While conversational agents’ (CAs) semantic and syntactic capabilities have advanced, their pragmatic skills, using language appropriately in context, have emerged as a critical focus in practical applications. Hence, scholars integrate conversational skills derived from human-human interaction into CA designs. However, existing research mainly adopts an empirical approach and focuses on specific CA deployment, making it challenging to identify overarching patterns or develop a comprehensive methodology for transferring human pragmatic skills to CA design. Thus, we conducted a systematic review of 85 studies from primary databases (e.g., ACM, IEEE, etc.), focusing on designing CAs with human-derived conversational skills. We identified skill categories (verbal, paralinguistic, nonverbal), transfer strategies (from dialog data, theories, and via co-design), implementations, and evaluation metrics. We consolidated these insights into a four-stage design process: human skill exploration, definition, transfer, and iterative evaluation. Future research can leverage this to design CAs that achieve conversational goals through contextually appropriate language use.

User-centered design necessitates researchers deeply understanding target users throughout the design process. However, during early-stage user interviews, researchers may misinterpret users due to time constraints, incorrect assumptions, and communication barriers. To address this challenge, we introduce InsightBridge , a tool that supports real-time, AI-assisted information synthesis and visual-based verification. InsightBridge automatically organizes relevant information from ongoing interview conversations into an empathy map. It further allows researchers to specify elements to generate visual abstracts depicting the selected information, and then review these visuals with users to refine the visuals as needed. We evaluated the effectiveness of InsightBridge through a within-subject study (N=32) from both the researchers' and users' perspectives. Our findings indicate that InsightBridge can assist researchers in note-taking and organization, as well as in-time visual checking, thereby enhancing mutual understanding with users. Additionally, users' discussions of visuals prompt them to recall overlooked details and scenarios, leading to more insightful ideas.

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.

The design of a scalable Quantum Control Architecture (QCA) faces two primary challenges.First, the continuous growth in qubit counts has rendered distributed QCA inevitable, yet the nondeterministic latencies inherent in feedback loops demand cycleaccurate synchronization across multiple controllers.Existing synchronization strategies -whether lock-step or demand-drivenintroduce significant performance penalties.Second, existing quantum instruction set architectures are polarized, being either too abstract or too granular.This lack of a unifying design necessitates recurrent hardware customization for each new control requirement, which limits the system's reconfigurability and impedes the path toward a scalable and unified digital microarchitecture.Addressing these challenges, we propose Distributed-HISQ, featuring: (i) HISQ, A universal instruction set that redefines quantum control with a hardware-agnostic design.By decoupling from quantum operation semantics, HISQ provides a unified language for control sequences, enabling a single microarchitecture to support various control methods and enhancing system reconfigurability.(ii) BISP, a booking-based synchronization protocol that can potentially achieve zero-cycle synchronization overhead.The feasibility and adaptability of Distributed-HISQ are validated through its implementation on a commercial quantum control system targeting superconducting qubits.We performed a comprehensive evaluation using a customized quantum software stack.Our results show that BISP effectively synchronizes multiple control boards, leading to a 22.8% reduction in average program execution time and a ∼ 5× reduction in infidelity when compared to an existing lock-step synchronization scheme.

The ability to coordinate turn taking during spoken dialogue is crucial for an embodied spoken dialogue system (SDS), e.g., in a humanoid robot. The SDS needs to model transitions in the conversational floor, which describes each party’s stance (either speaking or listening). Further, the SDS needs to signal its perception of the floor to the human, so that they can coordinate floor transitions and resolve conflicts. Conventional SDS employ standalone modules to control floor transitions but do not produce timely and appropriate responses. Recent end-to-end audio LLMs generate responses quickly, but do not coordinate floor transitions as accurately. In this work, we propose an SDS architecture that dynamically adjusts its prompts to an end-to-end audio LLM based upon its perception of the conversational floor state. The LLM output determines not only the audio output, but also the perceived floor state. This enables the system to signal its stance to the human, both when listening and when speaking. We conducted an experiment where a humanoid robot administered a semi-structured interview with human subjects. Results show that, compared with baseline systems using static prompts, dynamic prompting enables the LLM to model floor transitions more accurately, to generate more appropriate signalling, and to interrupt less, leading to smoother turn-taking in dialogue.

The integration of Large Language Models (LLMs), especially ChatGPT, into education is poised to revolutionize students' learning experiences by introducing innovative conversational learning methodologies. To empower students to fully leverage the capabilities of ChatGPT in educational scenarios, understanding students' interaction patterns with ChatGPT is crucial for instructors. However, this endeavor is challenging due to the absence of datasets focused on student-ChatGPT conversations and the complexities in identifying and analyzing the evolutional interaction patterns within conversations. To address these challenges, we collected conversational data from 48 students interacting with ChatGPT in a master's level data visualization course over one semester. We then developed a coding scheme, grounded in the literature on cognitive levels and thematic analysis, to categorize students' interaction patterns with ChatGPT. Furthermore, we present a visual analytics system, StuGPTViz, that tracks and compares temporal patterns in student prompts and the quality of ChatGPT's responses at multiple scales, revealing significant pedagogical insights for instructors. We validated the system's effectiveness through expert interviews with six data visualization instructors and three case studies. The results confirmed StuGPTViz's capacity to enhance educators' insights into the pedagogical value of ChatGPT. We also discussed the potential research opportunities of applying visual analytics in education and developing AI-driven personalized learning solutions.

