Mid Sweden University

Despite the rapid integration of artificial intelligence (AI) in professional environments, its implications for employee engagement are not yet well understood. Hence, this study examines how AI technologies impact employee engagement in the IT sector. The study consisted of 28 semi-structured interviews with IT professionals, recruited in Sweden. The responses were recorded, transcribed, and thematically analyzed using the job demands-resource framework. The findings reveal that AI can enhance work engagement by reducing repetitive tasks, supporting learning, and increasing the meaningfulness of work. However, AI also introduces new demands, including cognitive overload, skill relevance uncertainty, and concerns about tool reliability and leadership support. Theoretically, the findings highlight the need to adapt the Job Demands Resources model to account for the dynamic, context-dependent nature of digital technologies, such as AI. This suggests the emergence of a concept we term Digital Work Engagement, a positive and fulfilling user experience of vigor, dedication, and absorption based on the worker’s interactions with and relation to technology in the workplace. Practically, the study offers guidance for designers and managers on fostering work engagement by aligning AI development and integration with professional growth, autonomy, and support.

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 accumulation of artificial intelligence (AI) in teaching and learning settings and systems invites critical inquiry into how to investigate emerging practices in education involving AI technologies. As AI agents are increasingly positioned as actors in their own right in practices of teaching and learning, questions of agency, participation, power, and relationality are brought to the fore in new ways that educational researchers need to consider. This entry focuses on how to theoretically approach sustainable AI practices in teaching and learning, underpinning the content of the master’s course AI Literacy in Educational Settings (7.5 ECTS). The course is embedded in the master’s program Learning, Digitalization, and Sustainability at Jönköping University, Sweden. The aim is to present alternative concepts to think with for students and educators who engage with challenges and opportunities that emerge through the introduction of AI technologies in pedagogic design ...

Artificial intelligence (AI) is reshaping work, presenting new challenges and opportunities for professionals across industries. This study, grounded in Self-Determination Theory (SDT), examines the impact of AI integration on work engagement among IT professionals. Through qualitative investigation, our research reveals that AI both augments and complicates professionals' work lives, providing opportunities for growth while also demanding ongoing adaptation. Key findings indicate that AI tools like UiPath and GitHub Copilot enhance work efficiency by automating routine tasks, enabling professionals to concentrate on more complex aspects of their work and enhancing their perceived competence. However, this efficiency gain requires continuous learning and adaptation, posing challenges in maintaining engagement and mastery. These findings illuminate the complex balance between leveraging AI for increased efficiency and maintaining the intrinsic human elements of IT design, offering some insights for navigating AI integration in the workplace.

As Artificial Intelligence (AI) continues transforming workplaces globally, particularly within the Information Technology (IT) industry, understanding its impact on IT professionals and computing curricula is crucial. This research builds on joint work from two countries, addressing concerns about AI's increasing influence in IT sector workplaces and its implications for tertiary education. The study focuses on AI technologies such as generative AI (GenAI) and large language models (LLMs). It examines how they are perceived and adopted and their effects on workplace dynamics, task allocation, and human-system interaction.

IT professionals, noted as early adopters of AI, offer valuable insights into the interplay between AI and work engagement, highlighting the significant competencies required for digital workplaces. This study employs a dual-method approach, combining a systematic and multi-vocal literature review and qualitative research methods. These included a thematic analysis of a set of 47 interviews conducted between March and May of 2024 with IT professionals in two countries (New Zealand and Sweden). The research aimed to understand the implications for computing students, education curricula, and the assessment of emerging professional competencies.

The literature review found insufficient evidence addressing comprehensive AI practice methodologies, highlighting the need to both develop and regulate professional competencies for effective AI integration. Key interview findings revealed diverse levels of GenAI adoption, ranging from individual experimentation to institutional integration. Participants generally expressed positive attitudes toward the technology and were actively pursuing self-learning despite some concerns. The themes emerging from the interviews included AI's role in augmenting human tasks, privacy and security concerns, productivity enhancements, legal and ethical challenges, and the evolving need for new competencies in the workplace.

