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.

AI is transforming live streaming with new communities forming around channels featuring automated or prompt-based AI-generated videos. By introducing AI as an active and sometimes unpredictable participant in community interactions, traditional creator-audience and audience-audience relations are disrupted, presenting challenges for toxicity and moderation. This paper presents findings from a mixed-methods survey of viewers ( ) to understand their motivations for engaging with AI content, perceptions of toxicity, and moderation preferences. Our findings highlight the variety of viewer motivations of the participants of the surveyed communities with both individual and social aspects, that viewers were more engaged by AI interactions and user-generated prompts than by other viewers, and that toxicity arose from various sources and negatively related to the sense of community, with most viewers favouring in-community moderation. Our insights represent an initial descriptive examination of communities that rely exclusively on fast-paced AI-generated content and underscore the need for updated moderation strategies to sustain healthier dynamics in AI livestreams • We study new livestream communities around AI-generated videos • We identify individual and social motivations of viewers • We identify sources of toxicity in the communities • We explore how the community members react to toxicity and think about moderation

AI Agents increasingly leverage speech synthesis models to communicate with their users. This study explores the integration of breathing patterns into synthesized speech to deepen empathy towards AI agents. The research methodologically diverges from traditional empathy studies and speech evaluation standards by proposing to the participants the resolution of an emotional dilemma within a cooperative game scenario, where they face a choice reflecting their empathetic engagement with an AI partner. The introduction of a novel speech assessment method that takes into account the interactive and contextual aspects of conversational speech is the first novelty of the paper. The second novelty is in the findings which indicate that breathing in synthesized speech significantly enhances agents' perceived naturalness and users' empathy towards them.

When a task is repeated, it becomes part of procedural memory.This type of memory dedicated to movement is called 'muscle memory', which allows one to perform actions unconsciously.Within the context of social media, muscle memory builds up if one uses SM applications frequently.In this paper, we investigate the effects of muscle memory within Instagram, and report the following findings: We designed a user study examining the speed and accuracy of using a newly changed interface which showed slower reaction time and more errors.Combining these results with users' perceived feelings lead us to conclude that in specific UX interface changes muscle memory can be applied as a dark pattern.

Artificial intelligence (AI) is increasingly being viewed as a creative partner rather than as a tool. How to design such collaborations is still a subject of speculation. In this pictorial, we propose a collaborative role for AI to prompt self-reflection. We explore this through a practice-based inquiry of whether and how AI could help a designer reflect on and relate to their own work. Three designers annotate a collection of images representing their fascinations, with subjective labels, indicating different dimensions of their visual concepts. These labels are used to teach an object detection model the designers’ perspectives. Then, they used this trained model on their own design work to evaluate the AI's potential to prompt self-reflection. By describing this process of AI-training we explore how an AI can help us become aware of our own implicit perspectives.

Breathing patterns are shown to have strong correlations with emotional states, and hence have promise for automatic mood order prediction and analysis. An essential challenge here is the lack of ground truth for breathing sounds, especially for medical and archival datasets. In this study, we provide a cross-dataset approach for breathing pattern prediction and analyse the contribution of predicted breath signals for the detection of depressive states, using the DAIC-WOZ corpus. We use interpretable features in our models to provide actionable insights. Our experimental evaluation shows that in participants with higher depression scores (as indicated by the eight-item Patient Health Questionnaire, PHQ-8), breathing events tend to be shallow or slow. We furthermore tested linear and non-linear regression models with breathing, linguistic sentiment and conversational features, and show that these simple models outperform the AVEC17 Real-life Depression Recognition Sub-challenge baseline.

When artists express their feelings through the artworks they create, it is believed that the resulting works transform into objects with “emotions” capable of conveying the artists' mood to the audience. There is little to no dispute about this belief: Regardless of the artwork, genre, time, and origin of creation, people from different backgrounds are able to read the emotional messages. This holds true even for the most abstract paintings. Could this idea be applied to machines as well? Can machines learn what makes a work of art “emotional”? In this work, we employ a state-of-the-art recognition system to learn which statistical patterns are associated with positive and negative emotions on two different datasets that comprise professional and amateur abstract artworks. Moreover, we analyze and compare two different annotation methods in order to establish the ground truth of positive and negative emotions in abstract art. Additionally, we use computer vision techniques to quantify which parts of a painting evoke positive and negative emotions. We also demonstrate how the quantification of evidence for positive and negative emotions can be used to predict which parts of a painting people prefer to focus on. This method opens new opportunities of research on why a specific painting is perceived as emotional at global and local scales.

Using the Arts & Humanities Citation Index (A&HCI) 2008, we apply mapping techniques previously developed for mapping journal structures in the Science and Social Sciences Citation Indices. Citation relations among the 110,718 records were aggregated at the level of 1,157 journals specific to the A&HCI, and the journal structures are questioned on whether a cognitive structure can be reconstructed and visualized. Both cosine-normalization (bottom up) and factor analysis (top down) suggest a division into approximately 12 subsets. The relations among these subsets are explored using various visualization techniques. However, we were not able to retrieve this structure using the Institute for Scientific Information Subject Categories, including the 25 categories that are specific to the A&HCI. We discuss options for validation such as against the categories of the Humanities Indicators of the American Academy of Arts and Sciences, the panel structure of the European Reference Index for the Humanities, and compare our results with the curriculum organization of the Humanities Section of the College of Letters and Sciences of the University of California at Los Angeles as an example of institutional organization.

Abstract The possibilities of using the Arts & Humanities Citation Index (A&HCI) for journal mapping have not been sufficiently recognized because of the absence of a Journal Citations Report (JCR) for this database. A quasi‐JCR for the A&HCI ( 2008 ) was constructed from the data contained in the Web of Science and is used for the evaluation of two journals as examples: Leonardo and Art Journal. The maps on the basis of the aggregated journal–journal citations within this domain can be compared with maps including references to journals in the Science Citation Index and Social Science Citation Index. Art journals are cited by (social) science journals more than by other art journals, but these journals draw upon one another in terms of their own references. This cultural impact in terms of being cited is not found when documents with a topic such as “digital humanities” are analyzed. This community of practice functions more as an intellectual organizer than a journal.