity, and social justice were addressed. Third, the review analyzed how literature framed the relationship between AI-related knowledge and pedagogical action, including notions of responsibility and teacher agency. Throughout the process, a critical stance was adopted to interrogate whose knowledge is foregrounded, which perspectives are marginalized, and how research contributes to or constrains transformative educational practice. Conclusions, Expected Outcomes or Findings This review is expected to demonstrate that majority of AI-related teacher education literature privileges technical and instrumental forms of knowledge, while giving limited attention to culturally responsive, ethical, and action-oriented dimensions of teaching. Although concerns about bias, fairness, and accountability are increasingly acknowledged, these issues are often discussed abstractly and remain weakly connected to pedagogical practice and teacher agency. By synthesizing the literature through a knowing–acting lens, the study is expected to identify a persistent conceptual gap between AI literacy and culturally responsive pedagogical action. The review will highlight how cultural responsiveness is frequently treated as an add-on rather than a foundational principle in AI-related teacher education research. Theoretically, the study contributes to teacher education and AI scholarship by integrating culturally responsive pedagogy and teacher agency into the analysis of AI-related knowledge production. Conceptually, it offers a critical framework for understanding how AI knowledge can be reoriented toward ethical and socially just educational action. Practically, the findings provide guidance for teacher ED researchers and curriculum designers seeking to move beyond technical competence toward preparing teachers as culturally responsive and ethically responsible agents in AI-mediated educational contexts. By doing so, the study responds directly to the core concerns of ECER 2026 and underscores the transformative potential of educational research. References Akgun, S., & Greenhow, C. (2021). Artificial intelligence in education: Addressing ethical challenges in K–12 and teacher education. Educational Technology Research and Development, 69(1), 1–24. Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim Code. Polity Press. Booth, A., Sutton, A., & Papaioannou, D. (2016). Systematic approaches to a successful literature review (2nd ed.). Sage. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. Gay, G. (2018). Culturally responsive teaching: Theory, research, and practice (3rd ed.). Teachers College Press. Gough, D., Oliver, S., & Thomas, J. (2017). An introduction to systematic reviews (2nd ed.). Sage. Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26(2), 91-108. https://doi.org/10.1111/j.1471-1842.2009.00848.x Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. Kizilcec, R. F., Lee, H. (2022). Algorithmic fairness in education. In W. Holmes & K. Porayska-Pomsta (Eds.), Ethics in Artificial Intelligence in Education, Routledge. ISBN: 9780429329067 Ladson-Billings, G. (1995). Toward a theory of culturally relevant pedagogy. American Educational Research Journal, 32(3), 465–491. http://links.jstor.org/sici?sici=0002-8312%28199523%2932%3A3%3C465%3ATATOCR%3E2.0.CO%3B2-4 Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson. Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press. Redecker, C. (2017). European framework for the digital competence of educators: DigCompEdu. Publications Office of the European Union.https://publications.jrc.ec.europa.eu/repository/handle/JRC107466 Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press. Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039 Williamson, B., Eynon, R., & Potter, J. (2020). Pandemic politics, pedagogies and practices: Digital technologies and distance education during the coronavirus emergency. Learning, Media and Technology, 45(2), 107–114. https://doi.org/10.1080/17439884.2020.1761641 10. Teacher Education Research
