Conference & Journal Papers

2026 Publications

ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactions

Jeongeon Park, Bryan Min, Kihoon Son, Jean Y. Song, Xiaojuan Ma, Juho Kim

IUI 2026 πŸ“„ arxiv human-ai-interaction

πŸ’‘ This paper shows that letting users directly orchestrate multiple AI agentsβ€”each representing different domain perspectivesβ€”helps people make more confident decisions in unfamiliar areas like choosing PhD programs or buying cameras.

CANVAS: A Benchmark for Vision-Language Models on Tool-Based User Interface Design

Daeheon Jeong, Seoyeon Byun, Kihoon Son, Dae Hyun Kim, Juho Kim

AAAI 2026 🌐 website πŸ“„ arxiv human-ai-interaction

πŸ’‘ This paper introduces a benchmark for testing whether AI vision-language models can effectively use design software tools to create and edit user interfaces, revealing that current models show promising but inconsistent abilities in this collaborative design task.

2025 Publications

Iffy-Or-Not: Critically Evaluating Potential Misinformation With Fallacy Detection and Socratic Questioning Using LLMs

Gionnieve Lim, Juho Kim, Simon Perrault

TOCHI πŸ“š acm dl πŸ“„ arxiv human-ai-interaction social-computing

πŸ’‘ This paper shows that a browser extension using fallacy detection and Socratic questioning can encourage readers to think more critically about online content, though some users resist tools that challenge their existing thinking habits.

Mind the Blind Spots: A Focus-Level Evaluation Framework for LLM Reviews

Hyungyu Shin, Jingyu Tang, Yoonjoo Lee, Nayoung Kim, Hyunseung Lim, Ji Yong Cho, Hwajung Hong, Moontae Lee, Juho Kim

EMNLP 2025 Main Track πŸ† SAC Highlights πŸ“„ arxiv human-ai-interaction

πŸ’‘ This paper reveals that AI systems generating peer reviews consistently overlook novelty assessment while overemphasizing technical validity, highlighting critical blind spots before these tools are trusted for scientific gatekeeping.

PANORAMA: A Dataset and Benchmark Tasks Capturing the Evaluation Trails and Rationales in Patent Examination

Hyunseung Lim, Sooyohn Nam, Sungmin Na, Ji Yong Cho, June Yong Yang, Hyungyu Shin, Yoonjoo Lee, Juho Kim, Moontae Lee, Hwajung Hong

NeurIPS 2025 Datasets & Benchmarks πŸ“„ arxiv human-ai-interaction

πŸ’‘ This paper reveals that while large language models can find relevant prior patents, they struggle to judge whether new patent claims are truly novelβ€”a gap that matters for automating the costly patent examination process.

Why Social Media Users Press "Not Interested": Motivations, Anticipated Effects, and Result Interpretation

Jihyeong Hong, Eunyoung Ko, Juho Kim, Jeong-woo Jang

CSCW 2025 πŸ† Honorable Mention Award πŸ“š acm dl social-computing human-ai-interaction

πŸ’‘ This paper reveals that social media users rely on "Not Interested" buttons to filter out unwanted content but remain confused about whether their feedback actually shapes what they see, highlighting a need for clearer algorithm transparency.

Can Fans Build Parasocial Relationships through Idols' Simulated Voice Messages?: A Study of AI Private Call Users' Perceptions, Cognitions, and Behaviors.

Eun Jeong Kang, Haesoo Kim, Hyunwoo Kim, Susan Fussell, Juho Kim

CSCW 2025 πŸ“š acm dl human-ai-interaction

πŸ’‘ This paper shows that fans use AI-generated voice messages from K-Pop idols as a substitute for direct interaction, actively working to perceive these synthetic messages as authentic to strengthen their emotional connection with celebrities.

CUPID: Evaluating Personalized and Contextualized Alignment of LLMs from Interactions

Tae Soo Kim, Yoonjoo Lee, Yoonah Park, Jiho Kim, Young-Ho Kim, Juho Kim

COLM 2025 human-ai-interaction

πŸ’‘ This paper shows that even advanced AI chatbots struggle to remember and correctly apply user preferences across different conversation contexts, helping developers understand why personalized AI assistants often feel frustratingly inconsistent.

BloomIntent: Automating Search Evaluation with LLM-Generated Fine-Grained User Intents

Yoonseo Choi, Eunhye Kim, Hyunwoo Kim, Donghyun Park, Honggu Lee, Jin Young Kim, Juho Kim

UIST 2025 πŸ“„ arxiv datamining human-ai-interaction

πŸ’‘ This paper shows that evaluating search engines by generating diverse plausible user intentsβ€”rather than treating identical queries as having one goalβ€”helps developers identify which user needs current systems fail to serve.

