Designing Looms as Kits for Collaborative AssemblyBoth engineering kits and collaboration skills are increasingly prevalent in education and are often used in conjunction, yet little research addresses how kit hardware can be designed to support collaboration. Collaboration skills are best acquired through carefully designed collaborative experiences. While prior work highlights technology's important role in fostering collaboration, less research explicitly explores hardware design. This paper posits that certain design features of kits can promote or hinder collaboration. We present a user study evaluating the collaborative assembly of two loom kits: one for higher education (RoboLoom) and one for individuals (Ashford Loom). We developed a coding scheme from the 3Cs framework (coordination, cooperation, and communication) to analyze which hardware features influenced collaboration. We find five design feature categories that may influence collaboration: repetitiveness, specificity, difficulty, parallelizability, and physicality. This paper presents our findings and recommendations for implementing these features into educational kit hardware design to create opportunities for collaboration.2025SSSamantha Speer et al.Makerspace CultureParticipatory DesignPrototyping & User TestingUIST
Creating Furniture-Scale Deployable Objects with a Computer-Controlled Sewing MachineWe introduce a novel method for fabricating functional flat-to-shape objects using a large computer-controlled sewing machine (11 ft / 3.4m wide), a process that is both rapid and scalable beyond the machine's sewable area. Flat-to-shape deployable objects can allow for quick and easy need-based activation, but the selective flexibility required can involve complex fabrication or tedious assembly. In our method, we sandwich rigid form-defining materials, such as plywood and acrylic, between layers of fabric. The sewing process secures these layers together, creating soft hinges between the rigid inserts which allow the object to transition smoothly into its three-dimensional functional form with little post-processing.2025STSapna Tayal et al.Carnegie Mellon University, Human-Computer Interaction InstituteDesktop 3D Printing & Personal FabricationShape-Changing Materials & 4D PrintingCHI
Investigating Why Clinicians Deviate from Standards of Care: Liberating Patients from Mechanical Ventilation in the ICUClinical practice guidelines, care pathways, and protocols are designed to support evidence-based practices for clinicians; however, their adoption remains a challenge. We set out to investigate why clinicians deviate from the "Wake Up and Breathe" protocol, an evidence-based guideline for liberating patients from mechanical ventilation in the intensive care unit (ICU). We conducted over 40 hours of direct observations of live clinical workflows, 17 interviews with frontline care providers, and 4 co-design workshops at three different medical intensive care units. Our findings indicate that unlike prior literature suggests, disagreement with the protocol is not a substantial barrier to adoption. Instead, the uncertainty surrounding the application of the protocol for individual patients leads clinicians to deprioritize adoption in favor of tasks where they have high certainty. Reflecting on these insights, we identify opportunities for technical systems to help clinicians in effectively executing the protocol and discuss future directions for HCI research to support the integration of protocols into clinical practice in complex, team-based healthcare settings.2024NYNur Yildirim et al.Carnegie Mellon UniversityMental Health Apps & Online Support CommunitiesUser Research Methods (Interviews, Surveys, Observation)CHI
Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care UnitAdvances in artificial intelligence (AI) have enabled unprecedented capabilities, yet innovation teams struggle when envisioning AI concepts. Data science teams think of innovations users do not want, while domain experts think of innovations that cannot be built. A lack of effective ideation seems to be a breakdown point. How might multidisciplinary teams identify buildable and desirable use cases? This paper presents a first hand account of ideating AI concepts to improve critical care medicine. As a team of data scientists, clinicians, and HCI researchers, we conducted a series of design workshops to explore more effective approaches to AI concept ideation and problem formulation. We detail our process, the challenges we encountered, and practices and artifacts that proved effective. We discuss the research implications for improved collaboration and stakeholder engagement, and discuss the role HCI might play in reducing the high failure rate experienced in AI innovation.2024NYNur Yildirim et al.Carnegie Mellon UniversityGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationMental Health Apps & Online Support CommunitiesCHI
