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.

Effectively providing emotional support is a critical yet intricate interpersonal skill. Supporters often lack accessible and practical training opportunities to develop this competency. To address this gap, we introduce ESR-Coach, a Large Language Model (LLM)-based coaching system designed to train individuals in emotionally supportive communication. ESR-Coach leverages multiple AI agents to generate practice scenarios, demonstrate reference responses, and provide assessments on user practice replies. We evaluate the proficiency of our system on these three tasks, demonstrating high-fidelity case generation, helpful exemplary responses, and valid response assessments. In our user study (N=20), ESR-Coach helped participants achieve an average improvement of 17% in response helpfulness. After training, participants also employed more diverse and effective strategies. We further discuss the social intelligence of LLMs and their potential to foster humans’ interpersonal skills in real-world scenarios.

Collective memory—community members' interconnected memories and impressions of the group—is essential to the community's culture and identity. Its development requires members' continuous participatory contribution and sensemaking. However, existing works mainly adopt a holistic sociological perspective to analyze well-developed collective memory, less focusing on member-level conceptualization of this possession or what the co-contribution practices can be. Therefore, this work alternatively adopts the latter perspective and probes such interpretative and interactional patterns with two mobile systems. With one being a locative narrative and exploration system condensed from existing literature's design frameworks, and the other being a conventional online forum representing current practices, they served as the anchors of observation for our two-week, mixed-methods field study (n=38) on a university campus. A core debate we have identified was to retrospectively contemplate or document the presence as a history for the future. This also subsequently impacted the narrative focuses, expectations of collective memory constituents, and the ways participants seek inspiration from the group. We further extracted design considerations that could better embrace the diverse conceptualizations of collective memory and bond different community members together. Lastly, revisiting and reflecting on our design, we provided extra insights on designing devoted locative narrative experiences for community-driven UGC platforms.

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.

Regular screening is critical for individuals at risk of neurocognitive disorders (NCDs) to receive early intervention. Conversational agents (CAs) have been adopted to administer dialog-based NCD screening tests for their scalability compared to human-administered tests. However, unique communication skills are required for CAs during NCD screening, e.g., clinicians often apply scaffolding to ensure subjects’ understanding of and engagement in screening tests. Based on scaffolding theories and analysis of clinicians’ practices from human-administered test recordings, we designed a scaffolding framework for the CA. In an exploratory wizard-of-Oz study, the CA empowered by ChatGPT administered tasks in the Grocery Shopping Dialog Task with 15 participants (10 diagnosed with NCDs). Clinical experts verified the quality of the CA’s scaffolding and we explored its effects on task understanding of the participants. Moreover, we proposed implications for the future design of CAs that enable scaffolding for scalable NCD screening.

Elicitation diary studies, a type of qualitative, longitudinal research method, involve participants to self-report aspects of events of interest at their occurrences as memory cues for providing details and insights during post-study interviews. However, due to time constraints and lack of motivation, participants’ diary entries may be vague or incomplete, impairing their later recall. To address this challenge, we designed an automatic contextual information recording agent, DiaryHelper, based on the theory of episodic memory. DiaryHelper can predict five dimensions of contextual information and confirm with participants. We evaluated the use of DiaryHelper in both the recording period and the elicitation interview through a within-subject study (N=12) over a period of two weeks. Our results demonstrated that DiaryHelper can assist participants in capturing abundant and accurate contextual information without significant burden, leading to a more detailed recall of recorded events and providing greater insights.

The reliability of routing algorithms is critical for network operations. Deep Reinforcement Learning (DRL)-based routing algorithms are emerging as promising solutions for achieving next-generation intelligent network routing. However, existing DRL-based routing algorithms struggle to handle topological changes such as link failures due to the restriction of traditional neural networks. In this paper, we propose a novel routing algorithm named PPO-TEGN for enhancing robustness against topological changes. PPO-TEGN leverages Proximal Policy Optimization (PPO) framework for training, and incorporates a meticulously designed Transformer-based Edge-Enhanced Graph Neural Network (TEGN) to extract graph information for probabilistic routing scenarios. Additionally, motivated by the analysis of traffic distribution, we propose a subgraph-enhancement mechanism to improve the ability of perceiving potential congestion at the source and destination nodes. We conduct experiments on simulated and real-world topologies in different traffic patterns. Experimental results reveal that PPO-TEGN outperforms baselines including the traditional routing algorithm and DRL-based routing algorithms with basic GNN in reducing end-to-end (E2E) delay in networks with link failures, implying the strong robustness of the proposed algorithm.

As a crucial role in communication networks, the routing algorithm determines how to transmit traffic from sources to destinations. In recent years, Deep Reinforcement Learning (D RL) has been introduced into routing algorithms to address dynamic traffic demands in complex networks. However, most DRL-based routing algorithms are implemented by traditional neural networks, which can only handle fixed-size matrices and operate on a fixed topology. Fortunately, Graph Neural Network (GNN) has been proposed to process graph-structured data and generalize on different graphs. Furthermore, contrastive learning has also been successfully applied to DRL for decision-making in multiple environments. In this paper, we introduce GNN and contrastive learning to DRL-based routing algorithms, and propose a Contrastive Graph Transformer Routing (CGTR) algorithm to improve the robustness of routing against link fail-ures. Aiming at probabilistic packet routing scenarios, we design Edge-enhanced Graph Transformer and Contrastive Grouping Routing in CG TR, enabling it to perceive unseen link failures without retraining. To evaluate the robustness of CG TR, we conduct extensive experiments with different patterns of traffic demands on both generated and real-world network topologies. The experimental results demonstrate that CGTR outperforms all baselines on unseen topologies with link failures, highlighting its stronger robustness.

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.