Less Talk, More Trust: Understanding Players' In-game Assessment of Communication Processes in League of Legends

Juhoon Lee, Seoyoung Kim, Yeon Su Park, Juho Kim, Jeong-woo Jang, Joseph Seering

CHI 2025 πŸ“„ arxiv human-ai-interaction

πŸ’‘ This paper shows that players in online team games often view any teammate communication as a warning sign of conflict rather than collaboration, helping game designers understand why communication tools frequently backfire despite good intentions.

Proxona: Supporting Creators' Sensemaking and Ideation with LLM-Powered Audience Personas

Yoonseo Choi, Eun Jeong Kang, Seulgi Choi, Min Kyung Lee, Juho Kim

CHI 2025 πŸ“„ arxiv human-ai-interaction

πŸ’‘ This paper shows that transforming audience comments into interactive AI personas helps content creators discover overlooked viewer segments and get actionable feedback on early drafts before publishing.

TeachTune: Reviewing Pedagogical Agents Against Diverse Student Profiles with Simulated Students

Hyoungwook Jin, Minju Yoo, Jeongeon Park, Yokyung Lee, Xu Wang, Juho Kim

CHI 2025 πŸ“„ arxiv human-ai-interaction learning

πŸ’‘ This paper shows that simulating diverse student personas to automatically test AI tutoring chatbots helps teachers more efficiently evaluate whether their tools adapt appropriately to students with different knowledge levels and motivations.

PlanTogether: Facilitating AI Application Planning Using Information Graphs and Large Language Models

Dae Hyun Kim, Daeheon Jeong, Shahnoza Yadgarova, Hyungyu Shin, Jinho Son, Hari Subramonyam, Juho Kim

CHI 2025 human-ai-interaction

πŸ’‘ This paper shows that structuring AI project planning around information dependencies helps non-technical clients articulate clearer, more actionable requirements before consulting AI experts, reducing miscommunication in collaborative development.

VideoMix: Aggregating How-To Videos for Task-Oriented Learning

Saelyne Yang, Anh Truong, Juho Kim, Dingzeyu Li

IUI 2025 πŸ“„ arxiv human-ai-interaction

πŸ’‘ This paper shows that aggregating information from multiple how-to videos into organized summaries helps learners understand practical tasks more efficiently than watching videos separately.

2024 Publications

ARXIVDIGESTABLES: Synthesizing Scientific Literature into Tables using Language Models

Benjamin Newman, Yoonjoo Lee, Aakanksha Naik, Pao Siangliulue, Raymond Fok, Juho Kim, Daniel S. Weld, Joseph Chee Chang, Kyle Lo

EMNLP 2024 Main Track πŸ“„ arxiv human-ai-interaction datamining

πŸ’‘ This paper shows that language models can automatically generate literature review tables comparing research papers, helping scientists synthesize large bodies of work more efficiently during systematic reviews.

LearnerVoice: A Dataset of Non-Native English Learners' Spontaneous Speech

Haechan Kim, Junho Myung, Seoyoung Kim, Sungpah Lee, Dongyeop Kang, Juho Kim

Interspeech 2024 πŸ† Shortlisted for ISCA Best Student Paper Award learning

πŸ’‘ This paper shows that speech recognition systems struggle with non-native learners' grammatical errors and hesitations, and provides a new dataset that cuts these recognition errors nearly in half.

A Context-Aware Onboarding Agent for Metaverse Powered by Large Language Models

Jihyeong Hong, Yokyung Lee, Dae Hyun Kim, DaEun Choi, Yeo-Jin Yoon, Gyu-cheol Lee, Zucheul Lee, Juho Kim

DIS 2024 human-ai-interaction

πŸ’‘ This paper shows that answering newcomers' questions in virtual worlds requires tracking both their immediate surroundings and exploration history, helping designers create onboarding assistants that improve learning and immersion.

Co-Creating Question-and-Answer Style Articles with Large Language Models for Research Promotion

Hyunseung Lim, Ji Yong Cho, Taewan Kim, Jeongeon Park, Hyungyu Shin, Seulgi Choi, Sunghyun Park, Kyungjae Lee, Juho Kim, Moontae Lee, Hwajung Hong

DIS 2024 human-ai-interaction

πŸ’‘ This paper shows that while AI writing assistants help researchers create accessible Q&A articles about their work, they struggle to capture authors' unique intentions and may discourage careful revision, revealing key tensions in human-AI collaborative writing.

One vs. Many: Comprehending Accurate Information from Multiple Erroneous and Inconsistent AI Generations

Yoonjoo Lee, Kihoon Son, Tae Soo Kim, Jisu Kim, John Joonyoung Chung, Eytan Adar, Juho Kim

FAccT 2024 πŸ“„ arxiv human-ai-interaction learning

πŸ’‘ This paper shows that showing users multiple inconsistent AI-generated answers actually improves their comprehension of information, suggesting that revealing AI disagreements could help people use these tools more critically.

