Conference Agenda

The increasing prominence of ethical guidelines for Artificial Intelligence (AI) and data use in education, promoted by international organisations such as the European Union (Directorate-General for Education, 2022), UNESCO (Bosen et al., 2023), and the OECD (OECD & Education International, 2023), has not resolved a persistent pedagogical challenge: while ethical principles are widely articulated and institutionally endorsed, their translation into educational practice remains fragile and uneven. In educational contexts, ethics risks being reduced to compliance-oriented checklists or abstract value statements, detached from professional judgment, institutional responsibility, and everyday pedagogical decision-making (Bélisle-Pipon et al., 2022; Gardelli, 2016).

This study addresses this gap by examining the design, implementation, and evaluation of a Self-Reflection Tool developed within the Erasmus+ ETH-TECH project (ETH-TECH Consortium, 2024), aimed at supporting ethical awareness and reflection on AI and data practices in education.

From a historical–cultural perspective on learning, pedagogical instruments function as mediational tools that organise and transform how individuals engage with complex concepts. Liaising with the work of Lev Vygotskij, Wertsch (2007) argues indeed that mediational means are not neutral supports but cultural artefacts that shape the very possibilities of thought and action. In this sense, we purport that an educational instrument that triggers situated reflection can scaffold learners’ understanding by embedding within its structure the cultural, institutional, and policy recommendations that frame the ethical use of educational technologies. The mediational tool thus bridges the distance between high-level guidelines and situated pedagogical practice, enabling users to navigate ethical dilemmas, recognise contradictions, and articulate informed positions. This aligns with historical–cultural theory’s emphasis on learning as a socially mediated activity in which tools and signs expand the learner’s zone of proximal development, opening new pathways for reflective and agentic engagement with the ethical dimensions of technology (Raffaghelli & Crudele, 2025).

The study is guided by three research questions: (1) How do educators and prospective educators perceive their ethical preparedness regarding AI and data use across key ethical dimensions? (2) How does a mediational self-reflection tool support ethical awareness and reflection across different levels of professional experience and training contexts? (3) How is ethical responsibility positioned by participants at the individual, technical, and institutional levels?

Methodologically, the study adopts a Design-Based Research (DBR) approach articulated through three iterative loops (Design Council, 2019). The Self-Reflection Tool was developed and refined through three interconnected research loops, each contributing distinct empirical and conceptual insights.

Across all contexts, participants consistently report higher confidence in human-centred ethical dimensions, such as agency, fairness, and wellbeing, while expressing lower confidence in institutional and technical domains, including accountability, data governance, and technical robustness. Mean scores for human-centred dimensions range approximately between 2.6 and 3.3 on a 0–5 scale, whereas institutional dimensions range between approximately 1.6 and 2.4. These patterns persist across experience levels: although experienced professionals report higher overall confidence, institutional responsibility remains a weak point even among senior educators. More than 75% of participants report a moderate to strong impact of the tool on their ethical reflection, and usability ratings are consistently high, indicating that ethical disengagement cannot be attributed to technical barriers.

The study concludes that the Self-Reflection Tool functions as a mediational artefact that makes visible the structural limits of individualised ethics in AI and data education. Rather than resolving ethical tensions, the tool exposes where ethical responsibility collapses at institutional boundaries, reinforcing the need for shared responsibility, institutional anchoring, and pedagogical mediation within teacher education and professional learning for ethical AI practices to be sustainably enacted.

Generative artificial intelligence (GAI) has become one of the most visible recent innovations shaping educational work, yet institutional responses remain uneven. Across Europe, expectations for responsible and transparent use are rising, while many educators encounter GAI primarily through informal experimentation rather than structured guidance. This creates an urgent need to understand how teachers and future teachers interpret GAI, what they actually use it for, and where they draw ethical boundaries.

This paper examines attitudes and experiences related to GAI among two key groups: (a) in-service teachers working in primary and secondary schools and (b) pre-service teachers enrolled in teacher training programmes. The study addresses four objectives. First, it maps the frequency and purposes of GAI use in educational contexts, including common activities such as generating ideas, searching for information, preparing teaching materials, supporting learning tasks, and improving written texts. Second, it captures perceived benefits and concerns, focusing on how respondents evaluate the usefulness, reliability, and potential harm of GAI in everyday educational practice. Third, it identifies barriers that restrict adoption, including limited knowledge, uncertainty about output accuracy, lack of institutional guidance, and ethical ambiguity. Fourth, it tests whether engagement and attitudes vary by professional status (in-service vs. pre-service), age, teaching experience, and perceived institutional support.

