Proactivity has become a central concept in research on conversational user interfaces and human–computer interaction. It is an evolutionary stage for conversational agents. Nevertheless, despite the growing research on this topic, the term remains conceptually underspecified and inconsistently applied. Systems that send reminders or recommend content are often labeled as proactive, even when their underlying mechanisms and intentions differ fundamentally. This provocation argues that current uses of the term proactivity in the context of conversational AI are overly broad and conceptually imprecise, which limits our ability to design, compare, and evaluate proactive conversational agents. We synthesize perspectives from human-computer interaction, sociology, and psychology to propose a principled definition and a conceptual framework for proactive conversational agents to distinguish proactive from reactive and other related system behaviors.
Matthias Kraus, Sebastian Zepf, Jan Leusmann et al.· International Conference on...· 0 citations
The rise of LLMs has enabled CUIs to increasingly mimic human social and conversational cues, e.g., tone of voice and emotional expressions. However, this mimicry usually lacks strategic communicative intent, placing the cognitive burden of mutual understanding on the user. At the same time, CUIs based on general-purpose, task-agnostic LLMs are being deployed across varied domains with distinct, context-specific conversational needs, including, e.g., healthcare, education, and journalism. Therefore, there is a growing need to transition from arbitrary, domain-agnostic generation of pragmatic cues to strategic adaptation of both visual and linguistic interface features. This workshop proposes a paradigm shift toward designing context-specific CUIs that actively support communicative success through pragmatic cues. Bringing together perspectives from HCI and social sciences, we will explore how users appropriate conversational AI across domains. Through cross-disciplinary dialogue, the workshop aims to establish a shared vocabulary, identify domain-specific challenges, and lay the groundwork for future collaboration.
Laura Spillner, Johanna Rockstroh, Paul Goerke et al.· International Conference on...· 0 citations
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.
Manveer Kalirai, C. Wei, Thomas Essmeyer et al.· International Conference on...· 0 citations
While participants rated hedged and unhedged AI as equally trustworthy and likely to be correct, they were significantly less likely to follow hedged advice in a binary choice, and how linguistic markers can be used to calibrate user reliance to model certainty is discussed.
Laura Spillner, Johanna Rockstroh, Nina Wenig et al.· International Conference on...· 0 citations