EduLive: Re-Creating Cues for Instructor-Learner Interaction in Educational Live Streams with Learners' Transcript-Based Annotations

Jingchao Fang, Jeongeon Park, Juho Kim, Hao-Chuan Wang

CSCW 2024 πŸ“š acm dl learning social

πŸ’‘ This paper shows that letting online learners annotate real-time lecture transcripts creates useful feedback signals for instructors, helping bridge the interaction gap caused by missing visual cues in educational live streams.

CodeTree: A System for Learnersourcing Subgoal Hierarchies in Code Examples

Hyoungwook Jin, Juho Kim

CSCW 2024 learning social-computing

πŸ’‘ This paper shows that novice programmers can collectively create high-quality hierarchical explanations of code examples, reducing dependence on experts while improving their own learning outcomes.

Exploring Cross-Cultural Differences in English Hate Speech Annotations: From Dataset Construction to Analysis

Nayeon Lee, Chani Jung, Junho Myung, Jiho Jin, Jose Camacho-Collados, Juho Kim, Alice Oh

NAACL 2024 πŸ† Resource Award social-computing

πŸ’‘ This paper reveals that hate speech labels vary significantly across English-speaking countriesβ€”with only 56% agreementβ€”showing that cultural context shapes what people consider hateful online.

VIVID: Human-AI Collaborative Authoring of Vicarious Dialogues from Lecture Videos

Seulgi Choi, Hyewon Lee, Yoonjoo Lee, Juho Kim

CHI 2024 πŸ“„ arxiv 🌐 website human-ai-interaction learning video creativity

πŸ’‘ This paper shows that instructors can use AI tools to efficiently convert monologue-style online lectures into interactive dialogues, helping educators create more engaging learning experiences without the usual time-intensive effort.

PaperWeaver: Enriching Topical Paper Alerts by Contextualizing Recommended Papers with User-collected Papers

Yoonjoo Lee, Hyeonsu B Kang, Matt Latzke, Juho Kim, Jonathan Bragg, Joseph Chee Chang, Pao Siangliulue

CHI 2024 human-ai-interaction

πŸ’‘ This paper shows that enriching paper alerts with AI-generated explanations of how recommended papers connect to a researcher's existing work helps them evaluate relevance faster and more confidently.

CreativeConnect: Supporting Reference Recombination for Graphic Design Ideation with Generative AI

DaEun Choi, Sumin Hong, Jeongeon Park, John Joon Young Chung, Juho Kim

CHI 2024 πŸ“„ arxiv 🌐 website human-ai-interaction creativity

πŸ’‘ This paper shows that AI-generated keyword suggestions and sketch recombinations help graphic designers discover more creative ideas from reference images, offering a practical way to support early-stage design brainstorming.

GenQuery: Supporting Expressive Visual Search with Generative Models

Kihoon Son, DaEun Choi, Tae Soo Kim, Young-Ho Kim, Juho Kim

CHI 2024 πŸ“„ arxiv 🌐 website human-ai-interaction visualization

πŸ’‘ This paper shows that integrating generative AI into visual search helps designers express vague or abstract ideas more precisely, leading to more satisfying and diverse creative exploration outcomes.

Demystifying Tacit Knowledge in Graphic Design: Characteristics, Instances, Approaches, and Guidelines

Kihoon Son, DaEun Choi, Tae Soo Kim, Juho Kim

CHI 2024 πŸ† Best Paper Honorable Mention πŸ“„ arxiv 🌐 website learning creativity

πŸ’‘ This paper identifies 123 examples of hard-to-articulate expert knowledge in graphic design, revealing which types remain unsupported by current tools and offering guidelines for making this hidden expertise more shareable.

Teach AI How to Code: Using Large Language Models as Teachable Agents for Programming Education

Hyoungwook Jin, Seonghee Lee, Hyungyu Shin, Juho Kim

CHI 2024 πŸ† Best Paper Honorable Mention πŸ“„ arxiv 🌐 website human-ai-interaction learning

πŸ’‘ This paper shows that large language models can be prompted to act as "teachable" student chatbots that strategically ask questions and simulate confusion, helping programming learners deepen their understanding by explaining concepts to an AI tutee.

EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria

Tae Soo Kim, Yoonjoo Lee, Jamin Shin, Young-Ho Kim, Juho Kim

CHI 2024 πŸ“„ arxiv 🌐 website human-ai-interaction

πŸ’‘ This paper shows that letting developers define custom evaluation criteria in natural language helps them refine AI prompts faster by revealing systematic weaknesses across many outputs at once.

