arXiv subjects

Sangho Suh

Publications and source records attributed to Sangho Suh.

15 recordsLinked to original sources

arXiv2026

Making Abstraction Concrete: A Design Space and Interaction Model of Abstraction in Interactive Systems

The principle of abstraction guides the design of interactive systems, yet we lack a conceptual framework to understand how it shapes interaction design. Existing models, such as the gulfs of execution and evaluation, do not explicitly model abstractions in the system or in users' mental models, and therefore lack actionable guidance for designing abstractions. To investigate how abstractions are employed in interactive systems, we surveyed 457 papers and synthesized a design space of abstraction techniques along six dimensions. We use this design space to reframe the gulfs through a lens of abstraction, explicitly articulate the cognitive and design processes by which users and systems bridge and navigate the abstraction gap, and demonstrate how this model integrates existing perspectives and surfaces new opportunities for future systems.

cs.HC↗

arXiv2026

Understanding Usage and Engagement in AI-Powered Scientific Research Tools: The Asta Interaction Dataset

AI-powered scientific research tools are rapidly being integrated into research workflows, yet the field lacks a clear lens into how researchers use these systems in real-world settings. We present and analyze the Asta Interaction Dataset, a large-scale resource comprising over 200,000 user queries and interaction logs from two deployed tools (a literature discovery interface and a scientific question-answering interface) within an LLM-powered retrieval-augmented generation platform. Using this dataset, we characterize query patterns, engagement behaviors, and how usage evolves with experience. We find that users submit longer and more complex queries than in traditional search, and treat the system as a collaborative research partner, delegating tasks such as drafting content and identifying research gaps. Users treat generated responses as persistent artifacts, revisiting and navigating among outputs and cited evidence in non-linear ways. With experience, users issue more targeted queries and engage more deeply with supporting citations, although keyword-style queries persist even among experienced users. We release the anonymized dataset and analysis with a new query intent taxonomy to inform future designs of real-world AI research assistants and to support realistic evaluation.

cs.HC↗

arXiv2026

Narrative Scaffolding: A Narrative-First Framework for Data-Driven Sensemaking

When exploring data, analysts construct narratives about what the data means by asking questions, generating visualizations, reflecting on patterns, and revising their interpretations as new insights emerge. Yet existing analysis tools treat narrative as an afterthought, breaking the link between reasoning, reflection, and the evolving story from exploration. Consequently, analysts lose the ability to see how their reasoning evolves, making it harder to reflect systematically or build coherent explanations. To address this gap, we propose Narrative Scaffolding, a framework for narrative-driven exploration that positions narrative construction as the primary interface for exploration and reasoning. We implement this framework in a system that externalizes iterative reasoning through narrative-first entry, semantically aligned view generation, and reflection support via insight provenance and inquiry tracking. In a within-subject study N=20, we demonstrate that narrative scaffolding facilitates broader exploration, deeper reflection, and more defensible narratives. An evaluation with visualization literacy experts (N = 6) confirmed that the system produced outputs aligned with narrative intent and facilitated intentional exploration.

cs.HC↗

arXiv2025

KnowledgeTrail: Generative Timeline for Exploration and Sensemaking of Historical Events and Knowledge Formation

The landscape of interactive systems is shifting toward dynamic, generative experiences that empower users to explore and construct knowledge in real time. Yet, timelines -- a fundamental tool for representing historical and conceptual development -- remain largely static, limiting user agency and curiosity. We introduce the concept of a generative timeline: an AI-powered timeline that adapts to users' evolving questions by expanding or contracting in response to input. We instantiate this concept through KnowledgeTrail, a system that enables users to co-construct timelines of historical events and knowledge formation processes. Two user studies showed that KnowledgeTrail fosters curiosity-driven exploration, serendipitous discovery, and the ability to trace complex relationships between ideas and events, while citation features supported verification yet revealed fragile trust shaped by perceptions of source credibility. We contribute a vision for generative timelines as a new class of exploratory interface, along with design insights for balancing serendipity and credibility.

cs.HC↗

arXiv2025

Perspectra: Choosing Your Experts Enhances Critical Thinking in Multi-Agent Research Ideation

Recent advances in multi-agent systems (MAS) enable tools for information search and ideation by assigning personas to agents. However, how users can effectively control, steer, and critically evaluate collaboration among multiple domain-expert agents remains underexplored. We present Perspectra, an interactive MAS that visualizes and structures deliberation among LLM agents via a forum-style interface, supporting @-mention to invite targeted agents, threading for parallel exploration, with a real-time mind map for visualizing arguments and rationales. In a within-subjects study with 18 participants, we compared Perspectra to a group-chat baseline as they developed research proposals. Our findings show that Perspectra significantly increased the frequency and depth of critical-thinking behaviors, elicited more interdisciplinary replies, and led to more frequent proposal revisions than the group chat condition. We discuss implications for designing multi-agent tools that scaffold critical thinking by supporting user control over multi-agent adversarial discourse.

