Conference Agenda

The rise of Large Language Models (LLMs) has fundamentally altered knowledge production in higher education, with over 80% of students now integrating AI into academic tasks (Xia et al., 2024; Fundación Ayuda en Acción, 2025). This widespread adoption, often lacking critical algorithmic or ethical awareness, necessitates a total reappraisal of traditional asynchronous written assessments (Vieru & Petrea, 2015; Francis et al. 2025). While some institutions have responded reactively through bans or proctored exams, this catalyst demands a holistic, integrative approach (Chan, 2023). Aligning with the European Higher Education Area’s emphasis on transferable competencies, AI serves as an urgent driver for redesigning pedagogical methodologies and fostering critical AI literacy within the university space.

This paper presents the design, theoretical foundations, and methodological approach of an ongoing research project. The study aims to analyze the uses and ethical perceptions of generative AI among Education students to evaluate its impact on assessment processes and propose pedagogical strategies that foster critical literacy and institutional debate. The study is part of a funded project (Rethinking the quality of training for Education students in the era of generative artificial intelligence - IA-EduProf) by the National Distance Education University (UNED, Spain) within the call for Teaching Innovation Projects for Teaching Innovation Groups (GID2016-47). It is explicitly situated within higher education, aligning with formative assessment frameworks and internal quality assurance. It seeks to articulate adapted responses capable of combining digital literacy and ethical reflection through curricular redesign and the continuous improvement of training programs for education professionals.

The research is grounded in theoretical frameworks such as Selwyn (2021) and Watters (2021), who advocate for a critical educational technology perspective over technological determinism, allowing AI to be analyzed not as a neutral process but as an opportunity to enhance student agency. The study of uses and ethics is framed by the guidelines of UNESCO (Giannini, 2023) and Tai et al. (2023) regarding algorithmic literacy and educational justice, as well as the theory of assessment as a social practice (Bearman et al., 2020), seeking tasks that transcend the mere generation of automated products. Finally, the project aligns with Luckin’s (2018) vision of adaptive human intelligence, placing critical thinking and pedagogical design at the center of the curricular transformation demanded by the new landscape of quality higher education.

Methodologically, the project adopts a mixed-methods research approach, following the principles of Creswell and Creswell (2018). This allows for the integration of the precision of quantitative usage patterns with the depth of qualitative ethical perceptions (Teddlie & Tashakkori, 2009). This design is based on the concept of methodological triangulation and the pursuit of complementarity (Greene, 2007), ensuring a holistic understanding of AI's impact.

It is expected that this research facilitates a reconfiguration of evaluative praxis, providing clear criteria for transitioning toward authentic assessment models with high resistance to algorithmic replication. Beyond AI detection, the anticipated impact lies in strengthening student commitment and agency by proposing tasks that connect with their future professional identity and reduce the automated nature of assessment. Ultimately, the integration of critical literacy into the curriculum will enable future educators to develop strategic autonomy and sound ethical judgment, consolidating a pedagogical redesign proposal that rigorously addresses the demands of new digital scenarios.

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.

Over the past 25 years, scholarly interest in the transfer of professional development (PD) results into practice and the measurement of its effectiveness has increased significantly. Several influential models have been developed, including Kirkpatrick’s evaluation framework (1956/2016), Guskey’s model of PD effectiveness (2000), Baldwin and Ford’s transfer model (1988), and Sims et al. (2023) IMTP model. Baldwin and Ford (1988) conceptualize transfer as the sustained application of learning in the workplace, emphasizing the interaction between individual characteristics, training design, and work environment support as key facilitating factors. Sims et al. (2023) model aims to measure the impact of teacher professional development through student outcomes by identifying testable indicators and focusing on specific PD interventions (insight, motivation, techniques, and practice). Although different models and frameworks are developed and applied to characterize effectiveness of PD programs and practices, consensus related to effective PD have not reached yet, and measuring its impact remains problematic (Nīmante et al, 2025).

