Generative AI (GAI) is rapidly reshaping higher education, introducing both transformative opportunities and significant challenges across teaching, research, and administration. While GAI offers potential benefits, such as enhanced productivity, personalized learning, and automation of routine tasks, it also raises concerns about academic integrity, ethical governance, and organizational readiness. Understanding how stakeholders perceive these changes is essential for developing strategies that balance innovation with responsibility. This study explores stakeholder perceptions of GAI through a SWOT analysis of 167 contributions collected via Padlet during webinars at two Swedish universities, attended by 152 higher education personnel. Thematic analysis revealed a nuanced landscape of views: strengths include capabilities for content creation, translation, and transcription, which can streamline workflows and support collaborations; weaknesses include inaccuracies, lack of system transparency, and user dependency, highlighting risks of misinformation and the need for advanced skills in and knowledge about AI tools; opportunities include supportive roles, outsourcing repetitive tasks, and partnering with AI for innovative teaching practices; while threats include destabilization of educational norms, ethical issues regarding data privacy and copyright, and organizational challenges such as workforce adaptation and brand reputation. In addition to mapping SWOT themes, the study identifies stakeholder requests for clear institutional guidelines, targeted professional development, and allocation of time and resources to facilitate responsible adoption of generative AI. These insights inform the development of a novel Balanced Scorecard (BSC) framework, translating study findings into actionable strategies across four perspectives: student or customer, internal business, innovation and learning, and financial. By bridging stakeholder perceptions with strategic planning, this study contributes a practical roadmap for higher education institutions seeking to harness the benefits of GAI while mitigating associated risks. The proposed framework underscores the importance of aligning technological integration with ethical, pedagogical, and organizational considerations to ensure long-term value and trust in academia. 

The fast development of immersive virtual reality (iVR) offersinnovative teaching methods, particularly in history education. Research hasexplored iVR’s pedagogical benefits and challenges, but there are few studieswith a focus on design of virtual environments to support history education.This scoping review explores the potential of improving teaching and learningthat has been presented in previous studies of iVR design. While some iVRapplications have been designed considering the potential to provide narrativeto the user, fewer were designed to support critical thinking and methodologicalapproaches to history as a science. This study underscores the need for furtherdevelopment in iVR design for critical and scientific thinking and providessuggestions for how to integrate iVR into history education. The conclusion ofthe study was that without thoughtful design, iVR would add limited value tolearning.

History education faces challenges engaging students due to its complexity,hindering understanding of key concepts like causality or multiperspectivity.Immersive virtual reality (iVR) is being broadly implemented in heritageinstitutions, and some history teachers are beginning to explore its potential tosupport learning. However, insufficient technological-pedagogical knowledgecomplicates its implementation while the competencies needed for K-12 historyteachers remain unexplored. Knowledge required to effectively implement iVR inhistory education is presented, aiming to discuss a model covering the various areasthat should be developed for successful history education through iVR.

An  increasingly  popular  subcategory  of  Artificial  Intelligence  (AI)  is  Generative  AI (GAI),  which  encompasses  technologies capable of creating new content, such as images, text, and music, often resembling outputs made by humans. The  potential  impact  by  GAI  on  sustainability  is  multifaceted.  On  the  positive  side,  generative  AI  can  aid  in  optimizing  processes, developing innovative solutions, and identifying patterns in large datasets related to sustainability. This can lead to  more  efficient  resource  management,  reduced  energy  consumption,  and  the  creation  of  more  sustainable  products.  However,  there  are  also  potential  negative  impacts,  such  as  increased  energy  consumption  associated  with  training  and  running  generative  AI  models,  as  well  as  the  potential  for  unintended  consequences  or  biases  in  the  generated  content.  Additionally,  overreliance  on  generative  AI  may  lead  to  reduced  human  oversight,  which  could  undermine  holistic,  interdisciplinary, and collaborative approaches to sustainability. The aim of this paper is to explore the potential impacts on sustainability by generative artificial intelligence through a review of prior research on the topic. The study was conducted with a scoping literature review approach to identify potential impacts by generative AI on sustainability. Data were collected through  a  search  in  the  database  Scopus  during  the  spring  semester  of  2024.  Keywords,  relevant  for  the  study,  were  combined  with  Boolean  operators.  Papers  identified  through  the  search  underwent  a  manual  screening  process  by  the  authors, in which papers were selected for inclusion or exclusion in the study based on a set of criteria. Included paper were then analyzed with thematic analysis, according to the guidelines by Braun and Clarke. A categorization matrix, based in prior research  on  sustainability,  supported  the  analysis  and  deductive  coding  of  collected  data. Results  of  the  study  highlight  generative AI’s potential impact on sustainability that relate to both environmental aspects, economic aspects, and social aspects of sustainability. These different aspects of sustainability impact make this research an important contribution for deepening  the  understanding  of  generative  AI  and  its  potential  consequences  for  society.  Findings  of  the  study  provide  theoretical  contribution,  implications  for  practice,  and  recommendations  for  future  research  on  generative  AI  and  sustainability. 

