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This study aims to develop a set of heuristics tailored for evaluating learning analytics in simulation-based professional learning, focusing on the following research questions: (1) What heuristics are appropriate for evaluating learning analytics in simulation-based professional learning contexts? (2) How can theoretical frameworks and empirical findings be combined in the development of such heuristics? (3) How can expert evaluation inform their refinement and applicability? The study combines a top-down approach, drawing on a theoretical framework for learning experience design, with a bottom-up analysis of empirical findings from prior studies in the context of a design project. An initial set of heuristics was iteratively reviewed and refined in collaboration with experts in user and learning experience design. The outcome is a detailed heuristic framework that supports the evaluation of learning analytics in simulation-based settings and accounts for the technological, pedagogical, and social dimensions of professional learning.

Background The metaverse has the potential to transform healthcare and healthcare education by offering immersive, interactive experiences. As research on the metaverse rapidly expands, a synthesis is required to understand its current state, trends, and future directions in healthcare. Methods We conducted a mapping review of existing review studies on the metaverse in healthcare using topic modeling, hierarchical clustering, and qualitative interpretation. This was complemented by computational text analysis to examine thematic developments and structural patterns. Results The analysis yielded three distinct clusters: (1) immersive therapeutic and educational applications with intelligent integration, (2) immersive technologies for surgical training and clinical simulation, and (3) integrated, immersive, and intelligent technologies for personalized, networked healthcare. These clusters illustrate a shift from conceptual exploration toward applied, system-level integration. Applications show promise in mental health, surgical education, and personalized care, among others, but the evidence is preliminary. Key risks include privacy concerns, governance gaps, and equity challenges. Conclusions As enthusiasm for metaverse technologies grows, it is crucial to ensure that optimism does not outpace evidence and readiness. The metaverse offers significant opportunities for human-centered healthcare and professional training, but it requires rigorous validation, ethical frameworks, and inclusive design. To ensure responsible adoption and a sustainable impact, it is critical to align developments with the WHO's strategic objectives of collaboration, implementation, governance, and human-centered systems.

Hannes Lundkvist och Per Apelmo beskriver i sin text tillämpandet av en modell, på samma gång pedagogisk och vetenskapligt metodologisk, av författarna identifierad som en gestalt vilken de kallar ”musik-dikt-tystnad”. Gestaltens sammanhang kan vara pedagogiskt eller terapeutiskt. Utgångspunkten är deltagarna själva och deras livssituationer, den ”intra-aktion med andra människor, ting, ljud, känslor, erfarenheter” i vilken de som människor alltid befinner sig och blir till.

As the digital landscape of education continues to evolve, learning analytics has become an integral tool for understanding, measuring, and improving student outcomes. However, a significant gap remains in the design and implementation of these technologies, particularly in their ability to cater to the diverse cognitive and neurological profiles of students. This conceptual paper highlights the need for a neuroinclusive approach to learning analytics, arguing that current practices in educational technology risk excluding neurodivergent students by reinforcing a neurotypical-centric design. In doing so, the paper highlights the limitations of existing frameworks and offers suggestions for how learning analytics can become more inclusive to better serve all students, especially those with undiagnosed or undisclosed neurodivergence.