While these models provide a conceptual foundation, they also highlight that transfer is a multi-level and multi-dimensional process, involving cognitive, behavioral, affective, and technical outcomes (Lai, 2020). For PD to generate long-term added value, organizational conditions and systemic support are essential (Hughes et al., 2020). However, despite extensive theoretical development, the measurement of transfer effectiveness remains fragmented, with limited consensus on indicators, methods, and levels of analysis (Blume et al., 2010).

Existing studies demonstrate that PD outcomes are often assessed at the individual level, while organizational and longer-term impacts are less consistently measured. This lack of empirical synthesis and standardized measurement approaches complicates both evaluation and policy-level decision-making. As Boylan et al. (2023) argue, transformative professional learning requires grounding not only in theory but also in systematic evaluation practices. Therefore, further empirical research is needed to explore how PD results are transferred into practice and how transfer effectiveness is understood and measured across different contexts and countries.

Research addresses two main questions:

RQ1 What have been used in practice to determine transferring results of professional development into practice?

RQ2 How is transfer effectiveness measured?

The concept of the ‘expert teacher’ is an international phenomenon with many advanced systems investing in a formal designation, partly explained by education systems profound need to recruit and retain the best teachers, providing a career structure, maintaining their classroom impact and developing other teachers in the system. This research closely examines the purposes and structures of these evolving models in each national context, especially the history of their impact on teachers’ professional status and significance for the future, it traces global influences and the challenges of governance and political control of such designations. It provides an innovative, international typology, categorising and analysing the phenomenon of the models

The concept of the ‘expert teacher’ has a significant history, developing into an increasingly global phenomenon [Goodwyn, 2024]. The USA can celebrate having the longest running and only consistent, model, The Highly Accomplished Teacher, developed in the 1970s, it has an exemplary history. The international nature of the phenomenon is partly explained by the need systems have to recruit and retain the best teachers [Burge, P, Lu, H and Phillips, W., 2021], and to provide them with a meaningful and high status career structure. Such a structure maintains their impact in the classroom and, in some designations, on the development of other teachers in the system, often helping to repair the quality of struggling teachers. {Goodwyn. 2022, 2024]

There are many notable examples of the phenomenon, from around the world, with the Advanced Skills Teacher [AST] in Australia [over 30 years] [Ingvarson LC, 1998, 2009, 2013 & 2014) and The Highly Accomplished Teacher [HAT]in the USA [28 years] [Gitomer DH 2007 & 2008] being the most established. However, some models have disappeared – the AST in England 1997-2013, the Chartered Teacher in Scotland only lasted 7 years [McCormac Review 2011]. There are new developments, the Chartered teacher in England and the Highly Accomplished Teacher in Australia, now 6 years in development [Goodwyn, 2024]. Singapore is evolving an elaborate structure of Leading and Master teachers with several career pathways.

This research examines the history of the purposes and structures of these evolving models and their successes and challenges in each national context. It traces global influences, for example the adoption of the HAT designation in Australia, being adapted from the USA, the Chartered model beginning in England just as it is abolished in Scotland [Author, 2024]. These changes are part of the challenge of governance and political control of such designations and their relative stability or fragility. The overall findings are synthesised in a typology, which categorises the models and provides an analytical framework.

Ontologically, the research adopts a critical realist perspective [Sayer, 1992, 2012, Archer et al 1998] examining the history of the expert teacher concept as fundamentally concerned with structural, systemic improvement, a potentially emancipatory and future oriented project for the agentive teaching profession in each system that can challenge racial inequalities. Epistemologically it adopts a phenomenological stance towards ‘expert teaching’ as a designation, drawing on the emergent field of expertise studies [Dreyfus & Dreyfus, 1998, Ericsson et al, 2012]. It is notable that there are always relatively newer models, for example ‘The Extended Teacher’ from Norway, ‘The Chartered Teacher’ in England, the Expert Teacher Network in Chile and the Korean ‘Master Teacher’, all of which show influence from previous models in other jurisdictions and demonstrate global trends and local adaptations.