cs.HC↗

arXiv2025

StoryEnsemble: Enabling Dynamic Exploration & Iteration in the Design Process with AI and Forward-Backward Propagation

Design processes involve exploration, iteration, and movement across interconnected stages such as persona creation, problem framing, solution ideation, and prototyping. However, time and resource constraints often hinder designers from exploring broadly, collecting feedback, and revisiting earlier assumptions-making it difficult to uphold core design principles in practice. To better understand these challenges, we conducted a formative study with 15 participants-comprised of UX practitioners, students, and instructors. Based on the findings, we developed StoryEnsemble, a tool that integrates AI into a node-link interface and leverages forward and backward propagation to support dynamic exploration and iteration across the design process. A user study with 10 participants showed that StoryEnsemble enables rapid, multi-directional iteration and flexible navigation across design stages. This work advances our understanding of how AI can foster more iterative design practices by introducing novel interactions that make exploration and iteration more fluid, accessible, and engaging.

cs.HC↗

arXiv2025

MaRginalia: Enabling In-person Lecture Capturing and Note-taking Through Mixed Reality

Students often take digital notes during live lectures, but current methods can be slow when capturing information from lecture slides or the instructor's speech, and require them to focus on their devices, leading to distractions and missing important details. This paper explores supporting live lecture note-taking with mixed reality (MR) to quickly capture lecture information and take notes while staying engaged with the lecture. A survey and interviews with university students revealed common note-taking behaviors and challenges to inform the design. We present MaRginalia to provide digital note-taking with a stylus tablet and MR headset. Students can take notes with an MR representation of the tablet, lecture slides, and audio transcript without looking down at their device. When preferred, students can also perform detailed interactions by looking at the physical tablet. We demonstrate the feasibility and usefulness of MaRginalia and MR-based note-taking in a user study with 12 students.

cs.HC↗

arXiv2024

Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning

Novice programmers are increasingly relying on Large Language Models (LLMs) to generate code for learning programming concepts. However, this interaction can lead to superficial engagement, giving learners an illusion of learning and hindering skill development. To address this issue, we conducted a systematic design exploration to develop seven cognitive engagement techniques aimed at promoting deeper engagement with AI-generated code. In this paper, we describe our design process, the initial seven techniques and results from a between-subjects study (N=82). We then iteratively refined the top techniques and further evaluated them through a within-subjects study (N=42). We evaluate the friction each technique introduces, their effectiveness in helping learners apply concepts to isomorphic tasks without AI assistance, and their success in aligning learners' perceived and actual coding abilities. Ultimately, our results highlight the most effective technique: guiding learners through the step-by-step problem-solving process, where they engage in an interactive dialog with the AI, prompting what needs to be done at each stage before the corresponding code is revealed.

cs.HC↗

arXiv2024

Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creation

Thanks to their generative capabilities, large language models (LLMs) have become an invaluable tool for creative processes. These models have the capacity to produce hundreds and thousands of visual and textual outputs, offering abundant inspiration for creative endeavors. But are we harnessing their full potential? We argue that current interaction paradigms fall short, guiding users towards rapid convergence on a limited set of ideas, rather than empowering them to explore the vast latent design space in generative models. To address this limitation, we propose a framework that facilitates the structured generation of design space in which users can seamlessly explore, evaluate, and synthesize a multitude of responses. We demonstrate the feasibility and usefulness of this framework through the design and development of an interactive system, Luminate, and a user study with 14 professional writers. Our work advances how we interact with LLMs for creative tasks, introducing a way to harness the creative potential of LLMs.

cs.HC↗

arXiv2023

CoLadder: Supporting Programmers with Hierarchical Code Generation in Multi-Level Abstraction

Programmers increasingly rely on Large Language Models (LLMs) for code generation. However, misalignment between programmers' goals and generated code complicates the code evaluation process and demands frequent switching between prompt authoring and code evaluation. Yet, current LLM-driven code assistants lack sufficient scaffolding to help programmers format intentions from their overarching goals, a crucial step before translating these intentions into natural language prompts. To address this gap, we adopted an iterative design process to gain insights into programmers' strategies when using LLMs for programming. Building on our findings, we created CoLadder, a system that supports programmers by facilitating hierarchical task decomposition, direct code segment manipulation, and result evaluation during prompt authoring. A user study with 12 experienced programmers showed that CoLadder is effective in helping programmers externalize their problem-solving intentions flexibly, improving their ability to evaluate and modify code across various abstraction levels, from goal to final code implementation.