Humanoid robots are promising approach to automating patient interviews routinely conducted by medical staff. Their human-like appearance enables them to use the full gamut of verbal and behavioral cues that are critical to a successful interview. On the other hand, anthropomorphism can induce expectations of human-level performance by the robot. Not meeting such expectations degrades the quality of interaction. Specifically, humans expect rich real-time interactions during speech exchange, such as backchanneling and barge-ins. The nature of the patient interview task differs from most other scenarios where task oriented dialogue systems have been used, as there is increased potential of engagement breakdown during interaction. We describe a dialogue system architecture that improves the performance of humanoid robots on the patient interview task. Our architecture adds a nested inner real-time control loop to improve the timeliness of the robot’s responses based on the notion of "stance", an elaboration of the concept of a "turn", common in most existing dialogue systems. It also expands the dialogue state to monitor not only task progress, but also human engagement. Experiments using a humanoid robot running our proposed architecture reveal improved performance on interview tasks in terms of the perceived timeliness of responses and users’ impressions of the system.

In Community-Based Question Answering (CQA) platforms, people can participate in discussions about non-factoid topics by marking their stances, providing premises, or arguing for the opinions they support, which forms “collective arguments”. The sustainable development of collective arguments relies on a big contributor base, yet most of the frequent CQA users are lurkers who seldom speak out. With a formative study, we identified detailed obstacles preventing lurkers from contributing to collective arguments. We consequently designed a processing pipeline for extracting and summarizing augmentative elements from question threads. Based on this we built CoArgue, a tool with navigation and chatbot features to support CQA lurkers’ motivation and ability in making contributions. Through a within-subject study (N=24), we found that, compared to a Quora-like baseline, participants perceived CoArgue as significantly more useful in enhancing their motivation and ability to join collective arguments and found the experience to be more engaging and productive.

An effective task-oriented chatbot should be able to exert a certain level of Social Intelligence (SI), the ability to emulate human social behaviors to reduce user frustration and dissatisfaction. However, few studies explored using humor, a common rhetorical device in human-human interactions, to improve chatbots’ overall SI. To fill this gap, we proposed to apply self-mockery humor to a customer service chatbot in different interaction stages with users. We proposed a pipeline to create situated self-mockery for the chatbot and conducted a within-subject experiment (N=28) to compare it with a chatbot without self-mockery utterance. Results showed that the self-mockery chatbot was perceived as significantly funnier, more satisfactory, and delivering higher performance in two out of the five measured characteristics of SI with comparable performance in the rest. We further discussed how participants’ individual factors might affect the perceived helpfulness of self-mockery on SI and concluded with design considerations.

Community-based Question Answering (CQA) platforms can provide rich experience and suggestions for people who seek to construct Activity Plans (AP), such as bodybuilding or sightseeing. However, answer posts in CQA platforms could be too unstructured and overwhelming to be easily applied to AP construction, as validated by our formative study for understanding relevant user challenges. We therefore proposed an answer-post processing pipeline, based on which we built PlanHelper, a tool assisting users in processing the CQA information and constructing AP interactively. We conducted a within-subject study (N=24) with a Quora-like interface as the baseline. Results suggested that when creating AP with PlanHelper, users were significantly more satisfied with the informational support and more engaged during the interaction. Moreover, we performed an in-depth analysis on the user behaviors with PlanHelper and summarized the design considerations for such supporting tools.

Facial expression is an important facial semantics on visual aspect. The facial expressions synthesis has a wide range of applications in human-computer interaction and virtual reality. In recent years, image synthesis base on generative adversarial networks(GANs) is developing rapidly. In the image-to-image translation work, we propose a new facial expression generation method base on the idea of conditional GANs and realize the optimization of the generated results. The main work of this paper includes: Editable facial lines map is utilized as a constraint, combining with neutral face images as inputs of generator, so that a variety of facial expression images can be generated by editing the constraints. Correntropy loss of feature matching is added, which is used to measure the intermediate representation between the real images and the generated images by improving the adversarial loss. Consequently, the generated facial expressions can be more realistic. Base on the ideas above, the proposed method needs only one generator to generate different realistic facial images with various expressions.

Dynamic facial expression synthesis has some wild applications in human-computer interaction and virtual reality. The popular data-driven synthesis method like generative adversarial network (GAN) has made a great progress in generating a single face image, but has not well performed for expression sequences. To solve this problem, we design a series of deformable semantic parts to represent facial geometrical movement. And we synthesize the facial appearance by the geometrical driven under the-state-of-art pix2pixHD framework. In order to maintain the person identity among image sequence, we utilize an encoder to constrain the attributes of target face. With the above efforts, our method is capable to synthesize satisfied dynamic facial expression sequences.