Identifying, Explaining, and Correcting Ableist Language with AIAbleist language perpetuates harmful stereotypes and exclusion, yet its nuanced nature makes it difficult to recognize and address. Artificial intelligence could serve as a powerful ally in the fight against ableist language, offering tools that detect and suggest alternatives to biased terms. This two-part study investigates the potential of large language models (LLMs), specifically ChatGPT, to rectify ableist language and educate users about inclusive communication. We compared GPT-4o generations with crowdsourced annotations from trained disability community members, then invited disabled participants to evaluate both. Participants reported equal agreement with human and AI annotations but significantly preferred the AI, citing its narrative consistency and accessible style. At the same time, they valued the emotional depth and cultural grounding of human annotations. These findings highlight the promise and limits of LLMs in handling culturally sensitive content. Our contributions include a dataset of nuanced ableism annotations and design considerations for inclusive writing tools.2026KSKynnedy Simone Smith et al.Carnegie Mellon UniversityHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)CHI
LogoMotion: Visually-Grounded Code Synthesis for Creating and Editing AnimationCreating animation takes time, effort, and technical expertise. To help novices with animation, we present LogoMotion, an AI code generation approach that helps users create semantically meaningful animation for logos. LogoMotion automatically generates animation code with a method called visually-grounded code synthesis and program repair. This method performs visual analysis, instantiates a design concept, and conducts visual checking to generate animation code. LogoMotion provides novices with code-connected AI editing widgets that help them edit the motion, grouping, and timing of their animation. In a comparison study on 276 animations, LogoMotion was found to produce more content-aware animation than an industry-leading tool. In a user evaluation (n=16) comparing against a prompt-only baseline, these code-connected widgets helped users edit animations with control, iteration, and creative expression.2025VLVivian Liu et al.Columbia University3D Modeling & AnimationCreative Coding & Computational ArtCHI
Copying style, Extracting value: Illustrators’ Perception of AI Style Transfer and its Impact on Creative LaborGenerative text-to-image models are disrupting the lives of creative professionals. Specifically, illustrators are threatened by models that claim to extract and reproduce their style. Yet, research on style transfer has rarely focused on their perspectives. We provided four illustrators with a model fine-tuned to their style and conducted semi-structured interviews about the model’s successes, limitations, and potential uses. Evaluating their output, artists reported that style transfer successfully copies aesthetic fragments but is limited by content-style disentanglement and lacks the crucial emergent quality of their style. They also deemed the others’ copies more successful. Understanding the results of style transfer as “boundary objects,” we analyze how they can simultaneously be considered unsuccessful by artists and poised to replace their work by others. We connect our findings to critical HCI frameworks, demonstrating that style transfer, rather than merely a Creativity Support Tool, should also be understood as a supply chain optimization one.2025JPJulien Porquet et al.University of CambridgeAI Ethics, Fairness & AccountabilityMotor Impairment Assistive Input TechnologiesInclusive DesignCHI
DynEx: Dynamic Code Synthesis with Structured Design Exploration for Accelerated Exploratory ProgrammingRecent advancements in large language models have significantly expedited the process of generating front-end code. This allows users to rapidly prototype user interfaces and ideate through code, a process known as exploratory programming. However, existing LLM code generation tools focus more on technical implementation details rather than finding the right design given a particular problem. We present DynEx, an LLM-based method for design exploration in accelerated exploratory programming. DynEx introduces a technique to explore the design space through a structured Design Matrix before creating the prototype with a modular, stepwise approach to LLM code generation. Code is generated sequentially, and users can test and approve each step before moving onto the next. A user study of 10 experts found that DynEx increased design exploration and enabled the creation of more complex and varied prototypes compared to a Claude Artifact baseline. We conclude with a discussion of the implications of design exploration for exploratory programming.2025JMJenny Ma et al.Columbia University, Computer Science DepartmentHuman-LLM CollaborationPrototyping & User TestingCHI
Metaphorian: Leveraging Large Language Models to Support Extended Metaphor Creation for Science WritingScience writers commonly use extended metaphors to communicate unfamiliar concepts in a more accessible way to a wider audience. However, creating metaphors for science writing is challenging even for professional writers; according to our formative study (n=6), finding inspiration and extending metaphors with coherent structures were critical yet significantly challenging tasks for them. We contribute Metaphorian, a system that supports science writers with the creation of scientific metaphors by facilitating the search, extension, and iterative revision of metaphors. Metaphorian uses a large language model-based workflow inspired by the heuristic rules revealed from a study with six professional writers. A user study (n=16) revealed that Metaphorian significantly enhances satisfaction, confidence, and inspiration in metaphor writing without decreasing writers' sense of agency. We discuss design implications for creativity support for figurative writing in science.2023JKJeongyeon Kim et al.Generative AI (Text, Image, Music, Video)AI-Assisted Creative WritingDIS