Paper Teacher Trainers as Artificial Intelligence Mediators: Policy Versus Practice 1: University of Helsinki, Finland; 2: University of Malta; 3: Vilnius University Presenting Author:The rapid development of generative artificial intelligence (GenAI), particularly large language model–based tools, is reshaping educational practices across Europe (Cukorova et al., 2024), and these tools are transforming teaching and learning practices (UNESCO, 2023). Unfortunately, Higher Education Institutions (HEIs) may be unprepared to equip teacher trainers and future teachers with the knowledge and skills needed to integrate AI into teaching and learning in an ethical and creative manner. Moreover, the inherent ethical and practical risks (Wieczorek et al., 2025) and national regulations set limits on how different institutions and schools can implement GenAI tools. Thus, teachers and teacher trainers face competing demands from various stakeholders and may struggle with conflicting pressures. While recent research has begun to explore teachers’ AI readiness (Wang et al., 2023) and pre-service teachers’ attitudes towards GenAI (Gamlem et al., 2025), the role of teacher trainers remains under-theorised and under-examined, despite their central position in shaping both initial teacher education (ITE) and continuous professional development (CPD). Therefore, research-based approaches to integrating AI literacy into teacher training are needed. Unfortunately, despite rising scholarly interest (Liu et al., 2023; Cukorova et al., 2024), evidence-based pedagogical strategies for integrating ethical and creative applications of AI in teaching and learning remain limited. This paper draws on data from EmpowerAId, an Erasmus+ Teacher Academies project involving ITE and CPD providers from eight European countries. The project aims to strengthen AI literacy and ethical awareness among pre- and in-service teachers by developing a European “Train the Trainer” programme and fostering cross-sectoral collaboration between teacher educators, policymakers, and education technology stakeholders. As part of the project’s needs analysis phase, qualitative interviews were conducted with teacher trainers to explore existing practices, institutional conditions, and perceived tensions related to GenAI integration in teacher education. By interviewing teacher trainers across different European countries, we aim to explore how AI is used in teacher training, what pedagogical value it offers, how policies or institutional structures affect training, and what differences exist between these countries. Our research questions are:
The findings inform both institutional decision-making and policy development by foregrounding the voices and practices of teacher trainers as key agents in the AI transition in education. Methodology, Methods, Research Instruments or Sources Used Altogether, thirty-five teacher trainers were interviewed across the eight partner countries: Cyprus, Finland, Greece, Lithuania, Malta, Romania, Slovenia, and Spain. The semi-structured thematic interviews were conducted around five thematic areas: Practice & Pedagogy, Ethics & Risk, Institutional Support, Capability & Partnerships, and Motivation & Evidence. Each partner conducted their interviews in their native language using a shared interview protocol developed within the project. Using the native languages was intended to support richer, more detailed responses from interviewees. The interviews were conducted either in person or online and were recorded. Subsequently, the recordings were transcribed and summarised in English based on the thematic structure. A joint rubric was used to ensure uniformity and consistency in the summaries across countries. The data were analysed using deductive thematic analysis, guided by predefined thematic domains embedded in the interview protocol. At the same time, the analysis allowed for the identification of cross-cutting and emergent patterns both within and across the themes. Conclusions, Expected Outcomes or Findings The interviews reveal that most teacher trainers actively use generative AI. Only a small number of respondents reported limited experience with GenAI, and just one expressed clear hesitancy, primarily due to a lack of personal preparedness. GenAI is used both for trainers’ own lesson planning and as a component of teacher education, with an added emphasis on promoting ethical and responsible use in the classroom. A likely reason for the widespread adoption—or the intention to adopt—GenAI is necessity, as one respondent noted: “Generative AI is essential and cannot be ignored.” This sentiment persists even among trainers who might otherwise prefer more traditional teaching approaches. Across countries, GenAI use was commonly described as a professional requirement rather than a pedagogical preference, reflecting the perceived inevitability of AI in contemporary education. Importantly, many teacher trainers reported integrating GenAI despite limited institutional guidance or material support, positioning them as proactive mediators rather than passive policy implementers. A recurring tension emerged between comparatively supportive higher education institutions and more cautious or