Natural Language Dataset Generation Framework for Visualizations Powered by Large Language Models

Hyung-Kwon Ko, Hyeon Jeon, Gwanmo Park, Dae Hyun Kim, Nam Wook Kim, Juho Kim, Jinwook Seo

CHI 2024 πŸ“„ arxiv code & data human-ai-interaction visualization

πŸ’‘ This paper shows that large language models can automatically generate diverse natural language descriptions from chart specifications, helping researchers build better conversational interfaces for data visualization without manually creating training datasets.

FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets

Seonghyeon Ye, Doyoung Kim, Sungdong Kim, Hyeonbin Hwang, Seungone Kim, Yongrae Jo, James Thorne, Juho Kim, Minjoon Seo

ICLR 2024 πŸ† Spotlight πŸ“„ arxiv πŸ’» code 🌐 website human-ai-interaction

πŸ’‘ This paper shows that evaluating language models on specific skills (like reasoning or factual accuracy) rather than overall preference scores reveals clearer performance differences and produces more reliable assessments of model capabilities.

Understanding Users' Dissatisfaction with ChatGPT Responses: Types, Resolving Tactics, and the Effect of Knowledge Level

Yoonsu Kim, Jueon Lee, Seoyoung Kim, Jaehyuk Park, Juho Kim

IUI 2024 arXiv 🌐 website human-ai-interaction

πŸ’‘ This paper reveals that most user frustrations with ChatGPT stem from the model misunderstanding intent rather than factual errors, and that 72% of these frustrations remain unresolved even when users try to fix themβ€”suggesting chatbot interfaces need better tools for helping users recover from failures.

ExpressEdit: Video Editing with Natural Language and Sketching

Bekzat Tilekbay, Saelyne Yang, Michal A. Lewkowicz, Alex Suryapranata, Juho Kim

IUI 2024 🌐 website video creativity human-ai-interaction

πŸ’‘ This paper shows that combining natural language and sketching lets novice video editors express and implement their ideas more easily, helping democratize video production by reducing the technical barriers to creating engaging informational content.

Bridging Learnersourcing and AI: Exploring the Dynamics of Student-AI Collaborative Feedback Generation

Anjali Singh, Christopher Brooks, Xu Wang, Warren Li, Juho Kim, Deepti Pandey

LAK 2024 πŸ† Best Short Paper Award arXiv learning human-ai-interaction social-computing

πŸ’‘ This paper shows that students revising AI-generated hints produce better feedback than writing from scratch, offering educators a practical way to improve peer learning while teaching critical evaluation of AI outputs.

2023 Publications

Beyond Instructions: A Taxonomy of Information Types in How-to Videos

Saelyne Yang, Sangkyung Kwak, Juhoon Lee, Juho Kim

CHI 2023 video learning

πŸ’‘ This paper identifies 21 distinct types of information in how-to videos beyond step-by-step instructions, enabling better video navigation tools that help viewers quickly find specific knowledge like explanations or tips.

How Older Adults Use Online Videos for Learning

Seoyoung Kim, Donghoon Shin, Jeongyeon Kim, Soonwoo Kwon, Juho Kim

CHI 2023 video learning

πŸ’‘ This paper shows that older adults prefer practical over technical video content and interact less with video controls, helping designers create more accessible online learning platforms for aging populations.

2022 Publications

RLens: A Computer-aided Visualization System for Supporting Reflection on Language Learning under Distributed Tutorship

Meng Xia, Yankun Zhao, Jihyeong Hong, Mehmet Hamza Erol, Taewook Kim, Juho Kim

Learning at Scale 2022 πŸ“š acm dl presentation video learning visualization

πŸ’‘ This paper shows that visualizing and grouping feedback from multiple online language tutors helps learners track their progress more effectively, addressing the challenge of inconsistent guidance in gig-economy tutoring platforms.

SoftVideo: Improving the Learning Experience of Software Tutorial Videos with Collective Interaction Data

Saelyne Yang, Jisu Yim, Aitolkyn Baigutanova, Seoyoung Kim, Minsuk Chang, Juho Kim

IUI 2022 πŸ“š acm dl 🌐 website video learning social-computing

πŸ’‘ Learning software from tutorial videos often means frustrating back-and-forth switching and missed stepsβ€”this system uses past learners' interaction patterns to flag difficult moments and help users catch errors before falling behind.

Promptiverse: Scalable Generation of Scaffolding Prompts through Human-AI Hybrid Knowledge Graph Annotation

Yoonjoo Lee, John Joon Young Chung, Tae Soo Kim, Jean Y. Song, Juho Kim

CHI 2022 πŸ“š acm dl video presentation 🌐 website human-ai-interaction learning social-computing

πŸ’‘ This paper shows that combining human expertise with AI-powered knowledge graphs can generate diverse learning prompts at 40 times the scale of manual authoring, helping online educators better support learners with different backgrounds and knowledge levels.

