The rapid development of generative AI is reshaping core assumptions about writing, learning, and independence in higher education [1]. The project is grounded in an academic literacies perspective, which views writing as a socially and institutionally situated practice shaped by norms, values, and disciplinary expectations. Complementing this, Science and Technology Studies (STS) is used to conceptualize AI as a sociotechnical actor that actively participates in reshaping assumptions about authorship, independence, and pedagogical practice. AI literacy serves as an analytic lens, capturing how students interpret, critique, and negotiate the role of AI technologies as part of their emerging professional identities as teachers.

The study addresses the following research question: How is writing constructed and understood in relation to AI literacy and independence by student teachers, and what forms of writing actions, cognitive processes, and ideals of independence are expressed in the teacher’s task instructions and assessment criteria?

Methodologically, the project employs classroom observation and textual analysis. Observation of one English-language lesson in teacher education provides insight into how students discuss and negotiate the role of AI in writing and how they position themselves in relation to expectations of independence. This is complemented by analysis of written task instructions and assessment documents, which reveals how institutional ideals surrounding writing, cognition, and autonomy are communicated.

Preliminary findings indicate a clear tension between institutional ideals of independent authorship and students’ uncertainty about the role AI should play in academic writing, in line with earlier research [2].

Students expressed hesitation and ambiguity regarding what forms of AI use are permissible and how independence ought to be interpreted in an AI-mediated environment. This uncertainty generates an ongoing negotiation as students attempt to align institutional expectations with evolving technological possibilities.

Analysis of the teacher’s task instructions and assessment criteria shows that independence, originality, and reflective engagement are strongly emphasized, and that the use of AI tools is (currently) explicitly prohibited. Expectations of cognitive effort and individual responsibility thus remain firmly anchored in pre-AI models of writing, creating a pronounced misalignment between institutional framings and the technological realities students encounter.

The study contributes to international research on writing, AI literacy, and teacher education by offering empirical insights into how student teachers negotiate authorship and independence in an AI-saturated context. The findings underscore the urgency of developing responsible, pedagogically grounded approaches to AI in teacher education—approaches that acknowledge both the transformative potential and the epistemic challenges introduced by generative AI [3].

References:[1] Kasneci, E. et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences, 103, p.102274.[2] Josefsson, P., Hüttenrauch, H., & Jacobsson, M. (2023). AI-driven plagiarism in higher education: Impact on learning and examination. Forskning om högre utbildning, 54–55.[3] Miao, F. and Shiohira, K., 2024. AI competency framework for students. UNESCO Publishing.Keywords: AI, academic writing, higher education, teacher education.

This text aims to contribute to the discussion about how technology affects plagiarism and cheating in higher education. This is done by describing a process where researchers and one AI-bot interacted via text on the topic of plagiarism and cheating in higher education. By entering text data and interacting in multiple steps with the Chatbot ChatGPT [1], developed by OpenAI and trained by reinforcement learning, text was iterated in response to questions about cheating and plagiarism in exams. By testing the AI bot’s ability to produce text and discussing what input data we provide the bot with, we explore possible consequences and impacts on learning in higher education. The general understanding of a higher education institution’s approach to plagiarism is guided by a few principles. For illustration, Södertörn University communicates a document [2] that outlines rules and processes for examinations. Relevant to this are a couple of premises that were assumed to be true, at least until recently: A student could misuse another person's work by claiming the authorship, i.e., by not indicating a proper source, the submitted work is falsely presented as an original creation. Another form of academic cheating is outsourcing to and paying for another (sometimes academically qualified expert), a process and questionable practice known as procurement and criticized as a major institutionalized fauxthentication in higher education [3].  Currently, the cloud services check written assignments automatically for plagiarism function as fear of detection and then punitive deterrence. This contrasts with using these as selfevaluation tools, where feedback from the software can be used by lecturing staff to teach students about academic reference management and make knowledge claims regarding the discipline's literacy [4]. As pointed out by Hass [4] it is a problem if we do not train students on how to interpret the results of plagiarism detection programs. The digital tools available will invariably provide students with the capacity to utilize them to complement their effort in producing academic writing. We already know that many students use online search engines to search for texts, as well as apply programs for translation. Using these services in combination allows you to search for text on a specific topic and then copy and paste the text into the translation program. By switching between different languages, the text is "washed" to be experienced as an original contribution.  With the latest development of AI technology, we see further challenges when it comes to working with originality in the writing process. Let's look back on our interaction with the chatbot. By using the bot’s functionality, based on reinforcement learning, and questions about plagiarism in higher education, the chatbot presented the following as one consequence "...students may become overly reliant on AIbased tools and lose the ability to think critically and independently." [1]. The chatbot's output 54 is in line with previous research that problematized the use of AI tools for plagiarism, see for example Francke [5] in which ethical implications are highlighted. As academics schooled in the tradition of original writing and not-AI-aided scholars we are literally being technology-pushed into an “interesting” situation: We want to uphold the creativity and developing aspects of writing as an externalization of thinking, but without becoming AI-Luddites. Instead, we (fore-)see the need of discussing the effects and needed higher education adaptations and changes of these new and empowering tools to teach aspiring students how to write better academic texts with them, too.  

