Scheduling is a perennial-and often challenging-problem for many groups. Existing tools are mostly static, showing an identical set of choices to everyone, regardless of the current status of attendees' inputs and preferences. In this paper, we propose Togedule, an adaptive scheduling tool that uses large language models to dynamically adjust the pool of choices and their presentation format. With the initial prototype, we conducted a formative study (N=10) and identified the potential benefits and risks of such an adaptive scheduling tool. Then, after enhancing the system, we conducted two controlled experiments, one each for attendees and organizers (total N=66). For each experiment, we compared scheduling with verbal messages, shared calendars, or Togedule. Results show that Togedule significantly reduces the cognitive load of attendees indicating their availability and improves the speed and quality of the decisions made by organizers.

Understanding the dynamics of human-AI interaction in question answering is crucial for enhancing collaborative efficiency. Extending from our initial formative study, which revealed challenges in human utilization of conversational AI support, we designed two configurations for prompt guidance: a Nudging approach, where the AI suggests potential responses for human agents, and a Highlight strategy, emphasizing crucial parts of reference documents to aid human responses. Through two controlled experiments, the first involving 31 participants and the second involving 106 participants, we compared these configurations against traditional human-only approaches, both with and without AI assistance. Our findings suggest that effective human-AI collaboration can enhance response quality, though merely combining human and AI efforts does not ensure improved outcomes. In particular, the Nudging configuration was shown to help improve the quality of the output when compared to AI alone. This paper delves into the development of these prompt guidance paradigms, offering insights for refining human-AI collaborations in conversational question-answering contexts and contributing to a broader understanding of human perceptions and expectations in AI partnerships.

Virtual academic networking is socio-technically challenging, however, fruitful for researchers' success. We introduce a system called Who2chat to tackle the challenge and facilitate connections of researchers in virtual social hours. Who2chat allows academic researchers to create a research profile and express their research interests, find researchers with similar interests, overcome social barriers, and coordinate and start video chats, all within a single interface. We engaged in an iterative design process by deploying Who2chat at academic conferences. In our preliminary deployment (N=80), we found that researchers often have difficulty finding other researchers who share similar interests, and they are shy about reaching out to other researchers. Inspired by this, we implemented social-signaling features to Who2chat and ran our first deployment (N=220). Our results highlight that the interface allowed users to find relevant researchers and helped them feel confident in joining conversations. However, this led to large group conversations where discussion topics were more superficial. In response, we developed and deployed our second interface (N=81). Key improvements were managing the size of conversations, dynamically determining and allowing individuals to join a conversation based on their relevance to the ongoing discussion, and maintaining the ratio of senior and junior members, to further enhance the quality of discussions. As a result, participants were able to meet more people and engage in more meaningful conversations. Our work demonstrates an interface design for social networking in academic settings and how to lower social barriers in virtual networking.

Even though today’s videoconferencing systems are often very useful, these systems do not provide support for one of the most important aspects of in-person meetings: the ad hoc, private conversations that happen before, after, and during the breaks of scheduled events–the proverbial hallway conversations. Here we describe our design of a simple system, called Minglr, which supports this kind of interaction by facilitating the matching of conversational partners. We describe two studies of this system’s use at two virtual conferences with over 450 total participants. Our results provide evidence for the usefulness of this capability, showing that, for example, 81% of people who used the system successfully thought that future virtual conferences should include a tool with similar functionality. We believe that similar functionality is likely to be widely implemented in many videoconferencing systems and to increase the feasibility and desirability of many kinds of remote work and socializing.

Online chat is an emerging channel for discussing community problems. It is common practice for communities to assign dedicated moderators to maintain a structured discussion and enhance the problem-solving experience. However, due to the synchronous nature of online chat, moderators face a high managerial overhead in tasks like discussion stage management, opinion summarization, and consensus-building support. To assist moderators with facilitating a structured discussion for community problem-solving, we introduce SolutionChat, a system that (1) visualizes discussion stages and featured opinions and (2) recommends contextually appropriate moderator messages. Results from a controlled lab study (n=55, 12 groups) suggest that participants' perceived discussion trackability was significantly higher with SolutionChat than without. Also, moderators provided better summarization with less effort and better managerial support using system-generated messages with SolutionChat than without. With SolutionChat, we envision untrained moderators to effectively facilitate chat-based discussions of important community matters.

Augmentative and alternative communication (AAC) technologies are widely used to help non-verbal children enable communication. For AAC-aided communication to be successful, caregivers should support children with consistent intervention strategies in various settings. As such, caregivers need to continuously observe and discuss children's AAC usage to create a shared understanding of these strategies. However, caregivers often find it challenging to effectively collaborate with one another due to a lack of family involvement and the unstructured process of collaboration. To address these issues, we present TalkingBoogie, which consists of two mobile apps: TalkingBoogie-AAC for caregiver-child communication, and TalkingBoogie-coach supporting caregiver collaboration. Working together, these applications provide contextualized layouts for symbol arrangement, scaffold the process of sharing and discussing observations, and induce caregivers' balanced participation. A two-week deployment study with four groups (N=11) found that TalkingBoogie helped increase mutual understanding of strategies and encourage balanced participation between caregivers with reduced cognitive loads.