Reflection and metacognition are central to the creative user experience. However, most HCI research on reflection focuses on clear, task-oriented goals such as to reflect on personal data or pedagogical outcomes. This contrasts with the open-ended and challenging to articulate goals of creative user experiences. For the first time, this workshop brings together interdisciplinary researchers, designers, educators, and artists across HCI, Cognitive Science, Design, AI, Learning Sciences, and Digital Art to examine reflection in creative interaction. The workshop will discuss themes, drawn from earlier discussions with HCI researchers and artists, on: how best to capture reflection in creative contexts, how to leverage the arts to support reflection for ethical change, and how to design creative AI that enhances – not hinders – critical thinking. By bringing interdisciplinary perspectives on reflection into discussion, the workshop will develop a guiding taxonomy for reflection in creative interaction to inform future creative practice and tool development.

The fourth workshop on Explainable AI for the Arts (XAIxArts) continues to bring together and expand a community of researchers and creative practitioners in Human-Computer Interaction (HCI), Interaction Design, AI, eXplainable AI (XAI), and Digital Arts to explore the role of XAI for the Arts. XAI is a key concern of Responsible and Human-Centred AI, emphasising HCI techniques that make opaque AI models more understandable to people. XAIxArts offers a distinctive lens to examine explainability through creative and artistic domains. The previous workshops explored the landscape and the speculative futures of AI in creative processes. To respond to emerging challenges and contribute to creative and societal transformation more broadly, this workshop focuses on the operationalisation of XAI in the Arts. Specifically, we will: i) critically reflect on emerging practices that encourage diversity and inclusivity in XAI; ii) collectively ideate a library of missing projects to encourage future collaborations and speculations; iii) scope the development of a resource hub for open XAIxArts projects to archive tangible XAI interventions and facilitate future community building with the wider discourse on Human-Centred AI.

The third workshop on Explainable AI for the Arts (XAIxArts) continues to bring together and expand a community of researchers and creative practitioners in Human-Computer Interaction (HCI), Interaction Design, AI, explainable AI (XAI), and Digital Arts to explore the role of XAI for the Arts.XAI is a key concern of Responsible and Human-Centred AI, emphasising the use of HCI techniques to explore how to make complicated and opaque AI models more understandable to people.The previous workshops moved from mapping the landscape of XAI for the Arts to co-developing an XAIxArts manifesto.To continue driving discourse on XAIxArts, the anticipated outcomes of this workshop are: i) fresh insights into the evolving challenges of AI bias, lack of transparency and barriers to inclusivity through discussion of current and emerging XAIxArts practices; ii) co-developed speculative futures which expand XAIxArts discourse beyond post-hoc rationalisations of AI decisions into the imaginative possibilities of AI as an interlocutor in the creative process; iii) plans for a co-developed proposal of an edited book on XAIxArts; and iv) community expansion and engagement in wider discourses on Responsible and Human-Centred AI.

The immersive elevator experience is a speculative design for a system which informs people about the issue of decolonization in computer science.The specific area of decolonization that we have chosen to focus on is the issue of how people in first-world countries use and dispose of technology, and how this links to the exploitation of people in third-world countries.We underwent an iterative design process and created an interactive experience that is installed inside an elevator.The aim is to educate users about how the way they use technology is harming people in poorer regions.Through using the iterative design process and receiving feedback from lecturers and our peers, we continuously made changes to design an installation that was engaging and informative.We also discovered how to make our project as user-friendly as possible.This helped us to ensure that users would pay attention to the important issues that we are raising and gives us confidence that they will come out of the experience willing to make a change.

Effective communication between AI and humans is essential for successful human-AI co-creation.However, many current co-creative AI systems lack effective communication, which limits their potential for collaboration.This paper presents the initial design of the Framework for AI Communication (FAICO) for co-creative AI, developed through a systematic review of 107 full-length papers.FAICO presents key aspects of AI communication and their impact on user experience, offering preliminary guidelines for designing human-centered AI communication.To improve the framework, we conducted a preliminary study with two focus groups involving skilled individuals in AI, HCI, and design.These sessions sought to understand participants' preferences for AI communication, gather their perceptions of the framework, collect feedback for refinement, and explore its use in co-creative domains like collaborative writing and design.Our findings reveal a preference for a human-AI feedback loop over linear communication and emphasize the importance of context in fostering mutual understanding.Based on these insights, we propose actionable strategies for applying FAICO in practice and future directions, marking the first step toward developing comprehensive guidelines for designing effective human-centered AI communication in co-creation.

This paper describes "Holograms in Holborn": a speculative design of an interactive holographic experience to engage people in reflection on decolonization in their everyday life.Specifically, the goal is to produce a speculative design which encourages people to apply foresight in considering colonial biases whilst going about their everyday activity on campus, at the University of the Arts London in High Holborn.The design uses 3D holograms, gesture-controlled maps, voice navigation, and 360-degree movement for immersive learning.It also draws inspiration from depictions of holograms from film.An iterative process was followed balancing the design goals to i) create an engaging experience, ii) encourage foresight in everyday experience and not just one time reflections, and iii) be inclusive in its interaction design.The paper explores possibilities for creating inclusive, community-driven digital experiences that challenge colonial legacies.