Interest is growing in how private–public policy spaces shape Education Technology (EdTech), not only through Big Tech, the rise of edu-businesses, and EdTech brokering (Candido et al., 2023; Ortegón et al., 2024), but also through the hybrid and networked forms of EdTech policy events (Decuypere et al., 2024; Lewis, 2023). EdTech trade fairs act as policy network events that mobilise actors and resources, highlight power asymmetries, and legitimise particular agendas for school digitalisation (Player-Koro et al., 2018, 2022; see also Gulson & Witzenberger, 2022, for AI policy). This paper draws on a decade of ethnographic research conducted at Swedish EdTech events within the Nordic context, recognised as significant policy spaces since 2013. It synthesises over ten years of event ethnography by addressing both the epistemic and organisational policy dimensions. First, it examines how educational problems are framed, transformed, and disrupted argumentatively, also relatively Nordic welfare transformations. Second, it analyses the mobilities and temporalities of EdTech policy events (Peck & Theodore, 2015; Lewis, 2023). Theoretically, EdTech events are conceptualised as immersive, hybrid (online/offline) and real-time policy enactments. The analysis draws on policy mobility approaches (Lewis, 2023), situating EdTech events within broader transformations of welfare governance and private–public policy networks. In the findings, particular attention is given to the epistemic and methodological implications of EdTech’s recurring “crisis” responses, evident during the pandemic and its aftermath. Such responses now drive policy programmes and techno-solutionism around educational performance, digital backlash, cybersecurity, and Generative or “Responsible” AI. Key actors include education ministries, NGOs, and global actors Microsoft, Google, Open AI, the OECD and UNESCO. Alternative policy formations emphasising reparation, care, and democracy (Sriprakash et al., 2024) are also discussed in relation to the findings. References: Candido, H. H. D., Seppänen, P., & Thrupp, M. (2023). Business as the new doxa in education? An analysis of edu-business events in Finland. European Educational Research Journal, 23(1), 48-71. Decuypere, M., et al. (2025). Tracing the infrastructural unfolding of (edtech) events through hybrid team ethnography. Learning, Media and Technology, 1-15. Gulson, K. N., & Witzenberger, K. (2020). Repackaging authority: artificial intelligence, automated governance and education trade shows. Journal of Education Policy, 37(1), 145–160. Lewis, S. (2023). (Re)drawing Lines in Our Research: Using Policy Mobilities and Network Ethnography to Research Global Policy Networks in Education. ECNU Review of Education, 6(4), 646–653. Ortegón, C., Decuypere, M., & Williamson, B. (2024). Mediating educational technologies: Edtech brokering between schools, academia, governance, and industry. Research in Education, 120(1), 35–53. Peck, J., & Theodore, N. (2015). Fast policy: Experimental Statecraft at the Thresholds of Neoliberalism. University of Minnesota Press. Player-Koro, C., Bergviken Rensfeldt, A., & Selwyn, N. (2017). Selling tech to teachers: education trade shows as policy events. Journal of Education Policy, 1–22. Player-Koro, C., Jobér, A. & Bergviken Rensfeldt, A. (2022). De-politicised Effects with Networked Governance? Ethnography and Education 17 (1), 1–16. Sriprakash, A., Williamson, B., Facer, K., Pykett, J., & Valladares Celis, C. (2024). Sociodigital futures of education: reparations, sovereignty, care, and democratisation. Oxford Review of Education, 1–18.

Generative AI (GenAI) is transforming higher education not only as a technical and epistemic phenomenon but as an affective infrastructure that shapes how students and educators experience learning, authorship, and accountability. This paper explores AI guilt as affect – the shame, anxiety, or ambivalence surrounding AI-assisted academic work – as an emergent element of socio-technical assemblages. Unlike previous studies that focus on AI-related guilt and value judgments from an individual (student or educator) perspective (e.g. Giray, 2025; Lindell & Stöhr, 2025), this study situates affects within socio-technical infrastructures, showing how GenAI assemblages emerge through entanglements. Disentangling the assemblages of GenAI technologies, policies, and institutional norms, reveal the tension between ideals of autonomous academic subjectivity and the computational realities. The aim is to understand how affective dynamics around GenAI articulate moral economies of higher education, distributing responsibility, vulnerability, and agency across human/non-human relations. The analysis draws on Science and Technology Studies to frame affect as relational, performative, and infrastructurally produced. Guilt and vulnerabilities around AI are not simply felt; they are performed and circulated through educational norms and socio-technical systems (Bosworth, 2023). Following Ahmed (2014), these affects align bodies and practices with institutional moral expectations. The concept of cruel optimism (Berlant, 2011) similarly can illuminate how attachment to ideals of originality, autonomy, and merit becomes affectively fraught under GenAI. Methodologically, the study adopts critical proximity as a stance of situated inquiry, engaging with GenAI ‘from within’ the affective assemblage. The empirical material comprises: (1) university AI policy campaigns, which articulate ethical responsibilities and acceptable use; (2) materials from GenAI service providers, which promote trust, innovation, and threat; and (3) documents from university disciplinary boards, including examples of AI-related academic misconduct, formalising guilt and accountability. Overall, the Nordics provide an interesting case of early institutional responses to GenAI, strong institutional autonomy and student-educator trust. The materials are analysed as affective nodes, tracing how moral and emotional intensities are both produced by and co-produce infrastructures (Bosworth, 2023). Recognising AI guilt as infrastructurally produced and engaging critically with these socio-technical assemblages allows reflexive critique, and interventions that reshape both practices and infrastructures. The findings indicate that AI guilt functions as an affective infrastructure; a distributed system through which academic subjects and affects are produced, governed, and emotionally invested. Feelings of guilt, shame, or anxiety emerge not from individual failings but from the relational tensions between use and non-use of GenAI, autonomy and automation, ethics and exploitation. In this way, AI guilt is a part of an affective infrastructure of invisible exploitations, including institutional dependency on corporate GenAI platforms, and environmental costs of energy-intensive computation. Ahmed, S. (2014). The Cultural Politics of Emotions. Edinburgh University Press. Berlant, L. (2020). Cruel optimism. Duke University Press. Bosworth, K. (2023). What is ‘affective infrastructure’?. Dialogues in Human Geography, 13(1), 54-–2. Giray, L. (2024). AI Shaming: The Silent Stigma. Annals of Biomed Eng, 52(9), 2319–2324. Lindell, T., & Stöhr, C. (2025). Navigating generative AI in higher education–Six near future scenarios. Learning, Media and Technology, 1–16.

