This bachelor thesis explores the driving forces behind the implementation of generative AI in work processes among companies and professionals in the advertising and media industry. The study investigates the extent to which implementation has progressed in the industry and how it may affect the industry in the future. Through a combination of qualitative interviews and quantitative analysis of survey data, including five interviews with professionals in the industry, the study highlights the challenges and issues individuals in the industry face when attempting to implement generative AI in workflows. To contextualize the industry's adoption of generative AI compared to other sectors, the Technology Acceptance Model (TAM), Technology Adoption Lifecycle (TAL), and Technostress are used as theoretical frameworks. The results indicate that professionals in the advertising and media industry have a high level of acceptance for the technology and experience technostress as a result of the proliferation of generative AI. The fear of being replaced or losing one's job emerges as a strong motivating factor for integrating and improving skills in the use of generative AI. According to the results, there is a strong belief that changes will occur in the industry as a result of the development. Due to the perceived concern and high level of acceptance within the industry, the advertising and media industry ranks higher on the TAL scale than the average. In summary, the media and advertising industry is in a unique stage of development where the use of generative AI evokes both concern and anticipation. Many see the potential of the technology to streamline and improve workflows, while others are concerned about its potential impact on jobs and the job market. The dual nature of the technology - as a potential solution to increase production and attract more projects while also posing a risk of job loss - is evident. 

With the purpose of developing new methods in science education, the authors combine a socio-scientific issue and game-based technologies from augmented reality games into an intervention activity called Transformer. The authors design the intervention, collect and analyze data, and present results using the three elements from the theory of transformational play (Barab, Gresalfi & Ingram-Goble 2010) namely: engaging and stimulating roles, scientifically relevant content and complex and societally significant context. In Transformer, 20 upper-secondary students engage in a role-play concerning the project of building a school campus area close to a transformer station. The students explore the actual area using simulation-based mobile technologies to collect information through interviews with virtual characters, virtual measurements, and own observations. Collected information is synthesized into arguments concerning the appropriateness of building the campus area. The Transformer activity is based on SSI (socio-scientific issue), a pedagogical model aiming at developing students’ competences to make well-informed decisions through engaging them in complex societal issues. Data from the intervention are collected with multiple methods using questionnaires and interviews. The analysis illustrates expressions of immersive role play and active and engaged students who enjoy the opportunity of working in groups with a challenging task outside the class room. For some roles, however, there is room for improvement in the outdoor part of the intervention. We argue that this design perspective makes it feasible for a teacher or team of teachers to stage a multi-disciplinary engaging intervention that is constructively aligned with the students’ and teachers’ own practices and that addresses curricular goals.

The Argon project was started to explore the creation of Augmented Reality applications with web technology. We have found this approach to be particularly useful for community-based applications. The Argon web browser has gone through two versions, informed by the work of our students and collaborators on these kinds of applications. In this paper, we highlight a number of the applications we and others have created, what we learned from them, and how our experiences creating these applications informed the design of Argon2 and the requirements for the next version, Argon3.