AngleKindling: Supporting Journalistic Angle Ideation with Large Language ModelsNews media often leverage documents to find ideas for stories, while being critical of the frames and narratives present. Developing angles from a document such as a press release is a cognitively taxing process, in which journalists critically examine the implicit meaning of its claims. Informed by interviews with journalists, we developed AngleKindling, an interactive tool which employs the common sense reasoning of large language models to help journalists explore angles for reporting on a press release. In a study with 12 professional journalists, we show that participants found AngleKindling significantly more helpful and less mentally demanding to use for brainstorming ideas, compared to a prior journalistic angle ideation tool. AngleKindling helped journalists deeply engage with the press release and recognize angles that were useful for multiple types of stories. From our findings, we discuss how to help journalists customize and identify promising angles, and extending AngleKindling to other knowledge-work domains.2023SPSavvas Petridis et al.Columbia UniversityHuman-LLM CollaborationAI-Assisted Creative WritingUser Research Methods (Interviews, Surveys, Observation)CHI
Social Dynamics of Human-AI Collaboration in Creative WritingRecently, large language models have made huge advances in generating coherent, creative text. While much research focuses on how users can interact with language models, less work considers the social-technical gap that this technology poses. What are the social nuances that underlie receiving support from a generative AI? In this work we ask when and why a creative writer might turn to a computer versus a peer or mentor for support. We interview 20 creative writers about their writing practice and their attitudes towards both human and computer support. We discover three elements that govern a writer’s interaction with support actors: 1) what writers desire help with, 2) how writers perceive potential support actors, and 3) the values writers hold. We align our results with existing frameworks of writing cognition and creativity support, uncovering the social dynamics which modulate user responses to generative technologies.2023KGKaty Ilonka Gero et al.Columbia UniversityHuman-LLM CollaborationAI-Assisted Creative WritingCHI
Improving Automatic Summarization for Browsing Longform Spoken DialogLongform spoken dialog delivers rich streams of informative content through podcasts, interviews, debates, and meetings. While production of this medium has grown tremendously, spoken dialog remains challenging to consume as listening is slower than reading and difficult to skim or navigate relative to text. Recent systems leveraging automatic speech recognition (ASR) and automatic summarization allow users to better browse speech data and forage for information of interest. However, these systems intake disfluent speech which causes automatic summarization to yield readability, adequacy, and accuracy problems. To improve navigability and browsability of speech, we present three training agnostic post-processing techniques that address dialog concerns of readability, coherence, and adequacy. We integrate these improvements with user interfaces which communicate estimated summary metrics to aid user browsing heuristics. Quantitative evaluation metrics show a 19\% improvement in summary quality. We discuss how summarization technologies can help people browse longform audio in trustworthy and readable ways.2023DLDaniel Li et al.Columbia UniversityConversational ChatbotsHuman-LLM CollaborationCHI
OPAL: Multimodal Image Generation for News IllustrationsAdvances in multimodal AI have presented people with powerful ways to create images from text. Recent work has shown that text-to-image generations are able to represent a broad range of subjects and artistic styles. However, finding the right visual language for text prompts is difficult. In this paper, we address this challenge with Opal, a system that produces text-to-image generations for news illustration. Given an article, Opal guides users through a structured search for visual concepts and provides a pipeline allowing users to generate illustrations based on an article's tone, keywords, and related artistic styles. Our evaluation shows that Opal efficiently generates diverse sets of news illustrations, visual assets, and concept ideas. Users with Opal generated two times more usable results than users without. We discuss how structured exploration can help users better understand the capabilities of human AI co-creative systems.2022VLVivian Liu et al.Generative AI (Text, Image, Music, Video)AI-Assisted Creative WritingUIST