References: [1] OpenAI (2022). ChatGPT. online: https://openai.com/blog/chatgpt/ (2022-12-10) [2] Södertörns University (2022). Rights and obligations - rules for studying at the first and second- cycle level at Södertörn University. https://www.sh.se/english/sodertorn-university/student/opportunities-resources-and-rights/rightsobligations-and-safety/regulations-for-studying-at-bachelors-and-masters-level-first-and-secondcycle-at-sodertorn-university (2022-12-10) [3] Szyber, B., (2020). Fauxthentication: Art Academia and Authorship (or the site-specifics of the Academic Artist). Stockholm University of the Arts. [4] Hass, A. (2022). Using plagiarism feedback as assessment for learning to socialize students into  disciplinary writing: a theoretical perspective. South African Journal of Higher Education, 36(2), 133-150. [5] Francke, E., & Alexander, B. (2019). The potential influence of artificial intelligence on plagiarism a higher education perspective. In Proc. European Conference on the Impact of Artificial Intelligence and Robotics. EM Normandie Business School, Oxford (pp. 131-140). 

Robots are increasingly being used in domestic environments and should be able to interact with inexperienced users. Human–human interaction and human–computer interaction research findings are relevant, but often limited because robots are different from both humans and computers. Therefore, new human–robot interaction (HRI) research methods can identify the design of robots suitable for inexperienced users. A video-based HRI (VHRI) methodology was used to carry out a multi-national HRI user study for the prototype domestic robot BIRON (BIelefeld RObot companioN). Previously, the VHRI methodology was used in constrained HRI situations, while in this study HRIs involved a series of events as part of a “home tour” scenario. Thus, the present chapter is the first study of this methodology in extended HRI contexts with a multi-national approach. Participants watched videos of the robot interacting with a human actor and rated two robot behaviors (Extrovert and Introvert). Participants’ perceptions and ratings of the robot’s behaviors differed with regard to both verbal interactions and person following by the robot. The study also confirms that the VHRI methodology provides a valuable means to obtain early user feedback, even before fully working prototypes are available. This can usefully guide the future design studies on robots, and associated verbal and non-verbal behaviors.

Data journalism is a form of storytelling where elements of text, interactive graphics and datasets are often combined into one interactive journalistic project or service. In addition, the general public are often encouraged to submit their own data for publication within the service. Data journalism is not a new field, but due to the expensive production process and the advanced skills needed by journalists, it is still relatively uncommon in the Swedish media landscape. This study is based on a joint research project together with SVT Pejl, the data journalism department at the Sveriges television (SVT). The main aim of this paper is to explore this emerging form of interactive journalism based on public sector data, and how the general public engages with it. This paper is based on a four-month case study in 2011 and 2012 of SVT Pejl and “Brottspejl”one of its data journalism services. An action research approach has been used, and the methods were in-depth interviews and a web-based survey.

Computer assisted reporting (CAR). Datadriven journalism. Databasjournalistik. Datajournalistik.

Det finns många namn på den växande genre inom undersökande journalistik som använder statistisk analys, datorstödd textanalys och olika typer av grafisk visualisering. Detta område har varit föremål för ett samarbete mellan forskare vid Södertörns högskola och Pejlredaktionen vid Sveriges Television (SVT) under 2011/12. Syftet med samarbetet har varit att utveckla kunskap om arbetsmetoder inom datajournalistik och om publikens användning av den. Projektet har haft stöd från den statliga forskningsstiftelsen Vinnova, och går från hösten 2012 över i en ny fas där ytterligare sju medieföretag ska vara med under en tvåårsperiod.