Mobile-Friendly Content Design for MOOCs: Challenges, Requirements, and Design Opportunities

Jeongyeon Kim, Yubin Choi, Meng Xia, Juho Kim

CHI 2022 πŸ† Best Paper Award πŸ“š acm dl video presentation 🌐 website learning

πŸ’‘ This paper reveals that most online course videos fail basic mobile readability standards because creators overlook how learners actually watch on phonesβ€”in bright sunlight, while commutingβ€”suggesting adaptive visual designs could significantly improve learning accessibility.

2021 Publications

The MOOClet Framework: Unifying Experimentation, Dynamic Improvement, and Personalization in Online Courses

Mohi Reza, Juho Kim, Ananya Bhattacharjee, Anna N. Rafferty, Joseph Jay Williams

Learning at Scale 2021 πŸ“š acm dl learning

πŸ’‘ This paper shows that modular software components can turn online course elements into mini-laboratories for continuous testing and personalization, helping educators rapidly improve student experiences through real-time experimentation rather than waiting months for results.

StarryThoughts: Facilitating Diverse Opinion Exploration on Social Issues

Hyunwoo Kim, Haesoo Kim, Kyung Je Jo, Juho Kim

CSCW 2021. Proceedings of the ACM on Human-Computer Interaction Vol. 5, Issue CSCW1, Article 66 (April 2021) πŸ“š acm dl live demo 🌐 website social-computing visualization

πŸ’‘ This paper shows that organizing online opinions by demographic identity and semantic viewpoint helps users encounter perspectives outside their usual echo chambers, making public discourse on divisive social issues more representative and less polarized.

Supporting Collaborative Sequencing of Small Groups through Visual Awareness

Tae Soo Kim, Nitesh Goyal, Jeongyeon Kim, Juho Kim, Sungsoo Ray Hong

CSCW 2021. Proceedings of the ACM on Human-Computer Interaction Vol. 5, Issue CSCW1, Article 176 (April 2021) πŸ“š acm dl 🌐 website social-computing visualization

πŸ’‘ This paper shows that visual awareness tools can help small groups more efficiently build consensus when collaboratively creating sequencesβ€”like planning trips or scheduling coursesβ€”by making everyone's preferences and conflicts easier to recognize.

Understanding the Role of User Interface for Multi-Criteria Decision-Making in Supporting Exploratory Usage of Information Systems

Sungsoo Ray Hong, Rafal Kocilenik, Cecilia Aragon, Sarah Battersby, Juho Kim

HICCS-54 2021 dl visualization

πŸ’‘ This paper shows that how interfaces display and collect user preferences significantly affects people's experience when making decisions involving multiple competing criteria, helping designers create tools that better support everyday comparison tasks like choosing products or services.

2020 Publications

What Makes It Hard for Users to Follow Software Tutorial Videos?

Saelyne Yang, Juho Kim

HCI Korea 2020 learning

πŸ’‘ This paper identifies four key factorsβ€”user skill, software complexity, tutorial quality, and user-tutorial interactionβ€”that determine whether people can successfully follow software tutorial videos, guiding creators toward more effective instructional content.

ProtoChat: Supporting the Conversation Design Process with Crowd Feedback

Yoonseo Choi, Toni-Jan Monserrat, Jeongeon Park, Hyungyu Shin, Nyoungwoo Lee, Juho Kim

CSCW 2020. Proceedings of the ACM on Human-Computer Interaction Vol. 4, Issue CSCW3, Article 225 (December 2020) πŸ“š acm dl 🌐 website social-computing creativity

πŸ’‘ This paper shows that integrating crowd feedback directly into chatbot design tools helps conversation designers quickly identify usability problems and iterate on their designs without the delays of traditional user recruitment.

Messaging Beyond Texts with Real-time Image Suggestions

Joon-Gyum Kim, Taesik Gong, Kyungsik Han, Juho Kim, JeongGil Ko, Sung-Ju Lee

MobileHCI 2020 πŸ“š acm dl human-ai-interaction

πŸ’‘ This paper introduces a system that automatically suggests relevant images during mobile chat conversations, helping users communicate more expressively without the friction of manually searching for visual content.

Understanding How People Reason about Aesthetic Evaluations of Artificial Intelligence

Changhoon Oh, Seonghyeon Kim, Jinhan Choi, Jinsu Eun, Soomin Kim, Juho Kim, Joonhwan Lee, Bongwon Suh

DIS 2020 πŸ† Honorable Mention Award πŸ“š acm dl human-ai-interaction creativity

πŸ’‘ This paper shows that people interpret AI aesthetic judgments through the lens of their own expertise, helping designers understand how to build AI systems for subjective domains that diverse users can actually make sense of.

