Jul 2026· International Conference on Conversational User Interfaces· pp. 1-8· 0 citations· 66 references
Computer Science
TL;DR
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.
Abstract
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.
The advent of Large Language Models has accelerated interest in empathetic conversational agents. Despite a surge in empirical research, artificial empathy remains deeply fragmented, often serving as a catch-all term for diverse interactional phenomena. Addressing this conceptual gap, we systematically review 89 empirical studies to map how human-machine empathy is operationalized. Our synthesis reveals that empathy is highly situated and driven by functional goals, like health and well-being, transactional service, social interaction, and learning support. Within these contexts, we classify affective responsiveness by its directional flow, detailing how agents project, elicit, or mediate empathy. We structure the literature into a cohesive framework spanning linguistic, paralinguistic, identity, and architectural strategies. Furthermore, our methodological evaluation reveals a reliance on adapted clinical metrics, a scarcity of longitudinal studies, and a disproportionate focus on text-based over voice-based interfaces. Ultimately, this review equips researchers and practitioners with an actionable foundation for designing, measuring, and implementing contextually appropriate and empathetic agents.
Supriya Khadka, Smit Desai· International Conference on...· 0 citations
This study reconceptualizes contemporary communication by extending the traditional notion of conversation to include not only face-to-face interaction but also non-face-to-face exchanges, as well as spoken, written, and multimodal forms of expression. In this framework, multimodal communication is treated as the primary object of analysis, defined as the integration of linguistic and nonverbal resources in interaction.
To examine this phenomenon systematically, the study establishes an analytical framework based on five key elements of conversation: medium, setting, sensory modality, communicative purpose, and participant relationship. Drawing on data from television dramas alongside naturally occurring interactions, it explores how these elements are dynamically configured across diverse communicative contexts.
A multimodal corpus linguistic approach is employed as the methodological foundation to analyze the realization of speech acts, with particular attention to the interplay between linguistic expressions, prosodic features, and nonverbal behaviors. The analysis demonstrates that contemporary communication is characterized by considerable variability and flexibility, as multiple semiotic resources are strategically combined in response to contextual and interpersonal conditions.
The findings demonstrate that participant relationships and emotional states play a crucial role in shaping communicative practices, particularly in the organization and interpretation of actions in interaction. The study underscores the importance of understanding contemporary communication through corpus-driven multimodal analysis grounded in authentic discourse data. It further highlights the value of incorporating a conversation-analytic perspective on sequential interaction.
This research contributes to bridging theoretical perspectives on communication with empirical corpus-based analysis, offering meaningful implications for future research in conversation analysis and applied linguistics.
Kyongmi Yang· The Korean Society of Human...· 0 citations
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.
Manveer Kalirai, C. Wei, Thomas Essmeyer et al.· International Conference on...· 0 citations
As large language models become primary communication partners, the stylistic and communicative character of their output—specifically the degree to which it relies on intellectual, affective, or action-oriented language—shapes how users interpret, and act on what they read. Yet this property is rarely measured or controlled directly. We introduce the Intellect-Emotion-Action Profile (IEAP), a purpose-built lexical framework featuring an inductively constructed dictionary from AI-generated text. IEAP decomposes any response into the proportional usage of intellectual, affective, and action words. Using a single automated harvester, we elicited and scored responses from four contemporary architectures (Claude, ChatGPT, Grok, and Gemini) across five question domains under four instructional-mode directives that span a cold-to-fire register (
n
= 30 per architecture per domain). Three primary findings emerge. First, when no directive is applied, native communicative profiles are dominated by the underlying question, with architecture playing a secondary, architecture-dependent role; thus, the prompt establishes the baseline for the response. Second, an explicit mode directive displaces this profile substantially from its baseline. Specifically, the directives produced large, monotonic shifts in affective word usage, exceeding 50 percentage points in the most responsive domain with no reversals. Third, response depth alters length and conceptual breadth while leaving lexical composition essentially unchanged, establishing register and depth as orthogonal controls. These three dimensions correlated with human-rated NRC lexicons in the expected directions, supporting their validity. Within-domain robustness checks reproduce both the baseline profiles and the titration trajectories across multiple question framings. Because a measurable communicative property can be monitored and audited, these results position IEAP as a practical foundation for studying, comparing, and ultimately governing how AI systems communicate.
William C. Kouns· Frontiers in Artificial Inte...· 0 citations
Since the expansion of generative AI’s functions, chatbots such as Open AI’s Chat-GPT appear as more than tools. They can be perceived as communicative partners, shaping exchanges with high variability and linguistic competence. Current versions, such as Chat-GPT 5.5, even provide spoken language using different verbal styles and vocabulary, intonation and prosody. Investigating this new context of communication, we question whether people expect chatbots to detect non-literal meanings. Extending the illusory transparency of intention research paradigm introduced by Keysar, information was presented through scenarios of interactions with the Chat-GPT voice function that disambiguated the presented verbal information as either non-literal (sarcastic) or literal (sincere) in an within-subject experimental design (N = 327). Scenarios were placed within the higher education context. Results reveal that participants did not only falsely expect the chatbot to detect a non-literal meaning, but they also even expected the chatbot to reply accordingly. Implications for higher education contexts are discussed.
Lea Marie Petrasch, Regina Jucks· Frontiers in Psychology· 0 citations
Generative artificial intelligence (GenAI) has rapidly expanded from text-based assistance to voice-enabled dialogue, automated oral feedback, and multimodal speaking assessment. These developments appear to address a persistent problem in second-language (L2) education: learners need frequent, low-risk opportunities to speak, but teachers cannot always provide individual interaction and feedback at scale. However, the availability of an apparently fluent conversation partner does not establish that durable speaking development, fair assessment, or transfer to human communication will follow. This critical integrative review examines three questions: what learning and affective outcomes are associated with GenAI-supported L2 speaking practice; what limitations weaken its pedagogical value; and what implementation conditions support responsible use. Targeted searches of Google Scholar, publisher platforms, open research repositories, and citation chains produced an analytical corpus of 11 core publications, supported by 14 theoretical, methodological, assessment, feedback-literacy, and ethics sources published or retained for interpretation through July 2026. Evidence was appraised for contextual clarity, task alignment, outcome validity, feedback transparency, and the strength of claims. The synthesis indicates that GenAI can increase practice volume, support rehearsal, provide scenario-based interaction, and reduce fear of immediate human judgement. Adaptive prompts and rapid feedback may also support vocabulary retrieval, discourse organisation, pronunciation awareness, and willingness to communicate. Yet the evidence remains dominated by small samples, short interventions, self-report measures, prototype studies, and technical benchmarks. Recurring risks include inaccurate or overconfident feedback, accent and speech-recognition bias, unnatural interaction, dependency, privacy concerns, unequal access, and weak transfer evidence. The review proposes the SPEAK framework: Structured tasks, Progressive intelligibility-focused feedback, Equity and ethics, Agency and anxiety-sensitive practice, and Knowledgeable teacher oversight. GenAI should therefore be used as a structured rehearsal and feedback resource within teacher-designed speaking pedagogy, not as an autonomous replacement for human interaction or professional judgement.
D. Dasanayake· International journal of res...· 0 citations
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