Conference Agenda
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Agenda Overview |
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D433: HUMAN-CENTRED ASPECTS OF AI
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Contextualizing sensor data: integrating user voice in data-driven design INDEX, Faculty of Environment, Science and Economy, University of Exeter, United Kingdom Data-driven design increasingly relies on sensor data, yet these thin measurements often lack the experiential context needed to explain why events occur or what users feel and need. This limits their value for human-centred design. Passive and active contextualisation are introduced to describe how meaning is produced through inference and user participation. A real-world case study using See.Sense cycling data from Melbourne shows how combining thin and thick data produces more situated understanding and actionable design insight. Interactive visualisation of collaborative dynamics: a VLM-based approach for behavior and affect analysis 1Technical University of Munich, Germany; 2Anhalt University of Applied Sciences, Germany Collaboration is crucial in design and management, fostering innovation, problem-solving, and decision-making. We explore the use of vision-language models (VLMs) for analyzing collaboration, focusing on detecting social behavior and group affect. By fusing multimodal cues, VLMs enable more context-aware reasoning beyond surface-level perception. We develop a pipeline, a structured prompt and an interactive visualization for integrating VLMs into the analysis workflow. Comparing VLM and human analysis results, we discuss how VLMs can advance collaboration analysis and the remaining challenges. Designing for dignity: a sociotechnical framework for AI-mediated systems University of Utah, United States of America This paper introduces Dignity-Centered Design (DCD), a sociotechnical framework for AI-mediated systems. While AI ethics often focuses on concepts such as fairness and transparency, DCD evaluates how systems shape lived experience, power dynamics, and human agency. Drawing on healthcare traditions and the Dignity Index, the framework articulates three dimensions (individual, relational, and systemic) alongside five core principles. It includes a Dignity Spectrum in AI System Design to assess design choices and applies these to healthcare AI to support reflective practice. Comparing human, LLM, and LLM-QFD approaches to technical requirement extraction 1Sprott School of Business, Carleton University, Canada; 2Faculty of Mechanical Engineering, University of Ljubljana, Slovenia This study investigates how large language models (LLMs) support extracting technical requirements from early product pitches. Mechanical engineering students worked under three conditions: manual, LLM-assisted, and LLM combined with a QFD interface. Both AI-assisted conditions improved requirement quality and lowered perceived difficulty. Thematic analysis showed cognitive effort shifted from generating requirements to evaluating and verifying AI outputs, while the LLM-only group reported the most positive attitudes. | ||