SPEERLoom: An Open-Source Loom Kit for Interdisciplinary Engagement in Math, Engineering, and TextilesWeaving is a fabrication process that is grounded in mathematics and engineering: from the binary, matrix-like nature of the pattern drafts weavers have used for centuries, to the punch card programming of the first Jacquard looms. This intersection of disciplines provides an opportunity to ground abstract mathematical concepts in a concrete and embodied art, viewing this textile art through the lens of engineering. Currently, available looms are not optimized to take advantage of this opportunity to increase mathematics learning by providing hands-on interdisciplinary learning in collegiate classrooms. In this work, we present SPEERLoom: an open-source, robotic Jacquard loom kit designed to be a tool for interweaving cloth fabrication, mathematics, and engineering to support interdisciplinary learning in the classroom. We discuss the design requirements and subsequent design of SPEERLoom. We also present the results of a pilot study in a post-secondary class finding that SPEERLoom supports hands-on, interdisciplinary learning of math, engineering, and textiles.2023SSSamantha Speer et al.Textile Art & Craft DigitizationUIST
Creating Design Resources to Scaffold the Ideation of AI ConceptsAdvances in artificial intelligence have enabled unprecedented technical capabilities, yet making these advances useful in the real world remains challenging. We engaged in a Research through Design process to improve the ideation of AI products and services. We developed a design resource capturing AI capabilities based on 40 AI features commonly used across various domains. To probe its usefulness, we created a set of slides illustrating AI capabilities and asked designers to ideate AI-enabled user experiences. We also incorporated capabilities into our own design process to brainstorm concepts with domain experts and data scientists. Our research revealed that designers should focus on innovations where moderate AI performance creates value. We reflect on our process and discuss research implications for creating and assessing resources to systematically explore AI’s problem-solution space.2023NYNur Yildirim et al.Generative AI (Text, Image, Music, Video)Human-LLM CollaborationPrototyping & User TestingDIS
uKnit: A Position-aware Reconfigurable Machine-knitted Wearable for Gestural Interaction and Passive Sensing using Electrical Impedance Tomography A scarf is inherently reconfigurable: wearers often use it as a neck wrap, a shawl, a headband, a wristband, and more. We developed uKnit, a scarf-like soft sensor with scarf-like reconfigurability, built with machine knitting and electrical impedance tomography sensing. Soft wearable devices are comfortable and thus attractive for many human-computer interaction scenarios. While prior work has demonstrated various soft wearable capabilities, each capability is device- and location-specific, being incapable of meeting users' various needs with a single device. In contrast, uKnit explores the possibility of one-soft-wearable-for-all. We describe the fabrication and sensing principles behind uKnit, demonstrate several example applications, and evaluate it with 10-participant user studies and a washability test. uKnit achieves 88.0%/78.2% accuracy for 5-class worn-location detection and 80.4%/75.4% accuracy for 7-class gesture recognition with a per-user/universal model. Moreover, it identifies respiratory rate with an error rate of 1.25 bpm and detects binary sitting postures with an average accuracy of 86.2%.2023TYTianhong Catherine Yu et al.Carnegie Mellon UniversityElectrical Muscle Stimulation (EMS)Haptic WearablesHuman Pose & Activity RecognitionCHI
How Experienced Designers of Enterprise Applications Engage AI as a Design MaterialHCI research has explored AI as a design material, suggesting that designers can envision AI's design opportunities to improve UX. Recent research claimed that enterprise applications offer an opportunity for AI innovation at the user experience level. We conducted design workshops to explore the practices of experienced designers who work on cross-functional AI teams in the enterprise. We discussed how designers successfully work with and struggle with AI. Our findings revealed that designers can innovate at the system and service levels. We also discovered that making a case for an AI feature's return on investment is a barrier for designers when they propose AI concepts and ideas. Our discussions produced novel insights on designers' role on AI teams, and the boundary objects they used for collaborating with data scientists. We discuss the implications of these findings as opportunities for future research aiming to empower designers in working with data and AI.2022NYNur Yildirim et al.Carnegie Mellon UniversityGenerative AI (Text, Image, Music, Video)AI-Assisted Decision-Making & AutomationCHI
