The establishment of BIM raises issues at the transition between sketches, still mostly used in ideation, and project digital models. With the latest AI evolution, one way of supporting this transition is to provide sketch interpretation software generating digital representations. However, the question arises as to whether these two modes of representation can co-exist, and whether digital representations can provide a resource for designers in the preliminary phase. In this paper, we investigate the impact of auto-generated representations on the preliminary design activity of architects. We implement a Wizard of Oz protocol with nine participants sketching their design and being provided with CAD plans, 3D models and inspirational images. We study reflexive conversations by analyzing and categorizing behavior patterns preceding and following the reception of self-generated representations. We identify 23 patterns, and show the flexible role of the digital representations, having a strong impact on the process.

As global populations age, ensuring safe and independent mobility for older adults is a critical priority for urban planning and public health. Mobility in the built environment involves complex, moment-to-moment interactions, yet current assessment methods often rely on subjective surveys or static geospatial data that fail to capture dynamic human responses. Furthermore, these methods rarely address the specific needs of vulnerable individuals, such as those with knee osteoarthritis or a history of falls, who are most sensitive to environmental barriers. Hence, there is a lack of high-resolution, objective tools capable of quantifying how micro-scale built environmental features directly influence the physiological and kinematic states of these populations. Here we present an automated multimodal framework that integrates wearable motion sensors, physiological monitors, eye-tracking, and dual-perspective video to reconstruct a holistic profile of the walking experience. The framework employs SLAM and computer vision techniques to achieve precise tracking and environmental profiling. By synchronizing these inputs, the framework maps physiological and kinematic responses to specific built environment features. This overcomes the limitations of GNSS-denied settings and manual annotation, revealing micro-scale adaptations, including gait speed changes on slopes or during road crossings, that are often missed by conventional aggregate analyses. The resulting multimodal dataset captures the nuanced interplay between the built environment and vulnerable older adults. The computational pipelines and a specialized walkway surface material dataset are made publicly available. This work lays the foundation for large-scale, data-driven assessments of urban walkability, informing evidence-based design interventions to enhance safety and social engagement for aging societies.

One of the most exciting moments in a prenatal ultrasound session is learning the sex of the baby.Following a conversation analysis perspective, we present a multimodal analysis of sequences of interaction between patient and practitioner at the time the foetus’ sex is the focus of attention. Based on video data collected from maternity wards and private practitioners, we report on two types of sequences, which illustrate the different ways of responding to the perceptually-occasioned formulation of the foetus’ sex: as a telling or as a noticing (in which case participants orient towards jointly seeing). While the possibility of both response is inherent to the sequential properties of noticing-based claims in general, we will discuss how the production of both types of sequences is sensitive and articulated to the distribution of epistemic authority as a practical achievement in this medical setting, along two dimensions: expert versus ordinary knowledge, and professional vision versus lay gaze.

We explore the use of mediated visual touch in distant social interaction. Adopting an interactionist and collaborative approach to human communication, we focus on the communicative functions of distant touch behaviours which interactants co-elaborate throughout their mediated interactions. Our results highlight: (1) a variety of visual-touch functions with a redistribution of functions mostly supported by other modalities of communication in face-to-face interactions, such as illustrating aspects of the ongoing conversation; (2) the visual-touch characteristics as well as the verbal, paraverbal and non-verbal indicators of the interactional context considered by the participants to make sense of the stimuli and; (3) the multifactorial and dynamic aspects of the co-elaboration process of the visual-touch functions, reaffirming the role of interactional context, combined with cultural and biographical knowledge, in the meaning making.

We present analyses of how disagreements are managed across socio-technical and dialogical spaces in French Wikipedia, with respect to two case studies, on Freud and the Turin Shroud. We show, on one hand, that moderators’ attempts to resolve disagreements by requiring participants to cite sources simply displace conflicts to the nature of those sources, their validity, their authors and the good faith of their proponents. Disagreements are managed rather than resolved dialectically by displacing them to alternative socio-technical spaces, such as different sections of the text itself, or participants’ personal pages.

This research delves into the world of conversation analysis, focusing on the unique conversational agent, Lenny. In contrast to most modern AI-based chatbots, Lenny employs a minimalistic approach, utilizing pre-recorded turns to engage with unsolicited callers and extend interactions. The study aims to dissect how Lenny’s long turns contribute to displaying ‘his’ personhood. By analyzing Lenny’s long turn in interaction, we uncover how it consolidates Lenny’s relatable character. Through analysis of a corpus of recorded interactions, the paper highlights the role of turn design in simulating human-like interactions. Ultimately, this research offers insights into the interplay between scripted content and human understanding in live conversations.