Capturing professionals’ decision-making in creative workflows (e.g., UI/UX) is essential for reflection, collaboration, and knowledge sharing, yet existing methods often leave rationales incomplete and implicit decisions hidden. To address this, we present the Clear approach, which structures reasoning into cognitive decision steps—linked units of actions, artifacts, and explanations making decisions traceable with generative AI. Building on Clear, we introduce ClearFairy, a think-aloud AI assistant for UI design that detects weak explanations, asks lightweight clarifying questions, and infers missing rationales. In a study with twelve professionals, 85% of ClearFairy’s inferred rationales were accepted (as-is or with revisions). Notably, the system increased “strong explanations”—rationales providing sufficient causal reasoning—from 14% to 83% without adding cognitive demand. Furthermore, exploratory applications demonstrate that captured steps can enhance generative AI agents in Figma, yielding predictions better aligned with professionals and producing coherent outcomes. We release a dataset of 417 decision steps to support future research.

By simply composing prompts, developers can prototype novel generative applications with Large Language Models (LLMs). To refine prototypes into products, however, developers must iteratively revise prompts by evaluating outputs to diagnose weaknesses. Formative interviews (N=8) revealed that developers invest significant effort in manually evaluating outputs as they assess context-specific and subjective criteria. We present EvalLM, an interactive system for iteratively refining prompts by evaluating multiple outputs on user-defined criteria. By describing criteria in natural language, users can employ the system’s LLM-based evaluator to get an overview of where prompts excel or fail, and improve these based on the evaluator’s feedback. A comparative study (N=12) showed that EvalLM, when compared to manual evaluation, helped participants compose more diverse criteria, examine twice as many outputs, and reach satisfactory prompts with 59% fewer revisions. Beyond prompts, our work can be extended to augment model evaluation and alignment in specific application contexts.

Despite the growing demand for professional graphic design knowledge, the tacit nature of design inhibits knowledge sharing. However, there is a limited understanding on the characteristics and instances of tacit knowledge in graphic design. In this work, we build a comprehensive set of tacit knowledge characteristics through a literature review. Through interviews with 10 professional graphic designers, we collected 123 tacit knowledge instances and labeled their characteristics. By qualitatively coding the instances, we identified the prominent elements, actions, and purposes of tacit knowledge. To identify which instances have been addressed the least, we conducted a systematic literature review of prior system support to graphic design. By understanding the reasons for the lack of support on these instances based on their characteristics, we propose design guidelines for capturing and applying tacit knowledge in design tools. This work takes a step towards understanding tacit knowledge, and how this knowledge can be communicated.

Designers rely on visual search to explore and develop ideas in early design stages. However, designers can struggle to identify suitable text queries to initiate a search or to discover images for similarity-based search that can adequately express their intent. We propose GenQuery, a novel system that integrates generative models into the visual search process. GenQuery can automatically elaborate on users’ queries and surface concrete search directions when users only have abstract ideas. To support precise expression of search intents, the system enables users to generatively modify images and use these in similarity-based search. In a comparative user study (N=16), designers felt that they could more accurately express their intents and find more satisfactory outcomes with GenQuery compared to a tool without generative features. Furthermore, the unpredictability of generations allowed participants to uncover more diverse outcomes. By supporting both convergence and divergence, GenQuery led to a more creative experience.

Children acquire an understanding of the world by asking “why” and “how” questions. Conversational agents (CAs) like smart speakers or voice assistants can be promising respondents to children’s questions as they are more readily available than parents or teachers. However, CAs’ answers to “why” and “how” questions are not designed for children, as they can be difficult to understand and provide little interactivity to engage the child. In this work, we propose design guidelines for creating interactive dialogues that promote children’s engagement and help them understand explanations. Applying these guidelines, we propose DAPIE, a system that answers children’s questions through interactive dialogue by employing an AI-based pipeline that automatically transforms existing long-form answers from online sources into such dialogues. A user study (N=16) showed that, with DAPIE, children performed better in an immediate understanding assessment while also reporting higher enjoyment than when explanations were presented sentence-by-sentence.

Research consumption has been traditionally limited to the reading of academic papers—a static, dense, and formally written format. Alternatively, pre-recorded conference presentation videos, which are more dynamic, concise, and colloquial, have recently become more widely available but potentially under-utilized. In this work, we explore the design space and benefits for combining academic papers and talk videos to leverage their complementary nature to provide a rich and fluid research consumption experience. Based on formative and co-design studies, we present Papeos, a novel reading and authoring interface that allow authors to augment their papers by segmenting and localizing talk videos alongside relevant paper passages with automatically generated suggestions. With Papeos, readers can visually skim a paper through clip thumbnails, and fluidly switch between consuming dense text in the paper or visual summaries in the video. In a comparative lab study (n=16), Papeos reduced mental load, scaffolded navigation, and facilitated more comprehensive reading of papers.

Large Language Models (LLMs) have become the backbone of numerous writing interfaces with the goal of supporting end-users across diverse writing tasks. While LLMs reduce the effort of manual writing, end-users may need to experiment and iterate with various generation configurations (e.g., inputs and model parameters) until results meet their goals. However, these interfaces are not designed for experimentation and iteration, and can restrict how end-users track, compare, and combine configurations. In this work, we present “cells, generators, and lenses”, a framework to designing interfaces that support interactive objects that embody configuration components (i.e., input, model, output). Interface designers can apply our framework to produce interfaces that enable end-users to create variations of these objects, combine and recombine them into new configurations, and compare them in parallel to efficiently iterate and experiment with LLMs. To showcase how our framework generalizes to diverse writing tasks, we redesigned three different interfaces—story writing, copywriting, and email composing—and, to demonstrate its effectiveness in supporting end-users, we conducted a comparative study (N=18) where participants used our interactive objects to generate and experiment more. Finally, we investigate the usability of the framework through a workshop with designers (N=3) where we observed that our framework served as both bootstrapping and inspiration in the design process.

