Jul 2026· International Conference on Conversational User Interfaces· 0 citations· 73 references
Computer Science
TL;DR
This workshop aims to gather a multidisciplinary community to reflect on pressing challenges and uncertainties and explore risk analysis and mitigation strategies in CUI design and deployment and build an international network of scholars and practitioners to promote responsible and human-centred conversational AI.
Abstract
Conversational User Interfaces (CUIs) are rapidly advancing, moving from single-task assistants to powerful and engaging artificial agents. As CUIs become embedded in daily life, the implications for users interacting with such systems demand closer scrutiny. While some issues are immediately identifiable (e.g. privacy risks, misinformation, AI hallucinations), others may only become visible after longer periods of use, including over-reliance, erosion of human agency, or the normalization of biased or exclusionary language. This workshop aims to gather a multidisciplinary community to reflect on pressing challenges and uncertainties and explore risk analysis and mitigation strategies in CUI design and deployment. Through presentations, discussions, and collaborative design activities, participants will examine immediate and longitudinal risks and share empirical insights. The activities will support developing interdisciplinary frameworks to understand and handle unintended consequences of CUIs. The workshop seeks to build an international network of scholars and practitioners to promote responsible and human-centred conversational AI.
Everyday human-LLM interaction is not only answer delivery or cognitive offloading; it also contains measurable, selective and conditionally organized behavioural signatures of informal learning, which shift AI evaluation toward the preservation of cognitive opportunities for users to reason, test ideas and construct u...
Risks that may form on the user side during long and repeated interaction are examined to establish safety across a series of conversations, with the central claim that an acceptable response on its own does not establish safety across a series of conversations.
AffAdapt is presented, a seamless interaction design framework for AI-personas, which coordinates streaming speech recognition, proactive turn-management, persona-grounded response generation, a persistent emotional state, and synchronized embodied output into a single interaction loop.
Nishanth Chidambaram, K. Paliwal, Kayla Hom et al.· 1 citation· ⚡1
This workshop proposes a paradigm shift toward designing context-specific CUIs that actively support communicative success through pragmatic cues, and brings together perspectives from HCI and social sciences to explore how users appropriate conversational AI across domains.
Laura Spillner, Johanna Rockstroh, Paul Goerke et al.· International Conference on...· 0 citations
This work proposes a set of aspirational directions for guiding the behavior of general-purpose AI systems in ways that may reduce potential psychological harms and support user well-being, and identifies open questions and areas requiring deeper study.
KNIT-C is introduced, a conversational system that operationalises computational boundary objects within synchronous multi-user dialogue, eliciting individual viewpoints, synthesising group perspectives, and guiding discussion toward negotiated outcomes, finding that conversational engagement deepened individual reflec...
Echo Wan, C. Mougenot, Malak Sadek· International Conference on...· 0 citations
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