Information and Communication Technologies (ICT) have the potential of facilitating the lives of citizens. However, experience consistently shows that user-interface innovations for consumer products are being researched and developed without taking into account the needs of people with disabilities. ETSI, the European Telecommunications Standards Institute, has established a Specialist Task Force (STF) 377 on “Inclusive eServices for all: Optimizing the accessibility and use of upcoming user interaction technology”. The aimof this working group is to systematically evaluate ongoing and forthcoming interaction technologies to sketch a 10-year roadmap of foreseen technological enablers. The results of investigations using this method allowstakeholders in different stages of the research and development lifecycle of e-Services to identify and avoid potential difficulties in the design of user interfaces which could cause elderly or disabled users to experience usability issues.

Las Tecnologías de la Información y la Comunicación (TIC) poseen el potencial de facilitar la vida de los ciudadanos. Sin embargo, la experiencia muestra consistentemente que la investigación y desarrollo referentes a las interfaces de usuario de productos de consumo se llevan a cabo sin tener en cuenta las necesidades de las personas con discapacidad. ETSI, el Instituto Europeo de Normalización de las Telecomunicaciones, ha establecido un Specialist Task Force (STF) 377 acerca de “e-Servicios inclusivos para todas las personas: Optimizar la accesibilidad y el uso de las próximas tecnologías de interacción con el usuario”. El objetivo de este grupo de trabajo es el de evaluar interfaces de usuario de próxima aparición para esbozar una hoja de ruta paralos próximos 10 años sobre los aspectos que pueden facilitar que estas tecnologías sean accesibles en el momento de su implantación.Los resultados de la investigación permitirán a los actores implicados en las diferentes etapas de la investigación y desarrollo de e-Servicios identificar y evitar durante la fase de diseño posibles problemas de usabilidad par a las personas mayores y las personas con discapacidad.

Robots are increasingly being used in domestic environments and should be able to interact with inexperienced users. Human-human interaction and human-computer interaction research findings are relevant, but often limited because robots are different from both humans and computers. Therefore, new human-robot interaction (HRI) research methods can inform the design of robots suitable for inexperienced users. A video-based HRI (VHRI) methodology was here used to carry out a multi-national HRI user study for the prototype domestic robot BIRON (BIelefeld RObot companioN). Previously, the VHRI methodology was used in constrained HRI situations, while in this study HRIs involved a series of events as part of a 'home-tour' scenario. Thus, the present work is the first study of this methodology in extended HRI contexts with a multi-national approach. Participants watched videos of the robot interacting with a human actor and rated two robot behaviors (Extrovert and Introvert). Participants' perceptions and ratings of the robot's behaviors differed with regard to both verbal interactions and person following by the robot. The study also confirms that the VHRI methodology provides a valuable means to obtain early user feedback, even before fully working prototypes are available. This can usefully guide the future design work on robots, and associated verbal and non-verbal behaviors.

In designing socially interactive robots we have focused on robot movement and its role in multi-modal human-robot communication. In this chapter we describe design and evaluation of robot body movements supporting communication, investigating the idea of using speed and orientation adjustments as design elements in human-robot interaction. The scenario studied includes a robotic shopping trolley that offers products via speech and GUI to the user while both are moving in a supermarket-like environment. Our results show that if the robot slows down while making such offers, users are more prone to react upon them and to take the product. However, even from our early pre-study with mock-up robots we observed that users tended not to mention the robot’s slow-down movements, even if these movements were shown several times to them during a video-based debriefing. This phenomenon, that users react implicitly on the robot’s movements without being consciously aware of them, was confirmed during an experimental study with a fully integrated robot prototype. We discuss our results by reflecting on human-robot interaction design methods, and we draw implications from the lessons learned in the study of the design of robot behaviours. In particular, we list a whole set of challenges for HRI when both the user and the robot are moving.

In designing socially interactive robotswe have focused on robot movement and its role in multi-modal human-robot communication. In this paper we describe a user-centred design and evaluation process, investigating the idea of using speed and orientation adjustments as design elements in human-robot interaction. The scenario studied includes a robotic shopping trolley that offers products to the user while both are moving in a supermarket-like environment. Our results show that if the robot slows down while making such offers, users are more prone to react upon them. However, in an early pre-study, performed only with a robot mock-up, we observed that users tended not to notice the robot's slow-down movements while offers are made, even if these movements were shown several times to them during a video-based debriefing. This phenomenon, that users react implicitly on the robot'smovements without being consciously aware of them, was confirmed during an experimental study with a fully integrated robot prototype.We discuss our results by reflecting on human-robot interaction design methods, and we propose implications from the lessons learnt in the study of the design of robot behaviours.