Today, due to the rapid global changes, including technological innovation, intercultural collaboration, the integration of artificial intelligence, the teaching and learning of research methodologies in education are fundamentally reshaping. At the forefront of this transformation is generative artificial intelligence (GenAI), which enables faster management of scientific knowledge and sophisticated analysis of complex datasets (OECD, 2026). This technological shift is ushering in the era of big data and AI algorithms, capable of detecting various patterns and connections (OECD, 2026). Also, there is an increase a diverse of methods, such as experimental designs, cross-sectional quantitative studies, etc. (OECD, 2025). Under these conditions, researchers need to strengthen their research competence (European Commission, 2021), which can be achieved through continuous lifelong learning, as teaching and learning opportunities for researchers are increasingly expanding (OECD, 2024). For example, in the European Higher Education Area (EHEA), research-based teaching and researcher training are considered essential components of academic quality assurance, in line with the Bologna Process’s provisions on integrating teaching and research (European Commission, 2024). In this way, the doctoral studies and professional development of researchers must be based on the ability to transfer knowledge of research methodology to others, thereby forming a new role of the "researcher-teacher" (European Commission, 2023). Given these expanding pathways, it becomes important to understand what is already known about teaching and learning of research methodologies.

Many aspects, such as the intrinsic and extrinsic motivation of educational researchers to learn research methodologies (Gough, 1998), the personality characteristics that determine the successes and failures of learning research methodologies (Pather, 2019), the use of research methods (Wang et al., 2019) have been sufficiently analysed. Other researchers (Park, 2020; Matos et al., 2023; Argote et al., 2021; Lewthwaite & Nind, 2016, etc.) focus on the teaching and learning of research methodologies. Lewthwaite and Nind (2016) emphasise that education researchers themselves often lack confidence in their methodological skills, which affects their ability to teach effectively. It aligns with Matos et al.'s (2023) insights that, in teaching, still-dominant pitfalls such as limited pedagogical innovation (e.g., reliance on textbook-based teaching), teacher biases that may restrict learners’ critical engagement with different methodological approaches, and insufficient methodological knowledge among teachers. Meanwhile, Argote et al. (2021) point out that members of organisations often learn better from failures than successes because failures encourage a deeper search for causes; thus, analysing methodological mistakes becomes an effective way for learning.

The year 2050 may be conceptualised as the threshold of a new era, marking the emergence of novel strategies, renewed visions that respond to evolving global, technological, and socio-cultural transformations.
Looking ahead, Park et al. (2023) notice that future research methodology teaching should focus more strongly on the active experience of the learner. Moreover, in the future, soft skills such as creativity, flexibility, holistic problem-solving, critical thinking, and collaboration will remain in teaching (Park et al., 2023). Similarly, Glenn et al. (2019) advise developing creativity, critical thinking, human relations, philosophy, entrepreneurship, art, independent work, social harmony, ethics, and values in future learning. However, there is a lack of empirical studies that would reveal the views of education researchers themselves on the prospects and future development of research methodology teaching and learning. Considering these transformations, unresolved challenges, and anticipated methodological shifts, it becomes necessary to investigate how educational researchers will learn and teach research methodologies in 2050.
    The aim of the research is to discuss the teaching and learning of research methodologies for education researchers in 2050.

The participants and their sample. The participants were selected through purposeful sampling (Stratton, 2024). The participants were experts in education who were familiar with teaching and learning research methodologies: a) research methodology lecturers teaching doctoral students; b) professors/senior researchers, who are active in research; c) doctoral supervisors; d) postdoctoral supervisors; e) associate professors, who are scientific project managers and conduct scientific research, teach research methodologies; f) active researchers from other fields studying educational phenomena. Thus, in this research, 30 researchers from various Lithuanian universities participated. Data collection method. A Focus Group Discussion (FGD) is a qualitative research method and data collection technique in which a selected group of people discusses a given topic or issue in depth, under the guidance of moderators (van Eeuwijk & Angehrn, 2017). Krueger (1994) recommends using ‘mini-focus groups,’ consisting of 3–5 participants, when participants have specialised knowledge and/or experiences to discuss within the group. Thus, seven mini-focus groups, each consisting of 3–5 experts, were provided. As Onwuegbuzie et al. (2009) indicate, the ideal time for a focus group is 1–2 hours. Thus, the total time of the seven groups was about 11 hours. Focus group discussions lasted until the data saturation was reached (Saunders et al., 2018). The Focus groups discussion was conducted online via Microsoft Teams. In line with the study’s aim, the education experts were asked: How do you imagine the teaching and learning of research methodologies for education researchers in 2050? Also, during the discussions, the expression of diverse opinions of research participants was encouraged. Data analysis method. The data were analysed using reflective thematic analysis (Braun & Clarke, 2019; 2020), following six iterative steps while emphasising the researchers’ reflexivity at each stage. To disclose themes, we drew on our reflections. The disclosed themes were integrated into a narrative supported by illustrative quotes, explicitly acknowledging that meaning was co-constructed through the researchers’ reflective engagement with the data, demonstrating that reflective thematic analysis is an interpretative, reflexive process rather than a purely objective exercise. Research ethics. Research ethics were guided by Sim and Waterfield (2019), core principles: respect autonomy, confidentiality, non-maleficence, beneficence, justice and researcher responsibility. In the informed consent process, participants were clearly informed about the unpredictable and public nature of group interaction and the inherent limits of confidentiality. Each participant provided consent to participate in the study. This research was approved by the Ethical Committee of the university.

This research identified key themes in teaching and learning based on the research participants’ opinions. The first theme, "The teacher as a methodological curator and coach," reflects participants’ view that in 2050, teachers will act as personal mentors, helping learners reach their potential. The second theme, "Teaching through 'living labs and real-time data," discloses participants’ thoughts that teaching will be conducted in "living laboratories" here and now, where technology, researchers, and research participants become co-authors. The theme "AI integration in teaching" underscores that artificial intelligence will become a constant research partner, and its management will become a basic competence for researchers. "Value backbone: Ethics as continuous practical reflection" emphasises ethical sensitivity, in which ethics becomes a daily mindset for the researcher. The first learning theme, "Personalised and self-directed learning trajectories," reveals participants’ opinion that learning will become fully individualised, responding to the cognitive and emotional needs of the learner. The theme "Learning symbiosis with AI assistant" discloses participants’ beliefs that in 2050, AI will handle "black" work, letting researchers focus on methodological creativity." The third theme, "Methodological 'Jazz' and hybridity," emphasises that learning will be understood as the ability to improvise (jazz) with strong basic foundations. This learning will create unique, hybrid research designs and integrate various interdisciplinary methods. The theme ""Unlearning" and returning to basics" discloses that in 2050, researchers will "unlearn" technical subjects to return to fundamental values: critical thinking, philosophical logic, human intuition. Practical value: The results of this study will be applied to a Delphi study. This will enable the creation of teaching and learning scenarios for educational researchers in 2050. The findings can be used for strategic planning, curriculum innovation, and policy-oriented decision-making in researcher development programs. It will help education researchers navigate the future of methodological learning in a dynamic AI-enhanced educational environment.

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