Within higher education, students’ autonomy in connection with degree projects is highly valued and research indicates relationships between autonomy and motivation, which are essential for both academic achievement and lifelong learning. However, it is unclear what autonomy means and how it is promoted in tutoring. This study aims to obtain knowledge about how to enable a learning environment in tutoring that develops students’ autonomy during their work with the degree project. Through phenomenology, the essential meanings of students’ autonomy in their degree projects are described. Data is gathered through focus-group interviews with both students and tutors in various educational programs. The results indicate that autonomy develops in a supportive process balanced by the tutor between control and freedom and requires frameworks such as space, knowledge and time. The student’s self-awareness and the tutor’s knowledge of the student’s individual needs facilitate the student’s autonomy.

The integration of Artificial Intelligence (AI) into academic writing has sparked mixed reactions, from optimism about enhanced support to concerns over deskilling and ethical dilemmas. AI-driven tools offer personalized feedback, self-regulated learning, and writing assistance, but also present risks of over-reliance and loss of critical skills. This panel moves beyond simplistic debates to explore AI’s evolving role in shaping university writing competencies.

By bringing together international perspectives and empirical research, this session examines how AI is transforming writing practices, its cognitive and pedagogical implications, and the necessary adaptations in higher education.

Key Themes and Contributions

This discussion panel gathers experts from France, Switzerland, Portugal, Singapore, and Sweden to explore the impact of AI on academic writing practices, literacy development, and teacher education.

1. University Writing Practices and AI: Opportunities and Challenges

Philippe (France, écri+) will analyze the links between educational background and writing performance, drawing on his experience as a référent for écri+. He will present institutional strategies, student needs, and faculty concerns regarding AI’s role in academic writing.

2. AI as a Writing Aid: Supporting or Undermining Skills?

Gaëlle Molinari (Switzerland, TECFA, University of Geneva) will discuss how AI-based tools impact academic competencies and collaboration. She will introduce the concept of hybrid intelligence, emphasizing the co-evolution of human and AI capacities in writing.

3. AI in Writing Feedback: Enhancing Evaluation

Marie-Paule Jacques (France, écri+) will explore AI’s potential to provide detailed, student-centered feedback, improving upon traditional approaches. She will also address the risks of AI dependency and maintaining human oversight in evaluation.

4. AI and Self-Regulated Learning

Chue Kah Loong (Singapore, National University of Singapore) specializes in educational assessment and will discuss:

• AI’s role in self-regulated learning and student autonomy.
• Potential risks of over-reliance on AI-generated suggestions.
• Insights from Lee et al. (2025) on AI’s effects on cognitive effort and confidence in writing.

5. Linguistic and Epistemological Perspectives on AI in Academic Writing

Helena (Portugal, University of Lisbon) will examine AI-generated writing from a linguistic and epistemological perspective, discussing:

• The impact of AI on universities as spaces of discursive production and the role of linguists in this transformation.
• The limits of transparency and equivalence in natural languages, challenging the assumption that language is a direct representation of thought.
• Ethical considerations in linguistic epistemology, questioning formal models that predict alignment between language structure and cognitive processes.
• The challenge AI poses for contemporary linguists, particularly in fostering research and language learning conditions.
• A critique of current educational models, contrasting them with an emerging paradigm that promotes critical thinking, creativity, and questioning over efficiency-driven skill optimization

Demonstration using written data from primary, secondary, and university students in Portugal.

6. Challenges for Educators in an AI-Driven Landscape

Marita Cronqvist (Sweden, NW10 – Teacher Education Research) will highlight:

• The need to rethink writing instruction in the AI era.
• How AI affects academic integrity and writing assessment.
• Strategies to guide students in responsible AI use.

Discussion and Expected Outcomes

This interactive session will engage participants in a critical discussion on AI’s dual role in academic writing. Key questions include:

• Does AI enhance or hinder student writing quality?
• How can institutions integrate AI without diminishing critical writing skills?
• What role should educators play in guiding AI use?
• How do we balance AI support with maintaining academic integrity?

Aligning with EERA networks NW16, NW10, NW01, and NW31, this session will offer insights for educators, researchers, and policymakers, paving the way for future collaboration, including contributions to the AI-focused summer school planned for 2026.

At ECER 2024 the presentations of two projects on identifying and analysing thematic trends and clusters of the contributions to ECER since 1998 gained much attention and resulted in interesting and fruitful discussions. Both presentations utilized topic modelling (Blei et al., 2003; Griffiths & Steyvers, 2004) to determine the central content-related themes of over 30,000 contributions ranging over 25 years. At its core, topic modelling is a natural language processing and machine learning method clustering documents in regard to their thematic similarity based on the words occurring in the documents frequently (together).

