The proliferation of efficiency-focused AI tools in creative processes threatens to undermine critical, reflective practices foundational to design education. This approach can lead to creativity exhaustion and diminished agency among designers and students. As an antidote, we propose Reflective AI: an approach grounded in slow technology principles that reframes AI not as a production tool, but as a medium for reflecting on the creative process itself. This paper presents the Objective Portrait Workshop where design students engaged in slowed data collection, annotation, and model finetuning. Our contribution is threefold: we (1) document a methodology for implementing Reflective AI in design education; (2) provide empirical evidence that slow engagement cultivates reflection on creative processes and technical understanding of AI; and (3) propose material and temporal disentanglement as core mechanisms for Reflective AI practice. This work offers a practical alternative to "fast"AI, providing methodology that cultivates critical capabilities essential to design.

1. A key tenet to this paper’s contribution is the assertion that while there are many specialized terminologies relating to more-than-human design – terms including but not limited to posthumanism...

Generative AI technologies demand new practical and critical competencies, which call on design to respond to and foster these. We present an exploratory study guided by Research-through-Design, in which we partnered with a primary school to develop a constructionist curriculum centered on students interacting with a generative AI technology. We provide a detailed account of the design of and outputs from the curriculum and learning materials, finding centrally that the reflexive and prolonged ‘hands-on’ approach led to a co-development of students’ practical and critical competencies. From the study, we contribute guidance for designing constructionist approaches to generative AI technology education; further arguing to do so with ‘critical responsivity.’ We then discuss how HCI researchers may leverage constructionist strategies in designing interactions with generative AI technologies; and suggest that Research-through-Design can play an important role as a ‘rapid response methodology’ capable of reacting to fast-evolving, disruptive technologies such as generative AI.

Growing concerns over negligent or malicious uses of AI have increased the appetite for tools that help manage the risks of the technology. In 2018, licenses with behaviorial-use clauses (commonly referred to as Responsible AI Licenses) were proposed to give developers a framework for releasing AI assets while specifying their users to mitigate negative applications. As of the end of 2023, on the order of 40,000 software and model repositories have adopted responsible AI licenses licenses. Notable models licensed with behavioral use clauses include BLOOM (language) and LLaMA2 (language), Stable Diffusion (image), and GRID (robotics). This paper explores why and how these licenses have been adopted, and why and how they have been adapted to fit particular use cases. We use a mixed-methods methodology of qualitative interviews, clustering of license clauses, and quantitative analysis of license adoption. Based on this evidence we take the position that responsible AI licenses need standardization to avoid confusing users or diluting their impact. At the same time, customization of behavioral restrictions is also appropriate in some contexts (e.g., medical domains). We advocate for “standardized customization” that can meet users’ needs and can be supported via tooling.

The Shadowplay installation facilitates creative and embodied interaction with a generative AI image diffusion model. The work aspires to facilitate a tangible experience of working with generative AI and produces striking, aesthetic, and provocative images exposing the edges of our creative relationships with this new class of technology. Exhibition-goers enter into a generative interplay between the probabilistic uncertainty of AI technologies, the familiarity of light, and the experimental playfulness of using one’s body to cast a shadow. By enabling a tangible interaction with this rapidly-evolving technology, we probe the quality and nature of AI-mediated imaginaries, while also providing a ‘lived’ means to experience the limits of its expressiveness and the inseparable ties to underlying training data. Shadowplay balances a provocative and critical stance on AI and creativity, produces a captivating stream of beautiful and striking imagery, and achieves this through an engaging, playful, and tangible interaction.

Artificial intelligence (AI) technologies are widely deployed in smartphone photography; and prompt-based image synthesis models have rapidly become commonplace. In this paper, we describe a Research-through-Design (RtD) project which explores this shift in the means and modes of image production via the creation and use of the Entoptic Field Camera. Entoptic phenomena usually refer to perceptions of floaters or bright blue dots stemming from the physiological interplay of the eye and brain. We use the term entoptic as a metaphor to investigate how the material interplay of data and models in AI technologies shapes human experiences of reality. Through our case study using first-person design and a field study, we offer implications for critical, reflective, more-than-human and ludic design to engage AI technologies; the conceptualisation of an RtD research space which contributes to AI literacy discourses; and outline a research trajectory concerning materiality and design affordances of AI technologies.

An ongoing mystery of HCI is how do well-intentioned designers consistently enable products with unintentionally evil consequences. Using “questionable values” as a lens, we retell and analyze four design scenarios for smart homes that were created by participants with an IoT toolkit we designed. The selected design scenarios reveal practices that violate principles of responsible smart home design. Through our analysis we show (1) how participants explore sensor-driven objectification of the home then leverage data for surveillance, nudging, and control over others; (2) how the dominant technosolutionist narratives of efficiency and productivity ground such questionable values; (3) and how the materiality of mass-produced sensors pre-mediates questionable design scenarios. We discuss how to attend to and utilize questionable values in design: Making space for questionable values will empower design researchers to better “look around corners”, anticipating tomorrow's concerns and forestalling the worst of their harms.

During a research project in which we developed a machine learning (ML) driven visualization system for non-ML experts, we reflected on interpretability research in ML, computer-supported collaborative work and human-computer interaction. We found that while there are manifold technical approaches, these often focus on ML experts and are evaluated in decontextualized empirical studies. We hypothesized that participatory design research may support the understanding of stakeholders' situated sense-making in our project, yet, found guidance regarding ML interpretability inexhaustive. Building on philosophy of technology, we formulated explanation strategies as an empirical-analytical lens explicating how technical explanations mediate the contextual preferences concerning people's interpretations. In this paper, we contribute a report of our proof-of-concept use of explanation strategies to analyze a co-design workshop with non-ML experts, methodological implications for participatory design research, design implications for explanations for non-ML experts and suggest further investigation of technological mediation theories in the ML interpretability space.

Previous research has shown the influence of smell on emotions, memories, and body image. However, most of this work has taken place in laboratory settings and little is known about the influence of smell in real-world environments. In this article, we present novel insights gained from a field study investigating the emotional effect of smell on memories and body image. Taking inspiration from the cultural design probes approach, we designed QuintEssence, a probe package that includes three scents and materials to complete three tasks over a period of four weeks. Here, we describe the design of QuintEssence and the main findings based on the outcomes of the three tasks and a final individual interview. The findings show similar results between participants based on the scent. For example, with cinnamon, participants experienced feelings of warmth, coziness, happiness, and relaxation; they recalled blurred memories of past moments about themselves and reported a general feeling of being calm and peaceful towards their bodies. Our findings open up new design spaces for multisensory experiences and inspire future qualitative explorations beyond laboratory boundaries.

Design research is important for understanding and interrogating how emerging technologies shape human experience. However, design research with Machine Learning (ML) is relatively underdeveloped. Crucially, designers have not found a grasp on ML uncertainty as a design opportunity rather than an obstacle. The technical literature points to data and model uncertainties as two main properties of ML. Through post-phenomenology, we position uncertainty as one defining material attribute of ML processes which mediate human experience. To understand ML uncertainty as a design material, we investigate four design research case studies involving ML. We derive three provocative concepts: thingly uncertainty: ML-driven artefacts have uncertain, variable relations to their environments; pattern leakage: ML uncertainty can lead to patterns shaping the world they are meant to represent; and futures creep: ML technologies texture human relations to time with uncertainty. Finally, we outline design research trajectories and sketch a post-phenomenological approach to human-ML relations.