The analysis is informed by the assumption that technology uptake in education is shaped by both perceived value and contextual enablement. From this perspective, the paper examines three hypotheses. H1 proposes that the frequency of GAI use increases when respondents perceive greater institutional support and stronger educational benefits. H2 assumes that older age and longer teaching experience are associated with lower levels of use, reflecting differences in professional routines and technology confidence. H3 suggests that ethically sensitive applications—particularly assessment-related uses—will produce clearer divergence between in-service and pre-service teachers than general attitudes toward GAI integration.

The contribution is relevant to European discussions in three respects. First, it provides an empirical snapshot of early-stage adoption in a national context where professional norms and institutional policies are still evolving. Second, it highlights that institutional support operates as an active condition for adoption rather than a neutral background factor: guidance, recommendations, and shared expectations appear closely linked to routine use. Third, it demonstrates that ethical boundaries are negotiated differently by current and future teachers, which matters for developing realistic teacher education content and school-level rules.

Overall, the paper aims to inform evidence-based debate on how schools and teacher education institutions can move from fragmented individual practices toward supported, pedagogically grounded, and ethically coherent use of GAI.

According to Chugh et al. (2023), the initial incorporation of digital technologies in higher education was initially focused on access to infrastructure and platforms, however, the research and pedagogical focus has progressively shifted toward pedagogical appropriation and the impact of these technologies on teaching and learning processes. Currently, the availability of devices does not in itself ensure educational innovation, and gaps persist that depend more on teachers’ competencies and dispositions than on connectivity (Inamorato et al., 2023). This context requires analysing how sociocultural and personal factors of the teaching staff condition didactic decision-making, defining whether technology operates as an administrative support or as a transformative element of learning (Nagy & Dringó-Horváth, 2024; Osorio et al., 2025).

To examine these disparities, the study adopts Park’s (2017) Digital Capital theory. According to this framework, an individual’s technological competence results from the articulation of three forms of capital: economic (access to technologies), cultural (digital competencies), and social (support networks). Based on this approach, five academic profiles of technology use are identified: Traditional reproducer: basic instrumental use for management, maintaining the expository structure. Dependent laggard: low autonomy with reactive use subordinate to external support. Critical-reflective: high but selective capital, where technology is used only if there is a clear pedagogical justification. Cautious experimenter: carries out controlled innovations through pilots to assess risks before adopting them. Strategic innovator: an agent of change with high digital capital who seeks to transform the pedagogical environment.

According to Ertmer and Ottenbreit-Leftwich (2010), pedagogical beliefs filter the use of technology; therefore, its integration depends more on educational purpose than on access. Consequently, purpose—or, in a broader sense, teachers’ digital pedagogical intentionality—becomes relevant. Although there is no established definition of digital pedagogical intentionality, for the purposes of this study it is defined as the degree to which the use of technologies responds to deliberate planning aligned with learning objectives. Three types of digital pedagogical intentionality are proposed for analysis: with a focus on teaching, on learning, and on modelling teaching practices.

Ertmer and Ottenbreit-Leftwich (2010) have established that technology use focused on teaching occurs when the teacher is the central axis of the process and uses technology to optimize content delivery and management, while the student maintains a receptive role. Likewise, when technology is used with a focus on learning, student autonomy is sought, with students using tools to investigate and construct knowledge, and the teacher acting as a facilitator. Based on the review by Tondeur et al. (2017), it is possible to argue that pedagogical experiences mediated by digital technologies in teacher education contexts can influence the configuration of pedagogical beliefs that are more learning-centred, which opens a theoretical space (although not explicitly developed by the authors) to interpret these practices as potential formative references for future teachers.

Given the interaction between technological dispositions and didactic decisions, the research analysed the relationship between digital capital profiles and types of pedagogical intentionality, seeking to answer the following research question: What are the distinctive elements of the digital pedagogical intentionality of teachers classified in the different digital capital profiles?

This research includes an initial stage of interviews with academics to determine their digital profiles (presented here) and a developing phase of classroom observation in Chile and Spain to examine types of intentionality, task complexity, and their relationship with teachers’ autonomy in using digital technologies and the type of role assumed by students.