While both projects utilized topic modelling for analysing the same corpus, they differed in their central goals and approaches to interpreting the results: The presentation by Hoveid et al. (2024) utilized a more qualitative approach to analyse and interpret the results of a model consisting of k = 100 thematic topics to determine, assess and compare thematic trends of ECER since 1998. The second project by Schindler et al. (2024) was based on the development of the interactive web app EduTopics: ECER (Christ et al., 2024; url: https://dipf-lis.shinyapps.io/EduTopicsECER/) which enables users to explore clusters and trends within the corpus. This was made possible by providing univariate and multivariate interactive and manipulatable visualizations for bibliographic parameters of the contributions (EERA network, authors, country of affiliation) and their relevance for the k = 50 thematic clusters resulting from the topic modelling approach. During the research workshop on the app, examples of applications from various (sub-)disciplinary or EERA network perspectives were presented and discussed. The following discussion contained several relevant aspects for further developments of the app’s functionalities or application areas.

This research workshop addresses both aforementioned aspects by including presentations on (a) new developments regarding the functions and visualisations and (b) highlighting use-cases of the app from various different EERA network perspectives.

The first presentation is on new developments regarding the app including an update to the corpus with the ECER 2025 contributions. Updates to existing functions as well as new functions are presented, especially the new and improved hierarchical topic modelling approach, which addresses the main point of contention during the discussion in 2024 on the feasibility of clustering over 30,000 documents in only k = 50 topics (see methods and conclusion sections).

Another presentation is on the participation at ECER conferences across the years. This presentation aims at the identification of predictors for explaining the variance within the contribution numbers of affiliation countries across the years.

Two additional presentations are based on the application of EduTopics for research use-cases:

The first use-case focuses on clusters, trends and central nodes of research on teacher education at ECER both within network 10 “Teacher Education Research” and beyond.

The second uses EduTopics to develop an overview of trends and clusters of contributions from network 22 “Research in Higher Education”.

The final presentation gives a brief look at the future of EduTopics including the integration of additional data sources for comparative analyses of different conferences and national databases on educational research.

Finally, ample time for discussions and feedback on the presentations and the app is allocated to gather further information for additional (technical) developments, use-cases and applications of the app.    

Method

Presentation EduTopics: ECER 2025 Addressing the issue on the fit of a small number of topics and a large number of contributions, a hierarchical, nested topic modelling approach is utilized by simultaneously computing (1) a model with a large number of topics (between k = 200 and k = 300) capturing smaller thematic clusters giving the heterogeneity of educational research justice and (2) a broader model with a smaller number of topics (around k = 50), which enables an efficient overview over the central themes within the corpus. The similarities between the topics of both models are determined by aggregating various distance and similarity measures both on the document and word level (Aletras & Stevenson, 2014; Blair & Mulvenna, 2020). The aggregated similarity measure is utilized to determine the nesting of the subtopics (of model 1) within the supertopics (model 2). This hierarchical representation of the themes is utilized to determine which smaller, more detailed topics are most similar to the larger, broader topics. This enables users to gain insight into broad, overarching themes and their more fine-grained sub-topics such as varying trends of the topics “special needs education” and “inclusive education” in the overarching topic “special needs and inclusive education”. Presentation Country-Level-Data: Potential predictors, like size of national higher education systems or distance to conference venue, for the longitudinal and cross-sectional variance within the number of contributions by country are identified, presented and tested regarding their incremental explanation value within a multi-level regression model. The country-level regression intercepts – i.e. the controlled contribution rate – and the country-level longitudinal slope factors are then used to identify factors leading to in- and decreases of contributions by countries over the years. Presentations Use-Cases EduTopics: Both presentations will use the available functions of the app to determine clusters, trends and potential desiderata within their respective foci on networks and themes. Those include univariate trend analyses of contributions by EERA networks and trends of the thematic topics, relationships between networks and topics via the graph visualisations and relationships between the overarching themes (from model 1) and the detailed themes (from model 2).

Expected Outcomes

The presentations in this workshop give insight into the current and future development of the app EduTopics and use-cases of the app specifically but also of natural language processing and machine learning in general for educational research purposes. By applying different perspectives during the different presentations on the apps’ functions, results and visualisations, exemplary use cases and avenues for future research are highlighted. In addition, the presentations on the use-cases of the app show the potential of the combination of different research approaches. On the one side, the app opens up new ways for researchers to access data, which may not have been possible before. On the other side, the presentations of the use-cases of the app provide important feedback for the development of the underlying algorithms, (visual) presentations of the results and for the user interface and its accompanying explanations. Furthermore, the discussion will focus on current and potential future challenges for modelling heterogeneous literature corpora such as data quality, multilingualism or limitations of the applied methods in itself. We welcome additional feedback from the audience during the discussion and are open to answer ad-hoc research questions posed by audience members.