restrictive governmental policy frameworks. While universities often encouraged experimentation, national regulations and ambiguous guidelines constrained pedagogical implementation. Key limiting factors included a lack of concrete institutional guidance, insufficient access to licensed tools, infrastructural disparities—particularly in rural settings—and, most critically, limited time for pedagogical experimentation and reflection. Conceptually, the study advances the idea of teacher trainers as AI mediators, who navigate structural constraints while shaping ethically grounded and pedagogically meaningful uses of GenAI. The findings highlight the need for policy approaches that recognise and support this mediating role through clearer guidance, sustained professional development opportunities, and realistic resourcing strategies. References Gamlem, S. M., McGrane, J., Brandmo, C., Moltudal, S., Sun, S. Z. & Hopfenbeck, T. N. (2025). Exploring pre-service teachers’ attitudes and experiences with generative AI: a mixed methods study in Norwegian teacher education. Educational Psychology. https://doi.org/10.1080/01443410.2025.2528663 Cukurova, M., Kralj, L., Hertz, B. & Saltidou, E. (2024). Professional Development for Teachers in the Age of AI. European Schoolnet. Brussels, Belgium. Liu, B. L., Morales, D., Roser-Chinchilla, J., Sabzalieva, E., Valentini, A., Vieira do Nascimento, D., & Yerovi, C.(2023). Harnessing the era of artificial intelligence in higher education: a primer for higher education stakeholders. Unesco (2023). Guidance for generative AI in education and research. Education 20230. Unesco. Wang, X., Li, L., Tan, S. C., Yang, L., & Lei, J. (2023). Preparing for AI-enhanced education: Conceptualizing and empirically examining teachers’ AI readiness. Computers in Human Behavior, 146. https://doi.org/10.1016/j.chb.2023.107798 Wieczorek, M., Hosseini, M. & Gordijn, B. (2025). Unpacking the ethics of using AI in primary and secondary education: a systematic literature review. AI Ethics 5, 4693–4711. https://doi.org/10.1007/s43681-025-00770-0 10. Teacher Education Research
Paper Cross-border Perspectives on AI Adoption in Teacher Education: A Comparative Study of Pre-Service Teachers in Canada and the United Arab Emirates 1: Sharjah Education Academy, United Arab Emirates; 2: Brock University, Canada Presenting Author:In the age of Artificial Intelligence (AI), the discourse on equipping future teachers with the needed technological skills to effectively navigate the evolving classrooms continues to be prioritized in different educational policy contexts. According to Ejjami (2024), “the educational system has to change to keep up with technological improvements by incorporating advanced learning tools, updating curricula to include AI literacy, and training educators to utilize these technologies in the classroom effectively” (p. 3). The integration of GenAI in educational practices during the past decade has been leading to various inquiries on what implications AI may have for teacher education preparation and the kind of complexities future teachers are anticipated to face in terms of teaching, assessment, and ethical practices (Aydarova, 2023; Mai, 2024; Niu et al., 2022; Zulkarnain & Yunus, 2023). Studies have shown that the use of AI tools may foster educational effectiveness for both teachers and learners including an enhanced personalized learning and the creation of more engaging and supportive learning environments (Mai, 2024). That said, educational effectiveness and future teachers’ readiness for AI-integrated classrooms calls for revisiting teacher education programs to understand the existing limitations and possibilities surrounding the adoption of GenAI in teacher education designs (Gupta, 2024). In the United Arab Emirates (UAE), various policies and directives have been issued to further advance AI in education with the recent announcement of the government’s initiative of introducing GenAI as a mandatory subject in all UAE’s public schools’ curricula from Kindergarten to grade 12 effective August 2025 (Education UAE, 2025). The aim is to develop students’ skills, competencies, and knowledge for a fast-developing and technology-infused educational landscape. In Canada, GenAI use among university students is on the rise. The latest 2025 Canadian Student Wellbeing Report showed that 78% of students use AI tools to assist with their assignments and studying (Studiosity, 2025). University students use GenAI for a variety of purposes, most of which are closely related to academic tasks. These tasks include drafting essays, proofreading, solving complex