Session A1 - GenAI-Enhanced Communication

GenAI-Enhanced Communication

​​Session Host​: Zeyu Huang

Rambler in the Wild: A Diary Study of LLM-Assisted Writing With Speech

​​Speaker​​: Xuyu Yang, City University of Hong Kong

​​Abstract​​: Speech-to-text technologies have been shown to improve text input efficiency and potentially lower the barriers to writing. Recent LLM-assisted dictation tools aim to support writing with speech by bridging the gaps between speaking and traditional writing. This case study reports on the real-world writing experiences of twelve academic or creative writers using one such tool – Rambler, to write various articles such as blog posts, diaries, screenplays, notes, or fictional stories, etc. Through a ten-day diary study, we identified the participants’ in-context writing strategies using Rambler, such as how they expanded from an outline or organized their loose thoughts for different writing goals. The interviews uncovered the psychological and productivity affordances of writing with speech, pointing to future directions of designing for this writing modality and the utilization of AI support.

Scaffolded Turns and Logical Conversations: Designing Humanized LLM-Powered Conversational Agents for Hospital Admission Interviews

​​Speaker​​: Dingdong Liu, The Hong Kong University of Science and Technology

​​Abstract​​: Hospital admission interviews are critical for patient care but strain nurses’ capacity due to time constraints and staffing shortages.While LLM-powered conversational agents (CAs) offer automation potential, their rigid sequencing and lack of humanized communication skills risk misunderstandings and incomplete data capture.Through participatory design with clinicians and volunteers, we identified essential communication strategies and developed a novel CA that implements these strategies through: (1) dynamic topic management using graph-based conversation flows, and (2) context-aware scaffolding with few-shot prompt tuning.Technical evaluation on an admission interview dataset showed our system achieving performance comparable to or surpassing human-written ground truth, while outperforming prompt-engineered baselines.A between-subject study (N=44) demonstrated significantly improved user experience and data collection accuracy compared to existing solutions.We contribute a framework for humanizing medical CAs by translating clinician expertise into algorithmic strategies, alongside empirical insights for balancing efficiency and empathy in healthcare interactions, and considerations for generalizability.

“Ronaldo’s a poser!”: How the Use of Generative AI Shapes Debates in Online Forums

​​Speaker​​: Yuhan Zeng, City University of Hong Kong

​​Abstract​​: Online debates can enhance critical thinking but may escalate into hostile attacks. As humans are increasingly reliant on Generative AI (GenAI) in writing tasks, we need to understand how people utilize GenAI in online debates. To examine the patterns of writing behavior while making arguments with GenAI, we created an online forum for soccer fans to engage in turn-based and free debates in a post format with the assistance of ChatGPT, arguing on the topic of “Messi vs Ronaldo”. After 13 sessions of two-part study and semi-structured interviews with 39 participants, we conducted content and thematic analyses to integrate insights from interview transcripts, ChatGPT records, and forum posts. We found that participants prompted ChatGPT for aggressive responses, created posts with similar content and logical fallacies, and sacrificed the use of ChatGPT for better human-human communication. This work uncovers how polarized forum members work with GenAI to engage in debates online.

JournalAIde: Empowering Older Adults in Digital Journal Writing

​​Speaker​​: Shixu Zhou, The Hong Kong University of Science and Technology

​​Abstract​​: Digital journaling offers a means for older adults to express themselves, document their lives, and engage in self-reflection, contributing to the maintenance of cognitive function and social connectivity. Although previous works have investigated the motivations and benefits of digital journaling for older adults, little technical support has been designed to offer assistance. We conducted a formative study with older adults and uncovered their encountered challenges and preferences for technical support. Informed by the findings, we designed a Large Language Model (LLM) empowered tool, JournalAIde, which provides vicarious experience, idea organization, sample text generation, and visual editing cues to enhance older adults鈥?confidence, writing ability, and sustained attention during digital journaling. Through a between-subjects study and a field deployment, we demonstrated the JournalAIde鈥檚 significant effectiveness compared to a baseline system in empowering older adults in digital journaling. We further investigated older adults’ experiences and perceptions of LLM writing assistance.

HarmonyCut: Supporting Creative Chinese Paper-cutting Design with Form and Connotation Harmony

​​Speaker​​: Huanchen Wang, Southern University of Science and Technology

​​Abstract​​: Chinese paper-cutting, an Intangible Cultural Heritage (ICH), faces challenges from the erosion of traditional culture due to the prevalence of realism alongside limited public access to cultural elements. While generative AI can enhance paper-cutting design with its extensive knowledge base and efficient production capabilities, it often struggles to align content with cultural meaning due to users’ and models’ lack of comprehensive paper-cutting knowledge. To address these issues, we conducted a formative study (N=7) to identify the workflow and design space, including four core factors (Function, Subject Matter, Style, and Method of Expression) and a key element (Pattern). We then developed HarmonyCut, a generative AI-based tool that translates abstract intentions into creative and structured ideas. This tool facilitates the exploration of suggested related content (knowledge, works, and patterns), enabling users to select, combine, and adjust elements for creative paper-cutting design. A user study (N=16) and an expert evaluation (N=3) demonstrated that HarmonyCut effectively provided relevant knowledge, aiding the ideation of diverse paper-cutting designs and maintaining design quality within the design space to ensure alignment between form and cultural connotation.

ACKnowledge: A Computational Framework for Human Compatible Affordance-based Interaction Planning in Real-world Contexts

​​Speaker​​: Ziqi Pan, The Hong Kong University of Science and Technology

​​Abstract​​: Intelligent agents coexisting with humans often need to interact with human-shared objects in environments. Thus, agents should plan their interactions based on objects’ affordances and the current situation to achieve acceptable outcomes. How to support intelligent agents’ planning of affordance-based interactions compatible with human perception and values in real-world contexts remains under-explored. We conducted a formative study identifying the physical, intrapersonal, and interpersonal contexts that count to household human-agent interaction. We then proposed ACKnowledge, a computational framework integrating a dynamic knowledge graph, a large language model, and a vision language model for affordance-based interaction planning in dynamic human environments. In evaluations, ACKnowledge generated acceptable planning results with an understandable process. In real-world simulation tasks, ACKnowledge achieved a high execution success rate and overall acceptability, significantly enhancing usage-rights respectfulness and social appropriateness over baselines. The case study’s feedback demonstrated ACKnowledge’s negotiation and personalization capabilities toward an understandable planning process.

Exploring the Design of LLM-based Agent in Enhancing Self-disclosure Among the Older Adults

​​Speaker​​: Yijie Guo, Tsinghua Univeristy

​​Abstract​​: Social difficulties have become an increasingly serious issue among older adults. For older adults, regular self-disclosure is essential for maintaining mental health and building close relationships. Leveraging conversational agents to encourage self-disclosure in older adults has shown increasing potential. Understanding how LLM-based agents can influence and stimulate self-disclosure across different topics is crucial for designing future agents tailored to older users. This study introduces Disclosure-Agent, an LLM-based conversational agent, and examines its impact on self-disclosure in older adults through a user study involving 20 participants, 8 topics, and two interactive interfaces equipped with Disclosure-Agent. The findings provide valuable insights into how LLM-based agents can promote self-disclosure in older adults and offer design recommendations for future elderly-oriented conversational agents.