I Share, You Care: Private Status Sharing and Sender-Controlled Notifications in Mobile Instant Messaging

Hyunsung Cho, Jinyoung Oh, Juho Kim, Sung-Ju Lee

CSCW 2020. Proceedings of the ACM on Human-Computer Interaction Vol. 4, Issue CSCW1, Article 34 (May 2020) πŸ“š acm dl 🌐 website πŸŽ₯ video social-computing

πŸ’‘ This paper shows that letting messaging app users share context-specific availability and control whether messages trigger notifications reduces pressure to respond immediately while cutting unwanted interruptions.

No More One Liners: Bringing Context into Emoji Recommendations

Joon-Gyum Kim, Taesik Gong, Bogoan Kim, JaeYeon Park, Woojeong Kim, Evey Huang, Kyungsik Han, Juho Kim, JeongGil Ko, Sung-Ju Lee

ACM Transactions on Social Computing (TSC) Vol. 3, Article 9 (Apr 2020) πŸ“š acm dl human-ai-interaction

πŸ’‘ This paper shows that recommending emojis based on entire conversation context rather than just the current sentence helps users find desired emojis 38% faster and enables suggestions for emoji-only messages.

2019 Publications

Confronting the tensions where UX meets AI

Henriette Cramer, Juho Kim

Interactions 26.6 (2019): 69-71 πŸ“š acm dl human-ai-interaction

πŸ’‘ This paper launches a forum for exploring the practical and ethical challenges designers face when creating user experiences powered by AI, bridging gaps between research insights and real-world implementation.

Design for Collaborative Information-Seeking: Understanding User Challenges and Deploying ComeTogether Using Collaborative Dynamic Queries

Sungsoo (Ray) Hong, Minhyang (Mia) Suh, Tae Soo Kim, Irina Smoke, Sang-Wha Sien, Janet Ng, Mark Zachry, Juho Kim

CSCW 2019. Proceedings of the ACM on Human-Computer Interaction Vol. 3, Issue CSCW, Article 106 (November 2019) πŸ“š acm dl social-computing visualization

πŸ’‘ This paper reveals that common search and filter tools fail group decision-making because they're designed for individuals, causing communication breakdowns and unequal workloads when people try to plan activities together.

Efficient Elicitation Approaches to Estimate Collective Crowd Answers

John Joon Young Chung, Jean Y. Song, Sindhu Kutty, Sungsoo (Ray) Hong, Juho Kim, Walter S. Lasecki

CSCW 2019. Proceedings of the ACM on Human-Computer Interaction Vol. 3, Issue CSCW, Article 62 (November 2019) πŸ† Honorable Mention Award πŸ“š acm dl blog post social-computing

πŸ’‘ This paper shows that collecting diverse crowd opinions efficiently is possible by varying how annotators express their answers, helping researchers build machine learning datasets that better capture ambiguity without requiring massive annotation budgets.

Popup: Reconstructing 3D Video Using Particle Filtering to Aggregate Crowd Responses

Jean Y. Song, Stephan J. Lemmer, Michael Xieyang Liu, Shiyan Yan, Juho Kim, Jason J. Corso, Walter S. Lasecki

IUI 2019 πŸ“š acm dl talk video video social-computing

πŸ’‘ This paper shows that combining crowdsourced annotations across video frames using particle filtering produces more accurate 3D reconstructions of traffic incidents, helping autonomous vehicle developers create training data from ordinary dashboard and surveillance footage.

FourEyes: Leveraging Tool Diversity as a Means to Improve Aggregate Accuracy in Crowdsourcing

Jean Y. Song, Raymond Fok, Juho Kim, Walter S. Lasecki

ACM Transactions on Interactive Intelligent Systems (TIIS) 10, 1, Article 3 (August 2019) πŸ“š acm dl social-computing

πŸ’‘ This paper shows that using multiple different annotation tools reduces shared errors in crowdsourced image labeling, helping machine learning practitioners collect more accurate training data by offsetting the systematic biases each tool introduces.

2018 Publications

Personalized Motivation-supportive Messages for Increasing Participation in Crowd-civic Systems

Paul Grau, Babak Naderi, Juho Kim

CSCW 2018. Proceedings of the ACM on Human-Computer Interaction Vol. 2, Issue CSCW, Article 60 (November 2018) πŸ“š acm dl πŸ“Š slides social-computing

πŸ’‘ This paper shows that personalized messages supporting people's sense of autonomy, competence, and connection can help civic platforms better sustain volunteer participation in community problem-solving efforts.

RecipeScape: An Interactive Tool for Analyzing Cooking Instructions at Scale

Minsuk Chang, Leonore Guillain, Hyeungshik Jung, Vivian Hare, Juho Kim, Maneesh Agrawala

πŸ’‘ This paper shows that cooking instructions can be computationally analyzed and clustered by procedural similarity, helping culinary professionals quickly identify distinct approaches and ingredient patterns across hundreds of recipes.