To carry out a literature review often involves hard and tedious work. There is a tradition of using facilitating tools, that extended to the AI field in 2018 when iris.ai appeared. Today, in the emerging field of Generative AI tools based on Large Language Models, there has been rapid development of new literature search tools and approaches. This study has the aim of exploring this vast array of Generative AI tools, in a literature study where some of the found tools were used to facilitate the selection of relevant publication. Three research questions guided this study: RQ1) "What Generative AI tools can be found in literature?", RQ2) "Which of these tools could be of use in the literature review conducted in this study, and how?", and RQ3) "What are the ethical aspects of using Generative AI tools in literature studies?” The approach has been a scoping review, built around a search that combined the keywords: "AI supported", "AI generated", "AI based" and "Literature review". An initial result set was filtered with inclusion exclusion criteria in a strive for an interesting quality answer to the research questions. However, most publications that passed the filtering lacked any potential to contribute to answer the research questions. The most interesting finding in the first search was a hint about the new feature 'Scopus AI'. A new search with the Scopus AI tool resulted in a small but very relevant set of publications. These publications were analysed in a deductive inductive thematic analysis, and primary sorted into the categories of: 'Generative AI Tools', 'Supportive AI Techniques', and 'Ethical Issues'. Findings indicate that there is a wide variety of tools that can facilitate the skimming process of a literature, and to provide adequate summaries of retrieved publication. However, authors recommendation is to keep the tools on the facilitating support level, and that the main analysis and conclusion should be human conducted. With this, rather traditional approach, researchers will have clearly less ethical issues to consider. Finally, the ethical aspects of Generative AI tools in research ought to be investigated more in detail, in a separate future study.

In the contemporary society where the rapid development of generative AI (GenAI) infiltrate daily life, it isimperative for schools to keep up with the development. Future generations are expected to have GenAI skills and knowledgein the same way that traditional literacy is a recruitment condition today. School curricula need to integrate this newtechnology to support students' learning and development. Research on artificial in education (AIED) has reported on thechallenges with the involvement of GenAI in teaching and learning, but also on GenAi as a study support. To not fall behindmany schools have started out with AIED initiatives which creates a need for studying how GenAI could be applied in a usefulway in teaching and learning activities. The research question that guided this study was: In what ways could the use of GAIin visual form support students' learning process and motivation in upper secondary school settings? The overall researchstrategy was a qualitative case study approach with investigator triangulation. Data were collected in a combination ofobservations at workshop sessions and semi-structured interviews. with teachers as well as students. In a six-step inductivethematic analysis data excerpts were coded, aggregated to categories, and presented. Findings indicate that GenAI tools forimage generation, can have a positive effect on learning. At the same time that memorisation of information is supported,there were also a positive impact on motivation and student satisfaction. The involvement of image generation tools not asubstitute, but rather a complement to traditional teaching and learning activities. The conclusion is that the use of AI ineducation can offer new learning opportunities, and with the increased use of GenAI, it is crucial for both students andteachers to keep pace. However, this would require that more time and resources for teacher professional development on AIED. 

The rapid development of Generative AI (GAI) makes an impact in many fields of our society, andfor higher education this is a fact that must be addressed. Research has reported on GAI creating newchallenges, and GAI as a catalyst for a transformation of higher education (Chiu, 2024; Yusuf et al.,2024). According to Chiu (2024) the four most important educational domains to consider arelearning, teaching, assessment and administration. This study had a focus on learning, and as pointedout by Choudhuri et al. (2024), there is a gap in the understanding of the potential and the pitfallswhen using GAI in computing education. The aim of this paper is to explore the potential impact ofAI technologies on cognitive load, relevant for computing education at higher education level. Theresearch question to answer is: What is the potential impact of AI technologies on cognitive load in computing education accordingto recent research?