Personal Jacquard WeavingWe present an inexpensive tabletop loom that offers fully computational patterning while maintaining the flexibility of handweaving. Our loom can be assembled for under US\$200 with 3D printed parts, and it can be controlled straightforwardly over USB. Our loom is explicitly a \emph{hand} loom: that is, a weaver is required to operate the weaving process and may mediate row-by-row patterning and material specifics like yarn tension. This approach combines the flexibility of fully analog handweaving with the computational affordances of digital fabrication: it enables the incorporation of special techniques and materials, as well as allowing for the possibility of computational and creative interventions in the weaving process itself -- for skill-building, for interactive design, or for creative reflection. We describe the mechanical and electronic implementation of our loom and show examples of its use for personal fabrication.2021LALea Albaugh et al.Carnegie Mellon UniversityDesktop 3D Printing & Personal FabricationCustomizable & Personalized ObjectsCHI
Engineering Multifunctional Spacer Fabrics Through Machine KnittingMachine knitting is an increasingly accessible fabrication technology for producing custom soft goods. However, recent machine knitting research has focused on knit shaping, or on adapting hand-knitting patterns. We explore a capability unique to machine knitting: producing multilayer spacer fabrics. These fabrics consist of two face layers connected by a monofilament filler yarn which gives the structure stiffness and volume. We show how to vary knit patterning and yarn parameters in spacer fabrics to produce tactile materials with embedded functionality for forming soft actuated mechanisms and sensors with tunable density, stiffness, material bias, and bristle properties. These soft mechanisms can be rapidly produced on a computationally-controlled v-bed knitting machine and integrated directly into soft objects.2021LALea Albaugh et al.Carnegie Mellon UniversityShape-Changing Interfaces & Soft Robotic MaterialsShape-Changing Materials & 4D PrintingCHI
Design Adjectives: A Framework for Interactive Model-Guided Exploration of Parameterized Design SpacesMany digital design tasks require a user to set a large number of parameters. Gallery-based interfaces provide a way to quickly evaluate examples and explore the space of potential designs, but require systems to predict which designs from a high-dimensional space are the right ones to present to the user. In this paper we present the design adjectives framework for building parameterized design tools in high dimensional design spaces. The framework allows users to create and edit design adjectives, machine-learned models of user intent, to guide exploration through high-dimensional design spaces. We provide a domain-agnostic implementation of the design adjectives framework based on Gaussian process regression, which is able to rapidly learn user intent from only a few examples. Learning and sampling of the design adjective occurs at interactive rates, making the system suitable for iterative design workflows. We demonstrate use of the design adjectives framework to create design tools for three domains: materials, fonts, and particle systems. We evaluate these tools in a user study showing that participants were able to easily explore the design space and find designs that they liked, and in professional case studies that demonstrate the framework’s ability to support professional design concepting workflows.2020ESEvan Shimizu et al.Knowledge Worker Tools & WorkflowsPrototyping & User TestingUIST
Digital Fabrication Tools at Work: Probing Professionals' Current Needs and Desired FuturesDigital fabrication tools have transformed how people work in micro- and small-scale manufacturing settings. While increasing efficiency and precision, these tools raise concerns around user agency and control. This paper describes an exploratory study investigating the felt work experience and desired futures of professionals who use fabrication tools. We conducted co-design workshops with 23 professionals who use 3D printers, laser cutters, and CNC routers. We probed about current practices; machine awareness and autonomy; and user agency. Our findings reveal that current tools are not very professional. They are unreliable and untrustworthy. Participants desired smarter tools that can actively prevent errors and perform self-calibration and self-maintenance. They had few concerns that more intelligence would impact agency. They desired tools that could negotiate trade-offs between time, cost, and quality; and that can operate as super-human shop assistants. We discuss the implications of these findings as opportunities for research that can improve professionals' work experience.2020NYNur Yildirim et al.Carnegie Mellon UniversityDesktop 3D Printing & Personal FabricationLaser Cutting & Digital FabricationCircuit Making & Hardware PrototypingCHI