problems, and conducting research (Gracey et al., 2025). GenAI tools assist students in comprehending academic content and serve as a personalized learning aid that reduces academic stress by simplifying complex concepts (Lin et al., 2024). However, in a study by KPMG, Canada ranked 44th in AI training and literacy out of 47 countries, and 28th among 30 advanced economies (KPMG, 2025). As limited comparative studies on GenAI in teacher education between Canada and the UAE exist, this study was sought to examine the perspectives of PSTs in both countries on AI integration in teacher education through a survey with open and close-ended questions. The study aimed to understand how PSTs in both countries, based on their situated context, conceptualize this integration of GenAI and its implications to their professional practice in the classroom. We were interested in answering the following research questions:
The OECD AI Literacy Framework (Organization for Economic Cooperation and Development (OECD) & European Commission, 2025) was employed as the study’s theoretical lens. It provides a definition for AI across four different domains namely Engage with AI, Create with AI, Manage AI, and Design AI. Engage with AI is about recognizing the use of AI and evaluating its outputs critically. Create with AI relates to collaborating with AI to generate ideas and assist in problem solving. Manage AI and Design AI are for how users can delegate tasks for AI and understand the principles of AI systems and impact respectively. Methodology, Methods, Research Instruments or Sources Used Quantitative and qualitative data were collected through an online survey, developed by the authors based on a previous survey that has been checked for content validity. Each of the authors led the research in their respective institution and obtained the required ethical clearance from its ethics research board. Informed consent was obtained from all participants. The survey included five Likert scale items addressing PSTs’ views toward AIEd: 1. How do you feel about AI being an integral part of teacher education programs? (1 being extremely uncomfortable and 5 being extremely comfortable) 2. To what extent have AI tools (e.g., ChatGPT or other) facilitated or enhanced your learning experience in the teacher education program? (1 being not at all and 5 being to a great extent) 3. How do you feel about using AI in your future teaching? (1 being extremely uncomfortable and 5 being extremely comfortable) 4. How do you feel about using AI in your future teaching? (1 being extremely uncomfortable and 5 being extremely comfortable) 5. In your current studies (Teacher Education program), approximately what percentage of your assignments or other course work could have been done by ChatGPT/ other AI tools? (0-100%) Additionally, the survey included two open-ended questions: 1. Are you engaged in discussions around AI in any of the courses in your teacher education program? If yes, which? 2. What topics would you like to explore further in relation to AI in Education as part of your teacher education program? Participants in this study were 108 PSTs at a public Canadian university and 119 at a public university in the UAE. Participants in the Canadian university were PSTs in their first year of the two-year teacher education program. Participants in the UAE university were PSTs completing their postgraduate teacher education diploma following the teaching policy in the UAE issued in 2017 (UAE Ministry of Education, 2020) requiring all those teaching or planning to teach in K-12 settings to obtain a teaching diploma. Descriptive statistics was used to analyze the quantitative data. Using MS Excel, the authors calculated counts, averages, and standard deviation on Likert scale items. The quantitative data provided an overview of PSTs’ views toward AIEd in both contexts. Additionally, qualitative data was analyzed using inductive thematic analysis according to the OECD’s AI Literacy Framework and following Braun and Clarke’s (2022) six-steps approach to provide more details and insights. Conclusions, Expected Outcomes or Findings PSTs in the UAE reported higher comfort with AI being an integral part of their teacher education programs (M = 4.4, SD = 0.9) compared to their Canadian counterparts (M = 3.6, SD = 1.1). In contrast, Canadian participants exhibited more mixed attitudes, reflecting a wider range of comfort and uncertainty. UAE participants perceived AI tools as having enhanced their learning to a greater extent (M = 4.2, SD = 0.9) than Canadian participants (M = 3.4, SD = 1.1). Canadian participants’ perceptions were more moderate and varied. When asked about their comfort using AI in their future classrooms, UAE’s PSTs again reported higher comfort (M = 4.4, SD = 0.9) than those in Canada (M = 3.5, SD = 1.0). The higher average and lower standard deviation among UAE respondents point toward a more unified and positive outlook on adopting AI pedagogically. Qualitative results in the Canadian context found PSTs divided on whether GenAI has been a topic of discussion in their teacher education program. Some believed that there were no mention of AI and its related practices in teacher education while others found AI briefly addressed. PSTs in the UAE expressed different perspectives. While 48% reported that AI was indeed incorporated into the classroom discussion, 54% said that AI had not been addressed. For PSTs in the UAE, AI was discussed in courses like classroom management, methods of teaching science, education technology, curriculum design, STEM and special education. Moreover, Canadian PSTs expressed various interests that pertain to learning more about GenAI in their teacher education program. These included ethical use of AI, focusing on how to detect and prevent cheating. They were concerned about how to ensure students in their future classrooms learn in an authentic way and not rely on AI for submitting dishonest assignments. References Al Darayseh, A. (2023). Acceptance of artificial intelligence in teaching science: Science teachers’ perspective. Computers and Education: Artificial Intelligence, 4, 100132. https://doi.org/10.1016/j.caeai.2023.100132 Alneyadi, S., & Wardat, Y. (2023). ChatGPT: Revolutionizing student achievement in the electronic magnetism unit for eleventh-grade students in Emirates schools. Contemporary Educational Technology, 15(4), ep448. https://doi.org/10.30935/cedtech/13417 Altinay, Z., Altinay, F., Sharma, R. C., Dagli, G., Shadiev, R., Yikici, B., & Altinay, M. (2024). Capacity building for student teachers in learning, teaching artificial intelligence for quality of education. Societies, 14(8), 148. https://doi.org/10.3390/soc14080148 Butler-Ulrich, T., Hughes, J., & Morrison, L. (2024). Creativity and Generative AI for Preservice Teachers. In Creativity in Contemporaneity. IntechOpen. https://doi.org/10.5772/intechopen.1007517 Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/access.2020.2988510 Eaton, S. E. (2024). Pre-service teacher education in a postplagiarism world: Incorporating GenAI into teacher training. Brock Education Journal, 33(3), 11-16. https://doi.org/10.26522/brocked.v33i3.1175 Education UAE (2025). AI education initiative to equip UAE students for a tech-driven future. https://gulfnews.com/uae/education/uae-to-launch-ai-curriculum-starting-in-kindergarten-7-core-concepts-revealed-1.500115415 [Accessed 9th June 2025] Gracey, C., Morris, J., Witherspoon, R., & Moore, E. (2025). A study of graduate students’ experiences of artificial intelligence at the University of New Brunswick. Proceedings of the Annual Conference of CAIS 2024. https://doi.org/10.29173/cais1936 KPMG (2025). Study shows Canada among least AI literate nations. https://kpmg.com/ca/en/home/media/press-releases/2025/06/study-shows-canada-among-least-ai-literate-nations.html MacDowell, P., Moskalyk, K., Korchinski, K., & Morrison, D. (2024). Preparing educators to teach and create with generative artificial intelligence. Canadian Journal of Learning and Technology, 50(4) https://doi.org/10.21432/cjlt28606 Organization for Economic Cooperation and Development (OECD) & European Commission. (2025). Empowering learners for the age of AI: An AI literacy framework for primary and secondary education. OECD Publishing. Retrieved from https://ailiteracyframework.org Shwedeh, F., Salloum, S. A., Aburayya, A., Fatin, B., Elbadawi, M. A., Al Ghurabli, Z., & Al Dabbagh, T. (2024). AI adoption and educational sustainability in higher education in the UAE. In Artificial Intelligence in Education: The Power and Dangers of ChatGPT in the Classroom (pp. 201-229). Springer. https://doi.org/10.1007/978-3-031-52280-2_14. Studiosity. (2025). 78% of Canadian students have used genAI to help with assignments or study tasks. https://www.studiosity.com/2025-can-wellbeing Tomczyk, Ł. (2024). Digital competence among pre-service teachers: A global perspective on curriculum change as viewed by experts from 33 countries. Evaluation and Program Planning, 102, 102449. https://doi.org/10.1016/j.evalprogplan.2024.102449[1] UAE Ministry of Education. (2020) Educational professions licensing system. Available from: https://tls.moe.gov.ae/#!/about [Accessed 6th December 2025]. | ||