The rapid expansion of artificial intelligence (AI) in higher education has become a key concern across education systems, particularly in relation to teacher education, quality assurance, and ethical governance. Although AI is increasingly positioned in European policy discourse as a driver of innovation, inclusion, and competitiveness, research continues to demonstrate fragmented and uneven integration of AI into pedagogical practice and teacher preparation (Kalnina et al., 2024; Chan & Hu, 2023; Guan et al., 2025). Despite growing interest in AI-enhanced education, there remains a lack of empirical research examining the factors that shape pre-service teachers’ attitudes, intentions, and actual use of AI in learning and teaching contexts (Bearman et al., 2023).

 Teachers play a central role in developing learners’ AI literacy through the responsible integration of AI tools into educational practice. However, recent evidence suggests that only a small proportion (15%) of learners acquire knowledge about AI from teachers, while most exposure occurs through informal channels such as social media (OECD, 2025). Teacher educators, therefore, emphasise the importance of fostering AI literacy and promoting critical engagement with AI-generated content to ensure ethically and pedagogically sound implementation (Prilop et al., 2025). At the same time, research indicates that AI tools can enhance learning outcomes and teaching strategies for pre-service teachers, for example, by supporting personalised learning and facilitating the assessment of pedagogical content knowledge (Al-Shammari & Al-Enezi, 2024; Blonder et al., 2025).

 Alongside these benefits, significant concerns remain regarding ethical issues, data privacy, and the potential for AI to reinforce existing educational inequalities (Mohebi, 2025). Qualitative evidence suggests that pre-service teachers often lack a sufficient understanding of AI fundamentals and ethical principles necessary for the meaningful integration of AI into education (Guan et al., 2025). Moreover, there is a risk that over-reliance on AI tools may undermine the development of essential pedagogical skills, critical thinking, and human relationships in teaching and learning processes (Ziying et al., 2026). These tensions highlight the need for continued, context-sensitive research on the use of AI in teacher education.

 The proposed study contributes to this debate by examining pre-service teachers’ engagement with AI tools within the Latvian higher education context. Latvia represents a particularly relevant European case, as many pre-service teachers simultaneously study and work in schools—an increasingly common situation across European countries facing teacher shortages and flexible qualification pathways. This dual role places pre-service teachers at the intersection of higher education policy, school practice, and digital innovation, making their experiences especially informative for comparative European analysis.

 The study aims to explore pre-service teachers’ experiences, habitual practices, and attitudes toward AI use in the study process, with a focus on perceived benefits, academic and ethical risks, and impacts on learning quality and professional skill development. The research is guided by the following questions:

 RQ1: What AI tools do pre-service teachers use in the study process, and for which habitual learning practices are they most commonly applied?

RQ2: What attitudes do pre-service teachers hold toward the use of AI tools in higher education?

RQ3: What benefits and academic or ethical risks do pre-service teachers perceive in relation to AI use in their studies?

 Conceptually, the study draws on European and international AI literacy frameworks (OECD, 2025), research on digital competence in teacher education, and emerging European scholarship on generative AI in pedagogical contexts (Prilop et al., 2025). By situating empirical findings within these shared frameworks, the study contributes to European-level discussions on how teacher education programmes can support ethically grounded, pedagogically meaningful, and socially responsible integration of AI.

The study employs a mixed-methods research design, combining quantitative and qualitative approaches to capture both the breadth and depth of pre-service teachers’ engagement with AI tools. This design enables triangulation of data and supports a nuanced understanding of practices, perceptions, and contextual influences. The quantitative component consists of a survey administered to pre-service teachers enrolled in teacher education programmes at the University of Latvia. The survey will be distributed electronically to the participants. The estimated number of respondents is 300. The survey instrument is developed based on an extensive literature review and addresses AI tool usage patterns, purposes of use, perceived benefits and risks, ethical considerations, and self-assessed impacts on learning quality and skill development. A 5-point Likert scale ranging from “strongly disagree” to “strongly agree” will be used. A pilot study was conducted to refine the instrument prior to full-scale data collection. Quantitative data will be analysed using descriptive and inferential statistics in IBM SPSS Statistics (version 28), enabling identification of usage trends, associations between variables, and differences across subgroups. The qualitative data will be gathered through semi-structured interviews. The estimated number of participants is 15. The interviews explore participants’ experiences with AI tools in greater depth, focusing on decision-making processes, ethical reflections, professional identity, and perceived tensions between support and risk. Interview data are transcribed verbatim and analysed thematically using NVivo software. All research procedures adhere to institutional ethical guidelines, including obtaining informed consent, voluntary participation, maintaining confidentiality, and handling data securely. By integrating quantitative patterns with qualitative insights, the methodology provides a robust empirical foundation for addressing the research questions and informing international discussions on AI in teacher education.

Initial results indicate that students primarily use AI to search for information (62% report doing so often or very often). Compared with a previous study (Kalniņa et al., 2024), the proportion of students who believe that the use of AI in the study process should be prohibited has decreased significantly (from 35% to 8%). The data analysis will be completed by June 2026. The authors aim to identify and outline empirical evidence showing how pre-service teachers engage with a range of AI tools, primarily for information retrieval, academic writing support, lesson planning, and the development of instructional materials. While AI tools are commonly perceived as enhancing efficiency, flexibility, and learning support, the findings are also expected to highlight significant concerns related to academic integrity, critical thinking, data privacy, and ethical responsibility. The results will provide insight into how pre-service teachers use AI tools in their studies and into the institutional and pedagogical supports needed to strengthen their learning and professional preparation. By supplementing existing empirical research on AI use in higher education and teacher education, the study examines whether AI continues to be used mainly for informational and technical support, while simultaneously identifying associated academic and pedagogical risks. It is anticipated that many pre-service teachers will report limited formal guidance from teacher educators on the pedagogically and ethically responsible use of AI, revealing a gap between institutional expectations and actual support structures. From a broader European and international perspective, the study contributes context-sensitive evidence to ongoing debates on AI integration in education and informs curriculum development, teacher educator professional learning, and policy discussions on the responsible use of AI.

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