This article, based on Miranda Fricker’s concept epistemic injustice to deal with human rights and social sustainability, argues that teacher education must address the twofold mission to both enhance epistemic justice for student teachers and to develop their skills to enhance justice for future pupils. Thus, development of student’s ethical leadership involving transformative teaching is in focus. Based on previous research, ethical leadership is focused on self-awareness, character formation and modelling. This focus involves both verbal and bodily expressions. Verbal expressions are reinforced and developed through conceptualization, dilemmas and ethical codes while bodily expressions are reinforced and developed through modelling. In terms of modelling, Merleau-Ponty's theory of how people's embodied experiences shape their life worlds provides perspective on opportunities to develop transformative teaching to challenge norms and enable change. Empirical data from a previous phenomenological study on professional ethics in teacher education exemplifies teaching practice related to epistemic justice.

Within descriptive phenomenology, participants’ lived experiences provide data about the studied phenomenon, in this case academic freedom in teacher education in a tension between certainty and uncertainty. The current phenomenon was examined in a phenomenological study about preservice teachers’ possibilities to develop critical and autonomous thinking in their practice. This Case Study addresses the process, advantages, and challenges through the analysis to be able to formulate the essence of the phenomenon. The phenomenological analysis and formulation of an essence is a complex process, and this case is therefore aimed primarily at postgraduate students but will be useful to anyone doing phenomenological research. In the study, knowledge is given about how the essence emerges through the analysis and can be formulated as a new whole, knowledge that can be used in one’s own phenomenological studies.

One way of charting the way forward is to research potentials to educate through various options that today’s technology can offer. Virtual reality (VR) using 3D glasses is one example and we know from earlier research that learning in this way is experienced as joyful by students. In a European perspective, the use of technology in a pedagogical way to stimulate learning needs attention. Despite fairly common prerequisites in Europe, it is not obvious how technology can be used to not only be joyful but also enhance learning and develop the learning environment. In this specific study, the safe environment in a virtual reality lab (Tatli & Ayas, 2010) offers an alternative to the real physical lab when students in textile education are studying inkjet printing. The aim is to gain knowledge about how the learning of the inkjet printing process takes place in the traditional physical lab and in virtual reality lab. The research questions are: 

• What characterizes the learning conditions in a lab and VR environment respectively?
• What characterizes the learning through physical lab work and in VR respectively?
• What improvements do the students suggest for the VR app?
• What happens to learning when lab and VR experiences are combined in different sequences?

Closely related to the security that virtual environments offer is the joy that arises (De Vries & May, 2019; Makransky et al. 2019), for example, from students being able to repeat stages in a process as long as they want and need without risking destroying anything. The positive feelings increase students’ confidence in their own ability (Sarmouk et al., 2019) and thereby, motivation for learning is strengthened. Not least intrinsic motivation, based on Self Determination Theory, meaning that the driving force comes from the students themselves instead of, for example, parents or teachers, is desirable. The result in a study by Makransky et al. (2019) show that the use of VR provides increased internal motivation. Comparing VR and physical labs, the conclusion in most studies is drawn that VR constitutes a valuable complement to the physical lab (see for example Sarmouk et al., 2019; Vahdadikhaki et al., 2024). However, there are researchers claiming that VR labs in some cases are more effective than physical labs (Chan et al., 2021). Training in VR as preparation for students to handle the real lab better contributes to both cognitive and emotional aspects meaning that they increase their self-confidence (Sarmouk et al., 2019). Other researchers, for example Moozeh et al. (2020) show that VR is not only preparing students but also gives them an opportunity to integrate and apply knowledge from the physical lab in the VR context. Apart from the research showing that safety, joy and motivation through VR stimulate students' learning, there are no clear results about what characterizes learning in the virtual environment. Learning in areas such as processes, concepts, practical skills and analytical skills can according to some studies (for example Garcia Estrada & Prasolova-Førland, 2022) be facilitated by VR while other studies (for example Sarmouk et al., 2019) show that students can become accustomed to using the equipment through VR. However, it is not only the design of the specific VR app that is essential for learning. In addition, factors such as pursuit of realism (Vahdadikhaki et al., 2024), the implementation in terms of, for example, instructions, support (Chan et al., 2021) and adaptation to students' individual needs (Yang et al., 2023) also matters for learning.