Purpose The conducted examination of programming affordances and constraints had the purpose of adding knowledge and value that facilitate the on-going national curricula revision; knowledge that also could be of general interest outside the Swedish K-12 context. Design/methodology/approach With a qualitative approach, the study was conducted as a document analysis where submitted lesson plans were the base for a directed content analysis. Findings This study presents findings on how the involvement of programming in mathematics and technology have potential to foster engagement and motivation among students. Findings also indicate that the implementation of programming can develop important general skills that go beyond the boundaries of mathematics and technology. Moreover, the identified constraints could be valuable to improve the on-going curriculum development for K-12 mathematics and technology. Research limitations/implications This qualitative study was conducted on a relatively small number of teachers where the majority has taken the courses on a voluntary basis. An important complement would be to conduct a larger quantitative study with data from a more general sample of K-12 teachers. Practical implications Results and discussions provide guidance for K-12 teachers and other stakeholders who want to introduce programming as a complementary tool in teaching and learning activities. Social implications The study has a contribution to the on-going implementation of the Swedish national curricula for K-12 mathematics and technology. Originality/value During the last years, many studies have been published on teacher training in programming, and how the training can be improved. This study goes beyond the actual teacher training and examine aspects teachers translate to theirs daily work after completing the training.

This paper is a review of content analysis or thematic analysis which is further explored though the lens of impressions of doctoral students who attended a presentation on the subject.The long tradition of quantitative research still dominates many university courses on research methodology and data analysis. During the 20th century the field of qualitative research has had a growing need for new analysis methods that accommodate qualitative data and two frequently used methods are content analysis and thematic analysis. They have several things in common and sometimes, they have been understood by researchers to be interchangeably. It has been argued by some researchers that conventional content analysis has really the same functional approach to analysing data as an inductive thematic analysis. This study reports on two webinars on qualitative analysis involving doctoral students and facilitated by the authors. The webinars presented, discussed content analysis and thematic analysis, and gathered participants’ reflections on these methods using a Padlet (padlet.com). The aim of the study was to analyse and describe doctoral students' perceptions of content analysis and thematic analysis. The data collected has been analysed using conventional content analysis applying an abductive coding approach. The study identifies several perceived similarities and differences between the two methods, but also opportunities and challenges for applying them. Findings highlight that the two methods are perceived to be applicable to different types of research. Furthermore, they offer similar challenges to the researcher including their potential for bias and could be considered a choice between an intuitive and a practical approach to analysis. Many of the identified perceptions can be related to previous literature on content analysis and thematic analysis. However, other perceptions seems to indicates a need for more thorough and nuanced discussions on methods for qualitative analysis. The study suggest that more efforts should be made to support doctoral students in attaining a nuanced understanding of qualitative methods for analysis. 

Research has a long tradition of quantitative research which still dominates many university courses on research methods. Qualitative research is a younger phenomenon that was established in research after the second world war. An emerging research field that needed new analysis methods tailored for qualitative data. Two of the most frequently used approaches in qualitative data analysis are content analysis and thematic analysis. In several aspects content analysis and thematic analysis both share a common approach to analytically examine qualitative data, and the fact that they have been used interchangeably has made it difficult for the more unexperienced researchers to distinguish and choose between them. The aim of this study is to examine doctoral students’ perceptions of qualitative analysis with content analysis and thematicanalysis. The study had a qualitative approach with data collected from two webinars on qualitative data analysis, where a total of 76 doctoral students participated. Data consists of participant reflections in a Padlet on content analysis and thematic analysis at the two webinars. Webinar participants have given their consent to use their reflections in the Padlet for research. Content analysis with an abductive coding approach was used to analyse the collected data and formulate categories that answer the study’s aim and research question. Results show both perceived similarities and differences between content analysis and thematic analysis. Both are perceived to have a similar process in the coding of data, although content analysishas a wider selection of coding approaches and thematic analysis support deeper immersion. Content analysis is also perceived as more practical and straightforward, while thematic analysis is perceived as more intuitive and faster to learn. Both content analysis and thematic analysis are perceived to have individual opportunities and challenges that make them appropriate for different types of research. Findings presented in this study can be used by researchers at any level to explore similarities and differences between content analysis and thematic analysis, and where to apply them in research.