Louise Barkhuus

Generative artificial intelligence (GenAI) tools are increasingly embedded in software development workflows. Since the Greenland Crisis of early 2026, there has been a renewed push for evaluation of European reliance on American tech. The concern over American GenAI tools is particularly relevant in Denmark who was in the direct line of fire during the geopolitical tension escalation. Through questionnaires (N=13) and interviews (N=12) with software developers, we examine how developers reflect on the GenAI tools they are using, how geopolitical tensions inform GenAI tool usage, and who is responsible for selecting GenAI tools. Our findings show that American GenAI acceptance has wavered with multiple developers actively seeking alternatives to American tech while others raised concerns about a lack of European alternatives as a main barrier for change. Our study provides an important snapshot in time, of how political tension affects collaborative technologies on the ground while reinforcing AI sovereignty as a future avenue of research.

Implementing AI tools is an investment of resources in all software development teams. Members of software development teams have adopted AI tools differently based off of different priorities in programming. This interview study engages software developers (N = 11) in interviews about their software development practices since larger scale AI deployment in their respective companies. Our findings show four separate clusters of software developers coming out of these AI augmented teams with top priorities ranging from code quality, teamwork, up-skilling/learning, and speed of development. These different clusters were developed into personas which can support organizations in understanding how, why, and when AI tools are helpful to their software teams. We engaged both junior and senior developers in the interviews to understand if the clusters were experience dependent and found a spread of expertise across all categories sans the Democratizing Deployer which had only highly experienced members.

Generative artificial intelligence (AI) tools are increasingly embedded in software development work, reshaping how code is produced, reviewed, and maintained. Following the public release of ChatGPT and the expansion of the AI tooling ecosystem, developers now routinely encounter AI assistance in collaborative and security-sensitive contexts. Through interviews with eleven software developers familiar with generative AI tools and working across teams, we examine how developers adopt and evaluate AI in daily work, how AI use affects security-related practices, and how these tools shape collaboration and accountability. Our findings show that AI use is contingent and selective rather than habitual. Developers primarily rely on AI for low-complexity, low-accountability tasks, while deliberately limiting use in security-critical or system-defining work. Responsibility for correctness and safety is consistently framed as non-transferable leading many developers to refuse or limit AI use.

Designing new technologies to support synchronous interaction across distances has for many years focused on creating symmetry for participation between geographically distributed actors. Symmetry in synchronous interaction has, to some extent, been achieved technologically (while multiple social, historical, political, and hierarchical concerns continue to exist) and proven empirically in the increased use of remote-work technologies that were used during the pandemic. However, synchronous interaction in hybrid work is achieved differently, since the asymmetry produced by some participants being collocated while others geographically distributed introduces increased complexities for such interactions. Focusing on this challenge, we ask: To what extent can symmetry in cooperative work engagements be achieved in hybrid work contexts? We explore this question by interrogating multiple different empirical examples of synchronous hybrid interaction collected across different organizations, activities, and events. We found that the effort required to accomplish hybrid work includes additional articulation work necessary for bounding multiple intertwined artefacts across sites, devices, and applications. Further, the multiple artefacts setup across sites, combined with asymmetric collocation across participants, produce incongruence in technological frames of reference for each participant. All participants in hybrid work have only partial access to the hybrid setup, and no single person has access to the complete setup. The incongruence in technological frames produces insurmountable gaps in collaboration, causing all hybrid work situations to be characterized fundamentally by asymmetric relationships. We argue that symmetry in hybrid synchronous interaction is impossible to attain in attempts to solve this problem through design. Instead, we propose that designers of cooperative technologies for hybrid work shift towards developing artefact-ecologies supporting hybrid work, focusing on asymmetry as a necessary feature. Fundamentally, the design strategy should explore novel ways of taking advantage of the multiple different artefact-ecologies which serve as the foundation for the hybrid collaboration. Instead of striving for symmetry, we propose to feature asymmetric conditions in future technology designs for hybrid interaction.

Large language models (LLMs) stand to reshape traditional methods of working with data. While LLMs unlock new and potentially useful ways of interfacing with data, their use in research processes requires methodological and critical evaluation. In this workshop, we seek to gather a community of HCI researchers interested in navigating the responsible integration of LLMs into data work: data collection, processing, and analysis. We aim to create an understanding of how LLMs are being used to work with data in HCI research, and document the early challenges and concerns that have arisen. Together, we will outline a research agenda on using LLMs as research tools to work with data by defining the open empirical and ethical evaluation questions and thus contribute to setting norms in the community. We believe CHI to be the ideal place to address these questions due to the methodologically diverse researcher attendees, the prevalence of HCI research on human interaction with new computing and data paradigms, and the community’s sense of ethics and care. Insights from this forum can contribute to other research communities grappling with related questions.

We present a qualitative study of a Cypriot summer school where we used the BBC micro:bit to examine gender inclusivity in e-textiles. We employed the Computational Making framework to analyze ethnographic data teaching 24 middle school students, aged 10-15. The study contrasts the challenges faced by students using the micro:bit with those from our team’s previous similar e-textile studies using the LilyPad Arduino. We pinpoint the BBC micro:bit’s limitations from an inclusive design perspective, underscoring the absence of gender-sensitive considerations in its hardware design. The paper presents a critique how upwards of £75 Million were spent deploying the micro:bit without proper user studies of gender equity and e-textiles. We propose design recommendations for future BBC micro:bit versions, advocating for integrating a gender-sensitive participatory design approach to enhance the usability and engagement of computational and creative making with e-textiles for children of all genders.

Being part of a minority group can be isolating and lonely; in some places, members of LGBTQ+ groups build up communities and neighborhoods, but in other areas, LGBTQ+ places are more invisible. In this paper we present a system that facilitates a digital layer of stories and experiences by other LGBTQ+ individuals. We present our novel prototype and our user study of the application, where users discuss how this digital service can help building place based community among LGBTQ+. This study contributes by providing a better understanding of how the physical space of the city can be transformed into meaningful places through a digital layer of personal stories to encourage connections within the LGBTQ+ population.

After the pandemic, it is urgently important to explore the special challenges which arise with hybrid work. Through cross-case analyses of published papers, we propose collocated distance as a design challenge uniquely relevant for hybrid cooperative technologies. We identify and conceptualize collocated distance as a design challenge that arises in hybrid work situations, where at least three actors are mutually dependent in their work while being located within fewer contexts than the number of actors. Collocated distance reminds us that when designing hybrid technologies, we must not only focus on creating technologies that support the work across geographical locations but equally pay attention to the relations and possible disconnections which exist locally between collocated actors. When designing cooperative technologies supporting distributed work, often focus is on the boundaries between geographical contexts – however, in hybrid work, we must not forget to pay attention to the collocated boundaries within the same context.