Design Guidelines for Prompt Engineering Text-to-Image Generative ModelsText-to-image generative models are a new and powerful way to generate visual artwork. However, the open-ended nature of text as interaction is double-edged; while users can input anything and have access to an infinite range of generations, they also must engage in brute-force trial and error with the text prompt when the result quality is poor. We conduct a study exploring what prompt keywords and model hyperparameters can help produce coherent outputs. In particular, we study prompts structured to include subject and style keywords and investigate success and failure modes of these prompts. Our evaluation of 5493 generations over the course of five experiments spans 51 abstract and concrete subjects as well as 51 abstract and figurative styles. From this evaluation, we present design guidelines that can help people produce better outcomes from text-to-image generative models.2022VLVivian Liu et al.Columbia University, Columbia UniversityGenerative AI (Text, Image, Music, Video)CHI
Insights and Opportunities for HCI Research into Hurricane Risk CommunicationCommunicating risk to the public in the lead-up to tropical storms has the potential to significantly reduce the impacts on both livelihood and property. While significant research has been conducted in the storm risk community on how people receive, seek, and utilize risk information, given the importance of computing technologies and social media in these activities, human-centered design stands to make important contributions to this area. Drawing on an extensive literature review and 48 interviews with hurricane experts and members of the public, this paper makes three contributions. First, we provide a broad overview of hurricane risk communication. We then offer a set of guiding insights to inform HCI research work in this domain. Finally, we identify 6 opportunities that future human centered design work might pursue. In sum, this paper offers an invitation and a starting point for HCI to take up the problem of hurricane risk communication.2022RSRobert Soden et al.University of TorontoCommunity Engagement & Civic TechnologySustainable HCICHI
What Makes Tweetorials Tick: How Experts Communicate Complex Topics on TwitterPeople are increasingly getting information and news from social media. On Twitter we are seeing the emergence of "tweetorials" -- long, explanatory Twitter threads written by experts. In this work we study tweetorials as a form of science writing. While scientists have begun to champion the importance of Twitter as a science communication medium, few have studied how people are successfully using this medium to communicate complex and nuanced ideas. To understand how tweetorials work, we curated a collection of 46 clear and engaging tweetorials from multiple domains. We analyzed these tweetorials for the writing techniques that they employ, and found that while tweetorials use many traditional science writing techniques, they also use more subjective language, actively build credibility, and incorporate media in unique ways. In addition, we report on a workshop we ran to aid science PhD students in writing tweetorials, and find that while providing common tweetorial techniques improves their writing, the students still struggle to balance their scientific sensibilities with the informal tone associated with tweetorials. We discuss the implications of using informal and subjective language in science communication, as well as how technology can support scientists in writing tweetorials.2021KGKaty Ilonka Gero et al.Social MediaCSCW
Hierarchical Summarization for Longform Spoken DialogEvery day we are surrounded by spoken dialog. This medium delivers rich diverse streams of information auditorily; however, systematically understanding dialog can often be non-trivial. Despite the pervasiveness of spoken dialog, automated speech understanding and quality information extraction remains markedly poor, especially when compared to written prose. Furthermore, compared to understanding text, auditory communication poses many additional challenges such as speaker disfluencies, informal prose styles, and lack of structure. These concerns all demonstrate the need for a distinctly speech tailored interactive system to help users understand and navigate the spoken language domain. While individual automatic speech recognition (ASR) and text summarization methods already exist, they are imperfect technologies; neither consider user purpose and intent nor address spoken language induced complications. Consequently, we design a two stage ASR and text summarization pipeline and propose a set of semantic segmentation and merging algorithms to resolve these speech modeling challenges. Our system enables users to easily browse and navigate content as well as recover from errors in these underlying technologies. Finally, we present an evaluation of the system which highlights user preference for hierarchical summarization as a tool to quickly skim audio and identify content of interest to the user.2021DLDaniel Li et al.Conversational ChatbotsExplainable AI (XAI)UIST
Metaphoria: An Algorithmic Companion for Metaphor CreationCreative writing, from poetry to journalism, is at the crux of human ingenuity and social interaction. Existing creative writing support tools produce entire passages or fully formed sentences, but these approaches fail to adapt to the writer's own ideas and intentions. Instead we posit to build tools that generate ideas coherent with the writer's context and encourage writers to produce divergent outcomes. To explore this, we focus on supporting metaphor creation. We present Metaphoria, an interactive system that generates metaphorical connections based on an input word from the writer. Our studies show that Metaphoria provides more coherent suggestions than existing systems, and supports the expression of writers' unique intentions. We discuss the complex issue of ownership in human-machine collaboration and how to build adaptive creativity support tools in other domains.2019KGKaty Ilonka Gero et al.Columbia UniversityHuman-LLM CollaborationAI-Assisted Creative WritingCHI