This second workshop on explainable AI for the Arts (XAIxArts) brings together a community of researchers and creative practitioners in Human-Computer Interaction (HCI), Interaction Design, AI, explainable AI (XAI), and Digital Arts to explore the role of XAI for the Arts. XAI is a core concern of Human-Centred AI and relies heavily on HCI techniques to explore how to make complex and difficult to understand AI models more understandable to people. Our first workshop explored the landscape of XAIxArts and identified emergent themes. To move the discourse on XAIxArts forward and to contribute to Human-Centred AI more broadly this workshop will: i) bring researchers together to expand the XAIxArts community; ii) collect and critically reflect on current and emerging XAIxArts practice; iii) co-develop a manifesto for XAIxArts; iv) co-develop a proposal for an edited book on XAIxArts; v) engage with the wider discourse on Human-Centred AI.

Deep learning generative AI models trained on huge datasets are capable of producing complex and high quality music. However, there are few studies of how AI Generated Content (AIGC) is actually used or appropriated in creative practice. We present two first-person accounts by musician-researchers of explorations of an interactive generative AI system trained on Irish Folk music. The AI is intentionally used by musicians from incongruous genres of Punk and Glitch to explore questions of how the model is appropriated into creative practice and how it changes creative practice when used outside of its intended genre. Reflections on the first-person accounts highlight issues of control, ambiguity, trust, and filtering AIGC. The accounts also highlight the role of AI as an audience and critic and how the musicians’ practice changed in response to the AIGC. We suggest that our incongruous approach may help to foreground the creative work and frictions in human-AI creative practice.

Reflection is fundamental to creative practice. However, the plurality of ways in which people reflect when using AI Generated Content (AIGC) is underexplored. This paper takes AI-based music composition as a case study to explore how artist-researcher composers reflected when integrating AIGC into their music composition process. The AI tools explored range from Markov Chains for music generation to Variational Auto-Encoders for modifying timbre. We used a novel method where our composers would pause and reflect back on screenshots of their composing after every hour, using this documentation to write first-person accounts showcasing their subjective viewpoints on their experience. We triangulate the first-person accounts with interviews and questionnaire measures to contribute descriptions on how the composers reflected. For example, we found that many composers reflect on future directions in which to take their music whilst curating AIGC. Our findings contribute to supporting future explorations on reflection in creative HCI contexts.

This first workshop on explainable AI for the Arts (XAIxArts) brings together a community of researchers and creative practitioners in Human-Computer Interaction (HCI), Interaction Design, AI, explainable AI (XAI), and Digital Arts to explore the role of XAI for the Arts. XAI is a core concern of Human-Centred AI and relies heavily on HCI techniques to explore how complex and difficult to understand AI models such as deep learning techniques can be made more understandable to people. However, XAI research has primarily focused on work-oriented and task-oriented explanations of AI and there has been little research on XAI for creative domains such as the Arts. This workshop will: i) build an XAIxArts research community; ii) map out the current and future possible landscapes of XAIxArts; iii) critically reflect on the potential of XAI for the Arts, forming the basis for an edited book on XAIxArts and an international network of researchers.

Reflection is underexplored in Creativity Support Tool (CST) research, partly due to its ambiguous nature. We suggest that researchers could benefit from a measure of a CST’s capacity to support reflection. To this end, we detail the first stages of development of the Reflection in Creative Experience Questionnaire (RiCE) – a lightweight questionnaire for differentiating between creative user experiences which exhibit more or less moments of reflection. We develop RiCE through i) an expert review of questionnaire items (n=10) and ii) an exploratory factor analysis (n=300) of the reviewed items. We also present a user study testing RiCE (n=58) across two time points (one week apart) with novel interfaces designed for creative writing and music making. Although we do not confirm validity, we identify four factors for RiCE which we suggest are interpretable in a conceptually meaningful way. Our formative studies contribute towards supporting future explorations on reflection with CSTs.

Identifying points of engagement from a person’s interaction with computers could be used to assess their experience and to adapt user interfaces in real-time. However, it is difficult to identify points of engagement unobtrusively; HCI studies typically use retrospective protocols or rely on cumbersome sensors for real-time analysis. We present a case study on how children compose digital music at home in which we remotely identify points of engagement from patterns of interaction with a musical interface. A mixed-methods approach is contributed in which video recordings of children’s interactions whilst composing are labelled for engagement and linked to i) interaction logs from the interface to identify indicators of engagement in interaction, and ii) interview data gathered using a remote video-cued recall technique to understand the experiential qualities of engaging interactions directly from users. We conclude by speculating on how the suggested indicators of engagement inform the design of adaptive music systems.