From purchasing a gift to deciding on a hobby, unfamiliar decisions—decisions without domain knowledge and experience—are frequent and significant. The complexity and uncertainty of such decisions demand unique approaches to information seeking, understanding, and decision-making. Our formative study highlights that in the current workflow, users want to start by discovering broad and relevant domain information evenly and simultaneously, quickly address emerging inquiries, and gain personalized standards to assess information found. We present ChoiceMates, an interactive multi-agent system designed to address these needs by enabling users to engage with a dynamic set of LLM agents each presenting a unique experience in the domain. Unlike existing multi-agent systems that automate tasks with agents, the user orchestrates agents to assist their decision-making process in each turn, through chatting with all agents, with a tagged subset of agents, or calling in new agents into the space. By comparing ChoiceMates with a web search condition and a multi-agent framework (n=12), we show that ChoiceMates enables a more confident, satisfactory decision-making with better situation understanding than web search, and higher decision quality than a commercial multi-agent framework. We further illustrate how participants utilized ChoiceMates to make unfamiliar decisions, providing insights into designing a more controllable and collaborative multi-agent system.

Peer Reviewed

Research promotion enables researchers to share advanced knowledge with pertinent academic communities. The question-and-answer (QA) style articles are effective for researchers to promote their research by enabling readers to understand research on complex subjects. Recent advances in large language models (LLMs) have opened avenues for supporting researchers in creating QA-style articles for research promotion. However, without the authors’ involvement, these models may only partially capture the researcher’s intention and voice. We developed AQUA, a research probe that enables researchers to co-create QA-style articles with LLMs to promote their research papers. A user study (n=12) reveals that LLMs reduced authors’ burden and helped them understand the readers’ perspectives. Nevertheless, LLMs failed to capture the unique intent of the authors, and their automated generation discouraged authors from carefully revising their answers. Based on our findings, we discuss human-LLM interaction design to enable authors to create QA-style articles that reflect their intention.

Graphic designers often get inspiration through the recombination of references. Our formative study (N=6) reveals that graphic designers focus on conceptual keywords during this process, and want support for discovering the keywords, expanding them, and exploring diverse recombination options of them, while still having room for designers’ creativity. We propose CreativeConnect, a system with generative AI pipelines that helps users discover useful elements from the reference image using keywords, recommends relevant keywords, generates diverse recombination options with user-selected keywords, and shows recombinations as sketches with text descriptions. Our user study (N=16) showed that CreativeConnect helped users discover keywords from the reference and generate multiple ideas based on them, ultimately helping users produce more design ideas with higher self-reported creativity, compared to the baseline system without generative pipelines. While CreativeConnect was shown effective in ideation, we discussed how CreativeConnect can be extended to support other types of tasks in creativity support.

Label set construction—deciding on a group of distinct labels—is an essential stage in building a supervised machine learning (ML) application, as a badly designed label set negatively affects subsequent stages, such as training dataset construction, model training, and model deployment. Despite its significance, it is challenging for ML practitioners to come up with a well-defined label set, especially when no external references are available. Through our formative study (n=8), we observed that even with the help of external references or domain experts, ML practitioners still need to go through multiple iterations to gradually improve the label set. In this process, there exist challenges in collecting helpful feedback and utilizing it to make optimal refinement decisions. To support informed refinement, we present DynamicLabels, a system that aims to support a more informed label set-building process with crowd feedback. Crowd workers provide annotations and label suggestions to the ML practitioner’s label set, and the ML practitioner can review the feedback through multi-aspect analysis and refine the label set with crowd-made labels. Through a within-subjects study (n=16) using two datasets, we found that DynamicLabels enables better understanding and exploration of the collected feedback and supports a more structured and flexible refinement process. The crowd feedback helped ML practitioners explore diverse perspectives, spot current weaknesses, and shop from crowd-generated labels. Metrics and label suggestions in DynamicLabels helped in obtaining a high-level overview of the feedback, gaining assurance, and spotting surfacing conflicts and edge cases that could have been overlooked.

Educational live streaming has become a complement to in-person teaching. While synchronous instructor-learner communication is useful, the technology-mediated nature of live streaming can obscure many interaction cues (e.g., learners' facial expressions and body language), which dampens the instructors' ability to respond to remote learners' needs. We explore the opportunity of leveraging real-time transcripts generated from instructors' audio as a basis for re-creating interaction cues. Transcripts can be leveraged to reveal the content of live streams in a form that learners can trace back and annotate, and such annotations can be further aggregated and presented to instructors as signals to assist them in tracking learners' engagement. By designing and evaluating our proof-of-concept prototype system, EduLive, we show that instructors benefited from the summative information extracted from learners' annotations, and the context provided by the transcript enhanced their ability to answer learners' questions. Our system contributes to the design space of social annotations in CSCW by employing social annotations in educational live streaming scenarios.

Similar to a design process for designing graphical user interfaces, conversation designers often apply an iterative design process by defining a conversation flow, testing with users, reviewing user data, and improving the design. While it is possible to iterate on conversation design with existing chatbot prototyping tools, there still remain challenges in recruiting participants on-demand and collecting structured feedback on specific conversational components. These limitations hinder designers from running rapid iterations and making informed design decisions. We posit that involving a crowd in the conversation design process can address these challenges, and introduce ProtoChat, a crowd-powered chatbot design tool built to support the iterative process of conversation design. ProtoChat makes it easy to recruit crowd workers to test the current conversation within the design tool. ProtoChat's crowd-testing tool allows crowd workers to provide concrete and practical feedback and suggest improvements on specific parts of the conversation. With the data collected from crowd-testing, ProtoChat provides multiple types of visualizations to help designers analyze and revise their design. Through a three-day study with eight designers, we found that ProtoChat enabled an iterative design process for designing a chatbot. Designers improved their design by not only modifying the conversation design itself, but also adjusting the persona and getting UI design implications beyond the conversation design itself. The crowd responses were helpful for designers to explore user needs, contexts, and diverse response formats. With ProtoChat, designers can successfully collect concrete evidence from the crowd and make decisions to iteratively improve their conversation design.