Painting with CATS: Camera-Aided Texture SynthesisWe present CATS, a digital painting system that synthesizes textures from live video in real-time, short-cutting the typical brush- and texture- gathering workflow. Through the use of boundary-aware texture synthesis, CATS produces strokes that are non-repeating and blend smoothly with each other. This allows CATS to produce paintings that would be difficult to create with traditional art supplies or existing software. We evaluated the effectiveness of CATS by asking artists to integrate the tool into their creative practice for two weeks; their paintings and feedback demonstrate that CATS is an expressive tool which can be used to create richly textured paintings.2019TSTicha Sethapakdi et al.Carnegie Mellon UniversityCreative Coding & Computational ArtCHI
Geppetto: Enabling Semantic Design of Expressive Robot BehaviorsExpressive robots are useful in many contexts, from industrial to entertainment applications. However, designing expressive robot behaviors requires editing a large number of unintuitive control parameters. We present an interactive, data-driven system that allows editing of these complex parameters in a semantic space. Our system combines a physics-based simulation that captures the robot's motion capabilities, and a crowd-powered framework that extracts relationships between the robot's motion parameters and the desired semantic behavior. These relationships enable mixed-initiative exploration of possible robot motions. We specifically demonstrate our system in the context of designing emotionally expressive behaviors. A user-study finds the system to be useful for more quickly developing desirable robot behaviors, compared to manual parameter editing.2019RDRuta Desai et al.Carnegie Mellon UniversitySocial Robot InteractionHuman-Robot Collaboration (HRC)CHI
KnitPick: Programming and Modifying Complex Knitted Textures for Machine and Hand KnittingKnitting creates complex, soft objects with unique and controllable texture properties that can be used to create interactive objects. However, little work addresses the challenges of using knitted textures. We present KnitPick: a pipeline for interpreting pre-existing hand-knitting texture patterns into a directed-graph representation of knittable structures (KnitGraphs) which can be output to machine and hand-knitting instructions. Using KnitPick, we contribute a measured and photographed data set of \totaltextures{} knitted textures. Based on findings from this data set, we contribute two algorithms for manipulating KnitGraphs. KnitCarving shapes a graph while respecting a texture, and KnitPatching combines graphs with disparate textures while maintaining a consistent shape. Using these algorithms and textures in our data set we are able to create three Knitting based interactions: roll, tug, and slide. KnitPick is the first system to bridge the gap between hand- and machine-knitting when creating complex knitted textures.2019MHMegan Hofmann et al.Shape-Changing Interfaces & Soft Robotic MaterialsShape-Changing Materials & 4D PrintingTextile Art & Craft DigitizationUIST
Assembly-aware Design of Printable Electromechanical DevicesFrom smart toys and household appliances to personal robots, electromechanical devices play an increasingly important role in our daily lives. Rather than relying on gadgets that are mass-produced, our goal is to enable casual users to custom-design such devices based on their own needs and preferences. To this end, we present a computational design system that leverages the power of digital fabrication and the emergence of affordable electronics such as sensors and microcontrollers. The input to our system consists of a 3D representation of the desired device's shape, and a set of user-preferred off-the-shelf components. Based on this input, our method generates an optimized, 3D printable enclosure that can house the required components. To create these designs automatically, we formalize a new spatio-temporal model that captures the entire assembly process, including the placement of the components within the device, mounting structures and attachment strategies, the order in which components must be inserted, and collision-free assembly paths. Using this model as a technical core, we then leverage engineering design guidelines and efficient numerical techniques to optimize device designs. In a user study, which also highlights the challenges of designing such devices, we find our system to be effective in reducing the entry barriers faced by casual users in creating such devices. We further demonstrate the versatility of our approach by designing and fabricating three devices with diverse functionalities.2018RDRuta Desai et al.Circuit Making & Hardware PrototypingCustomizable & Personalized ObjectsUIST