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:

1. How do PSTs in Canada and the UAE view AIEd and its integration in teacher education programs?
2. What are PSTs’ perceived needs for AI-related training?

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.

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.

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.

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