This study explores how artificial intelligence (AI) can support open research data practices in school leadership studies, with particular attention to ethical, participatory, and governance-related challenges. While Open Science promotes transparency, accessibility, and reusability of research data, educational leadership research often relies on highly contextual, sensitive, and relational data involving school principals, teachers, and institutions. The integration of AI into data preparation and reuse processes introduces new opportunities, but also raises concerns regarding data sovereignty, consent, power relations, and trust.

The central research question guiding this study is:

How can AI-supported data preparation, anonymisation, documentation, and reuse practices enhance Open Research Data in school leadership studies while maintaining ethical integrity and participatory governance?

By addressing this question, the study aims to move beyond technical discussions of AI and instead focus on everyday research practices, researcher responsibilities, and governance arrangements in educational leadership research.

Theoretical Framework

The theoretical framework of this study is grounded in an interdisciplinary combination of Open Science, participatory research, and democratic educational governance, with AI positioned as a mediating socio-technical actor rather than a neutral tool.

First, Open Science provides the overarching normative framework. Open Research Data is understood not only as a technical requirement for data sharing, but as a practice aimed at increasing transparency, traceability, and cumulative knowledge production in education. From this perspective, data quality, documentation, and reusability are closely linked to accountability and public trust in educational research—issues that are particularly salient in comparative and cross-national European research contexts.

Second, the study draws on participatory research and shared governance approaches in educational administration. These perspectives emphasise that research participants—such as school leaders and teachers—are not merely data providers but stakeholders with legitimate interests in how data are interpreted, shared, and reused. This lens is crucial when considering open data practices, as openness can unintentionally reproduce power asymmetries if participants are excluded from decisions about data governance. Integrating participatory principles into AI-supported data practices allows the study to examine how consent, agency, and co-ownership of data can be operationalised in Open Science.

Third, the framework incorporates democratic educational governance, focusing on how open and reusable leadership data can support evidence-informed decision-making, institutional learning, and policy development. In a European and international context, comparable and reusable data on school leadership practices can contribute to cross-country learning, policy dialogue, and capacity building. At the same time, governance-oriented perspectives highlight the risks of decontextualisation and misuse of leadership data when detached from local educational realities.

Within this framework, AI is conceptualised as a boundary object that connects researchers, infrastructures, and data users across national and institutional contexts. AI-supported processes such as anonymisation, metadata generation, and data curation are examined critically in terms of transparency, accountability, and inclusiveness. Rather than positioning AI as a solution in itself, the study treats it as a catalyst that amplifies existing ethical and governance questions in Open Research Data practices.

By combining these perspectives, the study contributes to international debates on how Open Science can be implemented in educational leadership research in ways that are ethically robust, participatory, and aligned with democratic values across European and global research communities.

This study adopts a qualitative, conceptual–empirical research design grounded in Open Science and participatory research traditions. Rather than evaluating the technical performance of artificial intelligence (AI) tools, the study focuses on research practices and methodological decision-making processes related to AI-supported Open Research Data in school leadership studies. The research is positioned within a practice-oriented reflective methodology, combining (a) systematic reflection on empirical research practices in school leadership studies and (b) expert-informed validation of methodological assumptions. This design aligns with calls in Open Science literature to examine “everyday data practices” rather than solely technical infrastructures (Fecher & Friesike, 2014; Leonelli, 2016). The empirical grounding of the study draws on school leadership research conducted in European and international contexts, particularly studies involving school principals and teachers using qualitative (e.g. interviews, focus groups) and mixed-methods designs. These types of studies typically generate highly contextual and relational data, which poses specific challenges for anonymisation, documentation, and reuse across national boundaries (Gewirtz & Cribb, 2006). Three complementary data sources inform the analysis: 1. Reflective analysis of research practices 2. Document analysis of Open Science guidelines, ethical frameworks, and data management recommendations. 3. Expert consultations with senior scholars in educational leadership research methodology To strengthen the methodological robustness and disciplinary validity of the study, expert consultation is employed as a systematic research instrument. Expert consultation is increasingly recognised as a valuable qualitative method for examining methodological innovation, ethical dilemmas, and emerging research practices (Bogner et al., 2009). A purposive sampling strategy is used to consult academic experts specialising in research methodology within education leadership, including scholars with established expertise in qualitative methods, mixed-methods designs, and Open Science practices. The selection criteria focuse on scholars who have (a) published methodological research, (b) have supervised doctoral research, or (c) served on ethics committees or editorial boards of international educational leadership journals. Semi-structured expert interviews are conducted to gather informed perspectives on: • the appropriateness of AI-supported tools in data preparation and anonymisation, • ethical risks associated with opening leadership-related educational data, • the feasibility of participatory data governance models in educational administration research, • and methodological safeguards required to ensure transparency and trustworthiness. The expert consultation serves a dual function: first, as a source of empirical insight into disciplinary norms and concerns; and second, as a form of methodological validation, enabling critical reflection on the proposed integration of AI into Open Research Data practices (Maxwell, 2012). Data from expert consultations and document analysis are examined using thematic analysis (Braun & Clarke, 2006).

This study is expected to contribute to ongoing international debates on Open Research Data in education by demonstrating how artificial intelligence (AI) can support data preparation and reuse practices in school leadership studies without compromising ethical integrity or participatory governance. One key expected outcome is the identification of critical stages in the research data lifecycle, including anonymisation, documentation, and metadata generation, where AI tools can enhance efficiency and reusability, while simultaneously requiring heightened ethical oversight. The findings are anticipated to show that AI-supported anonymisation can improve consistency and scalability in cross-national leadership research, but that it remains insufficient on its own to address risks related to identifiability in small or context-specific educational settings. A further expected contribution lies in articulating participatory safeguards for AI-supported open data practices. Drawing on expert consultations, the study is likely to reveal that meaningful openness in educational leadership research depends on the active involvement of school leaders and teachers in decisions concerning data sharing and reuse. This reinforces the argument that Open Research Data should be understood as a form of shared governance rather than a purely technical infrastructure. At the policy and governance level, the study is expected to demonstrate how ethically curated and well-documented leadership data can support evidence-informed decision-making and comparative learning across European education systems. By making leadership research data more transparent and reusable, Open Science practices, when carefully governed, can strengthen trust between researchers, practitioners, and policymakers. Overall, the study is expected to conclude that AI-supported Open Research Data practices hold significant potential for enhancing transparency, quality, and collaboration in school leadership research, provided that they are embedded within robust ethical frameworks and participatory research cultures. These insights are intended to inform future research, methodological guidelines, and European research infrastructures seeking to balance openness, responsibility, and democratic values in educational research.

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