This chapter critically examines how datafication and its infrastructures of platformization, cloudification, and AI are reshaping education across diverse contexts. Rather than assuming that data-driven agendas unfold uniformly, the analysis follows Critical Data Studies to make visible how local arrangements and resources intersect with global alignments of digital infrastructures. Drawing on empirical examples from the Reconfigurations of Educational In/equalities in a Digital World (RED) project in Latin America, Africa, and Europe, the chapter highlights the situated consequences of datafication’s impacts. It explores the paradox of being “data poor”: exclusion from both opportunities and risks of data exploitation, which simultaneously entrenches marginalization and renders the datafied world less representative. By foregrounding the politics of educational data, the chapter argues that digital infrastructures are not neutral but performative, producing new inequalities while reconfiguring power relations between states, corporations, and schools.

With focus on the sensory aspects of instruction and instruction following this chapter explores firefighting exercises in basic safety training for maritime students. Drawing on ethnomethodology and conversation analysis (EM/CA) the aim is to investigate the collaborative production of bodily knowledge in settings where embodied skills are being trained through hands-on-exercises at maritime training centres. Through an analysis of audio and video recorded firefighting exercises, the dynamic interplay between embodiment, sensory perception, and language is scrutinised. In our empirical material, the participants have restricted sensory access to each other as well as to the environment, which changes the interactional resources for constructing a shared social order. Our analysis highlights how instructions pertaining to a shared sensorial social order are accomplished in practice, and how extreme sensory conditions characterise both the setting in which training takes place as well as constitute the focus for instruction and instruction following in this setting. In firefighting exercises, training the hand to interpret haptic and thermoceptive information plays a central role in learning to handle emergency situations.

This article examines the rise of platform bureaucratization in highly digitalized classrooms. Drawing on an ethnographic study in a Swedish lower secondary school, we examine how teachers and students navigate digital platforms, integrated into routine classroom activities such as attendance management, assignment submission, and plagiarism detection, giving rise to bureaucratic forms of teaching and learning. Employing Sefton-Green’s concepts of textualization, templatization, and trainability, we explore how platforms impose rule-bound, procedural logics onto educational practices. Our findings suggest that these platform-driven bureaucratic processes, although seemingly mundane, reconfigure teacher-student interactions, redistribute pedagogical authority, and introduce complex layers of administrative work into everyday schooling. The article highlights the critical role of teacher knowledge in navigating, interpreting, and occasionally resisting platform-driven standardization, emphasizing the need for educational approaches that critically engage with the political and normative dimensions of platform infrastructures. By conceptualizing platform bureaucratization as pedagogy, the study contributes to discussions on the implications of educational platformization, calling for pedagogies that equip teachers and students to navigate and shape these evolving digital bureaucratic landscapes critically.