Enhancing Online Problems Through Instructor-Centered Tools for Randomized Experiments

Joseph Jay Williams, Anna Rafferty, Dustin Tingley, Andrew Ang, Walter S. Lasecki, Juho Kim

πŸ’‘ This paper shows that giving instructors easy-to-use tools for running randomized experiments on digital homework problems helps them systematically discover which hints and feedback actually work best for their students.

Two Tools are Better Than One: Tool Diversity as a Means of Improving Aggregate Crowd Performance

Jean Y. Song, Raymond Fok, Alan Lundgard, Fan Yang, Juho Kim, Walter S. Lasecki

IUI 2018 πŸ† Best Student Paper Honorable Mention πŸ“š acm dl blog social-computing

πŸ’‘ This paper shows that giving different crowdworkers different annotation tools reduces shared mistakes that persist after combining responses, helping researchers collect more accurate image segmentation data for training computer vision systems.

Korero: Facilitating Complex Referencing of Visual Materials in Asynchronous Discussion Interface

Soon Hau Chua, Toni-Jan Keith Monserrat, Dongwook Yoon, Juho Kim, Shengdong Zhao

CSCW 2018. Proceedings of the ACM on Human-Computer Interaction Vol. 1, Issue CSCW, Article 34 (November 2017) πŸ“š acm dl πŸ“Š slides social-computing

πŸ’‘ This paper shows that a redesigned discussion interface can make it significantly easier to reference specific parts of videos or documents, helping online learners and collaborators engage more deeply with shared materials.

2017 Publications

Designing Interactive Distance Cartograms to Support Urban Travelers

Sungsoo (Ray) Hong, Rafal Kocielnik, Min-Joon Yoo, Sarah Battersby, Juho Kim, Cecilia Aragon

PacificVis 2017 visualization

πŸ’‘ This paper shows that redesigning distance cartograms with context-preserving techniques and targeted interactions helps urban travelers quickly compare travel times to destinations without losing their sense of geographic orientation.

2016 Publications

Revising Learner Misconceptions Without Feedback: Prompting for Reflection on Anomalous Facts

Joseph Williams, Tania Lombrozo, Anne Hsu, Bernd Huber, Juho Kim

CHI 2016 πŸ† Best of CHI Honorable Mention πŸ“š acm dl talk video learning

πŸ’‘ Asking online learners to explain why surprising facts are trueβ€”rather than just think about themβ€”helps correct statistical misconceptions without personalized instructor feedback, especially when multiple counterexamples challenge wrong beliefs simultaneously.

AXIS: Generating Explanations at Scale with Learnersourcing and Machine Learning

Joseph Williams, Juho Kim, Anna Rafferty, Samuel Maldonado, Krzysztof Z. Gajos, Walter Lasecki, Neil Heffernan

Learning at Scale 2016 πŸ† Honorable Mention πŸ“š acm dl flipped conference learning social-computing human-ai-interaction

πŸ’‘ This paper shows that combining student-generated explanations with machine learning can produce homework explanations as helpful as instructor-written ones, helping online learning platforms scale high-quality feedback without requiring expert effort.

BudgetMap: Engaging Taxpayers in the Issue-Driven Classification of a Government Budget

Nam Wook Kim, Jonghyuk Jung, Eun-Young Ko, Songyi Han, Chang Won Lee, Juho Kim, Jihee Kim

πŸ’‘ This paper shows that crowdsourcing issue-based tags for government budget items helps ordinary citizens better understand public spending by connecting abstract line items to social issues they care about.

2015 Publications

Factful: Engaging Taxpayers in the Public Discussion of a Government Budget

Juho Kim, Eun-Young Ko, Jonghyuk Jung, Chang Won Lee, Nam Wook Kim, Jihee Kim

CHI 2015 πŸ† Best of CHI Honorable Mention 🌐 website 30-sec preview πŸ“š acm dl πŸ“Š slides social-computing

πŸ’‘ This paper shows that augmenting news articles with government budget data and fact-checking tools helps citizens discuss public spending more critically and with better evidence, making complex fiscal information accessible for democratic participation.

Apparition: Crowdsourced User Interfaces That Come To Life As You Sketch Them

Walter Lasecki, Juho Kim, Nick Rafter, Onkur Sen, Jeffery Bigham, Michael Bernstein

CHI 2015 πŸ† Best of CHI Honorable Mention πŸ“š acm dl social-computing creativity

πŸ’‘ This paper shows that combining real-time crowdsourcing with sketch recognition lets designers create working interactive prototypes as fast as they can draw and speak, dramatically accelerating the feedback cycle in interface design.

2014 Publications

Data-Driven Interaction Techniques for Improving Navigation of Educational Videos

Juho Kim, Philip J. Guo, Carrie J. Cai, Shang-Wen (Daniel) Li, Krzysztof Z. Gajos, Robert C. Miller

πŸ’‘ This paper shows that visualizing how other learners navigate educational videos can make finding specific information feel more efficient, helping online learners and course designers understand which video moments attract the most attention.