cs.SE↗

arXiv2023

Sensecape: Enabling Multilevel Exploration and Sensemaking with Large Language Models

People are increasingly turning to large language models (LLMs) for complex information tasks like academic research or planning a move to another city. However, while they often require working in a nonlinear manner -- e.g., to arrange information spatially to organize and make sense of it, current interfaces for interacting with LLMs are generally linear to support conversational interaction. To address this limitation and explore how we can support LLM-powered exploration and sensemaking, we developed Sensecape, an interactive system designed to support complex information tasks with an LLM by enabling users to (1) manage the complexity of information through multilevel abstraction and (2) seamlessly switch between foraging and sensemaking. Our within-subject user study reveals that Sensecape empowers users to explore more topics and structure their knowledge hierarchically, thanks to the externalization of levels of abstraction. We contribute implications for LLM-based workflows and interfaces for information tasks.

cs.HC↗

arXiv2023

Cheat Sheet for Teaching Programming with Comics: Through the Lens of Concept-Language-Procedure Framework

Comics is emerging as a popular medium for providing visual explanations of programming concepts and procedures. Recent research into this medium opened the door to new opportunities and tools to advance teaching and learning in computing. For instance, recent research on coding strip, a form of comic strip with its corresponding code, led to a new visual programming environment that generates comics from code and experience report detailing various ways coding strips can be used to benefit students' learning. However, how comics can be designed and used to teach programming has not yet been documented in a concise, accessible format to ease their adoption. To fill this gap, we developed a cheat sheet that summarizes the pedagogical techniques and designs teachers can use in their teaching. To develop this cheat sheet, we analyzed prior work on coding strip, including 26 coding strips and 30 coding strip design patterns. We also formulated a concept-language-procedure framework to delineate how comics can facilitate teaching in programming. To evaluate our cheat sheet, we presented it to 11 high school CS teachers at an annual conference for computer studies educators and asked them to rate its readability, usefulness, organization, and their interest in using it for their teaching. Our analysis suggests that this cheat sheet is easy to read/understand, useful, well-structured, and interests teachers to further explore how they can incorporate comics into their teaching.

cs.HC↗

arXiv2022

CodeToon: Story Ideation, Auto Comic Generation, and Structure Mapping for Code-Driven Storytelling

Recent work demonstrated how we can design and use coding strips, a form of comic strips with corresponding code, to enhance teaching and learning in programming. However, creating coding strips is a creative, time-consuming process. Creators have to generate stories from code (code->story) and design comics from stories (story->comic). We contribute CodeToon, a comic authoring tool that facilitates this code-driven storytelling process with two mechanisms: (1) story ideation from code using metaphor and (2) automatic comic generation from the story. We conducted a two-part user study that evaluates the tool and the comics generated by participants to test whether CodeToon facilitates the authoring process and helps generate quality comics. Our results show that CodeToon helps users create accurate, informative, and useful coding strips in a significantly shorter time. Overall, this work contributes methods and design guidelines for code-driven storytelling and opens up opportunities for using art to support computer science education.

cs.HC↗

arXiv2021

Exploring Individual and Collaborative Storytelling in an Introductory Creative Coding Class

Teaching programming through storytelling is a popular pedagogical approach and an active area of research. However, most previous work in this area focused on K-12 students using block-based programming. Little, if any, work has examined the approach with university students using text-based programming. This experience report fills this gap. Specifically, we report our experience administering three storytelling assignments -- two individual and one collaborative -- in an introductory computer science class with 49 undergraduate students using $\textit{p5.js}$, a text-based programming library for creative coding. Our work contributes an understanding of students' experiences with the three authoring processes and a set of recommendations to improve the administration of and experience with individual and collaborative storytelling with text-based programming.

cs.CY↗

arXiv2021

Using Comics to Introduce and Reinforce Programming Concepts in CS1

Recent work investigated the potential of comics to support the teaching and learning of programming concepts and suggested several ways $coding$ $strips$, a form of comic strip with its corresponding code, can be used. Building on this work, we tested the recommended use cases of $coding$ $strip$ in an undergraduate introductory computer science course at a large comprehensive university. At the end of the course, we surveyed students to assess their experience and found they benefited in various ways. Our work contributes a demonstration of the various ways comics can be used in introductory CS courses and an initial understanding of benefits and challenges with using comics in computing education gleaned from an analysis of students' survey responses and code submissions.

cs.CY↗