End-users can potentially style and customize websites by editing them through in-browser developer tools. Unfortunately, end-users lack the knowledge needed to translate high-level styling goals into low-level code edits. We present Stylette, a browser extension that enables users to change the style of websites by expressing goals in natural language. By interpreting the user’s goal with a large language model and extracting suggestions from our dataset of 1.7 million web components, Stylette generates a palette of CSS properties and values that the user can apply to reach their goal. A comparative study (N=40) showed that Stylette lowered the learning curve, helping participants perform styling changes 35% faster than those using developer tools. By presenting various alternatives for a single goal, the tool helped participants familiarize themselves with CSS through experimentation. Beyond CSS, our work can be expanded to help novices quickly grasp complex software or programming languages.

Online learners are hugely diverse with varying prior knowledge, but most instructional videos online are created to be one-size-fits-all. Thus, learners may struggle to understand the content by only watching the videos. Providing scaffolding prompts can help learners overcome these struggles through questions and hints that relate different concepts in the videos and elicit meaningful learning. However, serving diverse learners would require a spectrum of scaffolding prompts, which incurs high authoring effort. In this work, we introduce Promptiverse, an approach for generating diverse, multi-turn scaffolding prompts at scale, powered by numerous traversal paths over knowledge graphs. To facilitate the construction of the knowledge graphs, we propose a hybrid human-AI annotation tool, Grannotate. In our study (N=24), participants produced 40 times more on-par quality prompts with higher diversity, through Promptiverse and Grannotate, compared to hand-designed prompts. Promptiverse presents a model for creating diverse and adaptive learning experiences online.

Team members commonly collaborate on visual documents remotely and asynchronously. Particularly, students are frequently restricted to this setting as they often do not share work schedules or physical workspaces. As communication in this setting has delays and limits the main modality to text, members exert more effort to reference document objects and understand others’ intentions. We propose Winder, a Figma plugin that addresses these challenges through linked tapes—multimodal comments of clicks and voice. Bidirectional links between the clicked-on objects and voice recordings facilitate understanding tapes: selecting objects retrieves relevant recordings, and playing recordings highlights related objects. By periodically prompting users to produce tapes, Winder preemptively obtains information to satisfy potential communication needs. Through a five-day study with eight teams of three, we evaluated the system’s impact on teams asynchronously designing graphical user interfaces. Our findings revealed that producing linked tapes could be as lightweight as face-to-face (F2F) interactions while transmitting intentions more precisely than text. Furthermore, with preempted tapes, teammates coordinated tasks and invited members to build on each others’ work.

Collaborative Sequencing (CoSeq) is the process by which a group collaboratively constructs a sequence. CoSeq is ubiquitous, occurring across diverse situations like trip planning, course scheduling, or book writing. Building a consensus on a sequence is desirable to groups. However, accomplishing this requires groups to dedicate significant effort to comprehensively discuss preferences and resolve conflicts. Furthermore, as numerous decisions must be assessed to construct a sequence, this challenge can be exacerbated in CoSeq. However, little research has aimed to effectively support consensus building in CoSeq. As a first step to systematically understand and support consensus building in CoSeq, we conducted a formative study to gain insights into how visual awareness may facilitate the holistic recognition of preferences and the resolution of conflicts within a group. From the study, we identified design requirements to support consensus building and designed a novel visual awareness technique for CoSeq. We instantiated this design in a collaborative travel itinerary planning system, Twine, and conducted a summative study to evaluate its effects. We found that visual awareness could decrease the effort of communicating preferences by 21%, and participants' comments suggest that it also encouraged group members to behave more cooperatively when building a consensus. We discuss future research directions to further explore the needs and challenges in this unique context and to advance the development of support for CoSeq tasks.

Although Collaborative Information-Seeking (CIS) is becoming prevalent as people engage in shared decision-making, interface components adopted in the most commonly used information seeking tools (e.g., search, filter, select, and sort) are designed for individual use. To deepen our understanding of (1) how such single-user designs affect people's consensus building processes in CIS and (2) how to devise an alternative design to improve current practices, we conducted two 4-week diary studies and observed how groups seek out places together. Our studies focus on social event coordination as a case where CIS is necessary and important. In Study 1, we examined the major challenges people encounter when performing CIS using their preferred tools. These challenges include difficulties in capturing mutual preferences, high communication cost, and disparity of work depending on a group member's perceived role as an organizer or invitee. We discovered that improving a group's shared understanding of the target information they seek (e.g., places, products) could potentially address the challenges. In Study 2, we designed, deployed, and evaluated ComeTogether, a novel system that supports a group's social event coordination. ComeTogether adopts Collaborative Dynamic Queries (C-DQ), an interface designed to allow a group to share their preferences regarding potential destinations. Study 2 results indicate that using C-DQ increased users' awareness of other group members' preferences in performing CIS, making their coordination more transparent, more inviting, and fairer than what their current practice allows. Meanwhile, ComeTogether improved communication efficiency of groups while presenting opportunities to learn about others and to discover new places. We provide implications for design that explain considerations for adopting C-DQ and identify future research directions.