Method

Descriptive phenomenology and specifically the approach Reflective Lifeworld Research (RLR) (Dahlberg et al., 2008; Giorgi, 1997) has been used to study how the learning of the inkjet printing process takes place in the traditional physical lab and in virtual reality lab respectively. The current phenomena, i.e. those that manifest themselves, are both learning that takes place in a physical lab and learning that takes place in VR. Within descriptive phenomenology, only basic theories on which phenomenology generally rests, for example life-world theory (Husserl, 1970/1936) are used. It is a conscious choice to avoid other theories with the aim of reinforcing that the lived experiences of the participants must be central in the search for new knowledge. Data collection was conducted via five focus group interviews with students, four of them at bachelor’s level and one group at master's level, a total of 12 participants. The participants were expected to experience learning through both lab and VR and therefore were offered both for ethical reasons. Informed consent had been collected previously. Lab and VR were scheduled in different orders and interviews at different times enabled variations in the participants' experiences. To avoid that the learning VR experience was prevented by novice equipment issues, all participants had an opportunity to try VR glasses with Demo app before using the specific app. In phenomenological research, many different contexts and variations are desirable in the data and in this study, variation was offered through schedule, different study levels, gender and ages. The variations enable examination of what, despite all the differences, are common characteristic features of the phenomena (Dahlberg et al., 2008). The analysis was conducted in several different steps, all of which are characterized by openness and reflection so that the researcher's previous knowledge of the phenomenon is bridled during the process (Dahlberg et al., 2008). After reading the data, meaning-bearing units, which can consist of words, sentences or whole paragraphs are marked (van Manen, 2014). Patterns, called clusters are then sought among the units so that the meaning of the phenomenon can eventually be formulated as a new whole on an abstract level.

Expected Outcomes

The result shows that learning in both physical and VR lab evoke emotions. The students experience joy in both labs but express their motivation more clearly based on the experience of using VR glasses. The physical lab is experienced as more serious based on all safety instructions, protective clothing and more. The VR lab is more playful and because mistakes have no consequences, a kind of trial-and-error behavior develops. The instructions in the physical lab were more detailed and were perceived as very clear and easy to follow. In addition, there was the possibility to always ask a teacher. In VR, the instructions were not that specific and disappeared after a while.The students also felt that they were left to fend for themselves without having anyone to ask. In VR lab, different parts of the process can be practiced again and again without any material being destroyed, which offers students opportunities for extensive training that might be too costly in physical labs. An advantage of the physical lab is that the students experienced the process with many different senses. They understood how the ink worked by observing how particles got stuck in the filter and by handling the ink with their own hands. Learning differs in the various labs and aspects such as being able to use one's senses are difficult to achieve via VR, but for example the perceived uncertainty through unclear instructions could be remedied without major efforts. There is thus good potential for learning through the virtual lab and regardless of order, students need both labs as they offer different kinds of learning.

Virtual reality (VR) is an opportunity in today's higher education learning that risks ending up in a polarized approach where teachers are either for or against it. Instead, we need nuanced discussions about what VR can add to learning, cognitively and emotionally. In research, there are examples of how VR is used as a substitute for or as a complement to physical laboratories. The advantages of using a virtual laboratory are for example that certain rather costly and risky processes in physical laboratories can be handled safely and without wasting resources, something that promotes a sustainability perspective. In addition, research shows that the use of virtual environments sometimes simulates gaming experiences, which can both arouse positive emotions and increase motivation. Something that needs to be considered, however, is that the learning that takes place in the virtual and physical laboratories is different and that strong negative emotions can also arise.

In education, there is a risk that joy in learning is counteracted by allowing a performance culture to dominate. Research shows that emotions are of great importance for results, motivation and well-being. This study aims to add knowledge about the essential meanings of joy in learning based on students' lived experiences and thereby implications for the learning environment. The essence of the phenomenon of joy in learning has been formulated through descriptive phenomenological analysis. Qualitative data consists of 25 narratives from students engaged in voluntary forms of education. The study shows that joy in learning emerges throughout the learning process, when students discover that they gain knowledge, understand and can control their learning process and achieve something with their knowledge. The implications for teaching involve awareness of the learning process providing a balance between structure, support, challenge and personal choice which was valued along with relationships that contribute to autonomy. 

In all research, ethical considerations are crucial to reliability and quality and researchers are guided by various national and international documents and ethical committees. Despite different strategies to guide researchers and to ensure quality, there still seems to be uncertainty in educational science about how research ethics should be positioned and handled in practice. The aim of this study is to phenomenologically explore what meanings the phenomenon research ethics are given in Swedish doctoral dissertations in educational research based on how doctoral researchers position, frame and present research ethics in their ethical elaborations. The empirical data consists of 60 doctoral dissertations in educational science at Swedish universities from the past year. The result indicates very different meanings of ethical considerations despite a quite common point of departure in the Swedish Research Council’s guidelines and knowledge of the Ethics Review Act. Some variations can be related to the differences in the studies’ designs, but regardless of such explanations, the conclusion is that consensus regarding ethical considerations in research is largely lacking.