The study underscores the critical role of competency frameworks in guiding professional development and ensuring preparedness for an AI-driven environment. Additionally, it highlights the need for educational institutions to adapt curricula to address these emerging demands effectively.

Where do we start the history of Artificial Intelligence (AI)? Alan Turing introduced a test for measuring intelligence in a machine (the Turing test) in 1950. In 1942, Science Fiction writer Isaac Asimov introduced the three laws of robotics in the short story ‘Runaround’. The concept of Artificial Intelligence was coined by John McCarthy and colleagues in 1955. A lot has happened since the early days of AI. Today, the discussion about AI is mostly on Generative AI (GAI), which has the capabilities of generating seemingly novel content in the form of images, text, code, video and so on. This technology has been highlighted as both an opportunity and a potential disruptive force across several industries and in education. More universities have started to incorporate AI policies to guide staff and students on the use of Artificial Intelligence in higher education. However, these policy documents can take on different forms and contain different guidelines and information, since they are often decided by the university and not in collaboration between several universities. As a consequence, it is possible that staff and students are provided with AI policies that differ quite a lot depending on which university they work or study at. An important question to ask then: What are the similarities and differences between different university AI policies? This presentation focuses on the preliminary findings from a document analysis study where AI policies from Swedish universities have been collected and analysed during the spring semester of 2025. The presentation highlights common themes and patterns in the collected material, identified similarities and differences, and discuss the material in relation to other official AI guidelines (on national and EU level) and international research. The presentation concludes with a set of recommendations for future research and present the next steps of research for the document analysis study.

Um dos desafios mais discutidosatualmente é como atrair novos públicos para aprogramação de computadores e, em especial,como engajar meninas e mulheres nessecampo. Paralelamente, estudos apontam quecrianças dedicam uma parte significativade seu tempo a diferentes tipos de jogos,sendo que os jogos educativos já ocupam umespaço importante no aprendizado formal,informal e não formal. Apesar disso, muitosdesses jogos ainda apresentam um designque desperta maior interesse nos meninosdo que nas meninas. Outro ponto central,abordado neste artigo, diz respeito a comoavaliar os resultados de aprendizagem obtidospor meio de um jogo educativo. Desenvolverum jogo envolvente e prazeroso já representaum desafio considerável; contudo, mensurara aprendizagem é essencial para que o jogoseja aceito por professores e efetivamenteincorporado às práticas pedagógicas. Oobjetivo deste estudo foi descrever e discutir oprocesso de concepção e desenvolvimento deum jogo educativo pensado para que meninastivessem interesse em jogar em conjunto e, aomesmo tempo, pudessem aprender conceitosfundamentais de programação. A questão depesquisa que norteou o estudo foi: “Como umjogo educativo motivador e inclusivo sobreEducação no Século XXI: Perspectivas Contemporâneas sobreEnsino-Aprendizagem III Capítulo 14 182fundamentos de programação poderia ser projetado e desenvolvido, com pré-requisitosmínimos para alunos e professores? A metodologia adotada foi a Design Science Research(DSR), uma abordagem de pesquisa em ciência do design. O trabalho seguiu o processo daDSR, estruturado em cinco fases: 1) Explicitação do problema, 2) Definição dos requisitos,3) Concepção e desenvolvimento do artefato, 4) Demonstração do artefato, e 5) Avaliaçãodo artefato. A primeira fase apoiou-se em uma revisão exploratória da literatura; segunda,em um estudo bibliográfico mais amplo e sistemático, aliado a testes de jogos. Na terceirafase, realizaram-se sessões de brainstorming para o design, seguidas da implementaçãono ambiente de desenvolvimento Unity. Por fim, o jogo foi demonstrado e testado por umgrupo de desenvolvedores acadêmicos de jogos. Os resultados da avaliação formativamostraram-se promissores; contudo, o próximo passo essencial do projeto é realizar umaavaliação mais formal, utilizando learning analytics baseadas em jogos e envolvendo umpúblico de teste maior e mais diversificado.