Crowdsourcing Step-by-Step Information Extraction to Enhance Existing How-to Videos

Juho Kim, Phu Nguyen, Sarah Weir, Philip J. Guo, Robert C. Miller, Krzysztof Z. Gajos

CHI 2014 πŸ† Best of CHI Honorable Mention 🌐 website πŸ“Š slides πŸŽ₯ video πŸ“š acm dl video social-computing

πŸ’‘ This paper shows that crowdsourced step-by-step annotations significantly improve how people learn from instructional videos, offering a scalable method to make millions of existing tutorials easier to navigate and follow.

Frenzy: Collaborative Data Organization for Creating Conference Sessions

Lydia Chilton, Juho Kim, Paul AndrΓ©, Felicia Cordeiro, James Landay, Dan Weld, Steven P. Dow, Robert C. Miller, Haoqi Zhang

CHI 2014 πŸ† Best of CHI Honorable Mention πŸŽ₯ video πŸ“š acm dl social-computing

πŸ’‘ This paper shows that decomposing conference session planning into crowd-sourced metadata tagging and constraint satisfaction reduces a full-day task to 88 minutes, helping program committees organize papers more efficiently.

2013 Publications

Community Clustering: Leveraging an Academic Crowd to Form Coherent Conference Sessions

Paul AndrΓ©, Haoqi Zhang, Juho Kim, Lydia B. Chilton, Steven P. Dow, Robert C. Miller

HCOMP 2013 πŸ† Notable Paper Award social-computing

πŸ’‘ This paper shows that crowdsourcing paper groupings from committee members and authors produces conference sessions as good as or better than traditional manual organizing, while revealing hidden connections between papers that make scheduling more flexible.

Cobi: A Community-Informed Conference Scheduling Tool

Juho Kim, Haoqi Zhang, Paul AndrΓ©, Lydia B. Chilton, Wendy Mackay, Michel Beaudouin-Lafon, Robert C. Miller, Steven P. Dow

πŸ’‘ This paper shows that involving conference attendees and authors in providing scheduling preferences helps organizers resolve conflicts more effectively, making large academic conferences better organized through community input rather than top-down planning alone.

2012 Publications

2011 Publications

ReadWriter: Task Automation and Feedback Support for Bloggers with Inline Syntax [[ ]]

Juho Kim, Chen-Hsiang Yu, Robert C. Miller, Krzysztof Z. Gajos

Unpublished Manuscript, 2011 creativity

πŸ’‘ This paper shows that embedding natural-language help requests directly into blog drafts can streamline writing by automatically routing tasks to software, crowds, or readers, helping bloggers maintain creative flow while getting timely feedback.

Theses

2015 Publications

Book Chapters

2016 Publications

Posters, Demos, & Workshop Papers

2025 Publications

IdeaBlocks: Expressing and Reusing Exploratory Intents for Design Exploration with Generative AI

DaEun Choi, Kihoon Son, Jaesang Yu, Hyunjoon Jung, Juho Kim

UIST 2025 Demos human-ai-interaction

Applying the Gricean Maxims to a Human-LLM Interaction Cycle: Design Insights from a Participatory Approach

Yoonsu Kim, Brandon Chin, Kihoon Son, Seoyoung Kim, Juho Kim

CHI 2025 Extended Abstracts human-ai-interaction

Expandora: Broadening Design Exploration with Text-to-Image Model

DaEun Choi, Kihoon Son, Hyunjoon Jung, Juho Kim

CHI 2025 Extended Abstracts human-ai-interaction

2024 Publications

2023 Publications

Designing for AI-Powered Social Computing Systems

Gionnieve Lim, Hyunwoo Kim, Yoonseo Choi, Toby Jia-Jun Li, Chinmay Kulkarni, Hariharan Subramonyam, Joseph Seering, Michael S. Bernstein, Amy X. Zhang, Elena L. Glassman, Simon Perrault, Juho Kim

CSCW SIG human-ai-interaction

2022 Publications

When AI Meets the K-Pop culture: A case study of fans' perception of AI Private Call

Eun Jeong Kang, Haesoo Kim, Hyunwoo Kim, Juho Kim

NeurIPS 2022 Workshop on Cultures in AI / AI in Culture openreview social-computing

2021 Publications

2020 Publications

2019 Publications

2018 Publications

2017 Publications

Connecting Instructors, Learning Scientists, and Reinforcement Learning Researchers via Collaborative Dynamic Personalized Experimentation

Joseph Jay Williams, Anna Rafferty, Andrew Ang, Dustin Tingley, Walter Lasecki, Juho Kim

3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2017) learning

2016 Publications

2015 Publications

2014 Publications

2013 Publications

2012 Publications

2009 Publications