Generative AI (GenAI) works via goal-directed computation, which differs fundamentally from human creative processes. This poses challenges for the intelligent support of creative experiences. We propose ``breakdowns'' as opportunities for the exchange of perspectives between human and machine. Breakdowns disrupt a flow and force us to consciously evaluate our ``being-in-the-world''. Between human and machine, breakdowns can function as openings for collaborative creative reflection. We are currently studying human-human creative interactions, to identify the markers of these inter-subjective openings, and to understand how they are used in a co-creative process. We present preliminary findings on breakdowns as a design principle for creativity support, prioritising human creative agency and meaningful reflection over automated content generation.
Current human-machine collaboration (HMC) systems rely on environment-facing sensors to observe visible actions and scene states, but the internal perceptual, intention-related, and state-related processes of operators remain insufficiently integrated into machine perception. Ele…
Artificial intelligence (AI) systems for automated Critical View of Safety (CVS) assessment in laparoscopic cholecystectomy are nearing clinical translation. Beyond algorithmic performance, clinical safety and effectiveness depend on the quality of the human-machine interface (HM…
Affect-adaptive systems increasingly act as communicators that sense a user's emotion and respond with events meant to change it, closing an affective loop. This vision assumes both that a machine's affective messages are received and that the bodily channel it monitors carries a…
Modern AI evaluation frameworks treat evaluator disagreement as noise to be resolved. In creative domains, professional disagreement reflects genuine differences in taste, not measurement error. We argue that evaluating creative AI requires preserving two distinct signals: conver…
AI agents that generate final answers based on user input often do not meet the needs of creative fields. Fields such as structural design and architecture need interactive systems that help users externalise and develop ideas, explore alternatives, and refine partial solutions.…
Visual analytics (VA) plays an increasingly important role in supporting machine learning (ML) workflows. In the field of visualization, such approaches and techniques are referred to as VIS4ML. While ML models are mostly learned automatically, the corresponding ML workflows rece…