A well-discussed problem is how to attract a new public to computer programming, and especially how to reach girls and women. At the same time research reports on that children spend considerable amounts of time playing different types of games, where educational games today are part of mainstream learning. However, many educational games still have a design that appeals more to boys than to girls. Another problem addressed in this paper is how to measure the learning outcomes of an education game.  The aim of this study was to describe and discuss the design and development of an educational game where girls would like to play together and at the same time learn fundamental programming. The research question that guided this study was: “How could a motivating and inclusive educational game on fundamental programming be designed and developed, with minimal prerequisites for students and teachers?”. The overall strategy for the design and development of the was the Design Science Research approach. This work was carried out according to the recognised DSR process with the five phases of: 1) Explicating the problem, 2) Defining the requirements, 3) Designing and developing the artefact, 4) Demonstrating the artefact, and 5) Evaluating the artefact. Phase one was based on a minor literature study, while Phase 2 was a combination of a larger and more systematic literature study combined with game testing. Phase 3 was conducted with brain storming sessions for design followed by implementation in the Unity game development tool. Finally, the game has been demonstrated for, and tested by, a group of academic game developers. Results from the formative evaluation look promising, but the important next step in this project is a more formal evaluation using game-based learning analytics with a larger and more diverse test audience.

In the EDU-AI project (Adapting Computing Education for an AI-Driven Future: Empirical Insights and Guidelines for Integrating Generative AI into Curriculum and Practice), we investigate the application of generative AI by IT professionals, how students view this technology in higher education, as well as faculty adaptation and changing teaching strategies due to generative AI. This presentation will be based on a report from the EDU-AI project where we summarize ongoing research and discuss findings, related to higher education teaching and learning. These findings include adoption patterns with generative AI, generative AI advantages (such as increased productivity, enhanced learning, and innovative problem-solving), challenges with generative AI (such as ethical considerations, data biases, and potential misuse), and possible AI competence requirements in future labor market.

Artificial Intelligence (AI) has been highlighted as a potentially disruptive force across several industries and in higher education. Research has suggested that education and upskilling of citizens should adopt a broad approach, not only focusing on experts, for better nationwide AI readiness. This study investigates the content of AI policies in higher education by analysing official documents from Swedish universities. It identifies key themes and patterns, comparing them with related research and international guidelines. Expanding on the results of the study, it develops a dynamic alignment model for higher education AI policy (DAMHEAP) which, grounded in institutional theory, highlights strategic, pedagogical, ethical and legal, operational and adaptive alignments. This model, together with 10 practical recommendations, provides a roadmap for higher education institutions to develop and maintain AI policies that are pedagogically relevant, ethically responsible and adaptive to technological change.

Generative Artificial Intelligence (GAI) transforms our technological interactions, including new capabilities and concerns about biases and misuse. In the field of human-computer interaction (HCI), previous research has investigated generative AI in relation to human-centred AI, user trust, user experience, design work, co-creativity, and user personas. This study applies the theoretical lens of affordances and constraints to ask the question: Which affordances and constraints of generative AI can be identified in human-computer interaction research? The study employs a scoping literature review approach to collect data from the Web of Science Core Collection databases. The query string combined keywords, such as “generative”, “artificial intelligence”, and “human computer interaction”, with Boolean operators AND and OR. Inclusion and exclusion criteria were used in the screening of 156 identified articles, from which a total of 37 were selected for inclusion in the study. An initial categorization matrix, based on the theory of affordances, was used toconduct a deductive thematic analysis. The analysis followed the guidelines for thematic analysis suggested by Braun and Clarke.The investigation identified seven key themes, with included sub-themes, illustrating the varied applications and potential effects of generative AI. The seven key themes are: 1) improving algorithms, 2) collaborative work, 3) education support, 4) truth issues, 5) biases, 6) ethical considerations, and 7) consequences for job market. The study further highlights the importance of considering contextual differences and short-term and long-term consequences when applying GAI technologies, as well as ethical considerations, such as ethical and legal accountability. The paper concludes with a novel conceptual model for affordances and constraints of generative AI, informing future research, guiding stakeholders’ use and implementation, and providing design recommendations for generative AI systems across various sectors.