Visual design instructors often provide multi-modal feedback, mixing annotations with text. Prior theory emphasizes the importance of actionable feedback, where “actionability” lies on a spectrum—from surfacing relevant design concepts to suggesting concrete fixes. How might creativity tools implement annotations that support such feedback, and how does the actionability of feedback impact novices’ process-related behaviors, perceptions of creativity, learning of design principles, and overall outcomes? We introduce VizCrit, a system for providing computational feedback that supports the actionability spectrum, realized through algorithmic issue detection and visual annotation generation. In a between-subjects study (N=36), novices revised a design under one of three conditions: textbook-based, awareness-centered, or solution-centered feedback. We found that solution-centered feedback led to fewer design issues and higher self-perceived creativity compared with textbook-based feedback, although expert ratings on creativity showed no significant differences. We discuss the implications for AI in Creativity Support Tools, including the potential of calibrating feedback actionability to help novices balance productivity with learning, growth, and developing design awareness.

This paper explores how and why artists with disabilities use, do not use, or modify tools in their creative practice.To inform the design of future creativity support tools (CSTs), we learned from 12 artists with a wide range of disabilities and mediums, including photography, theater, and drawing.Through their stories, we show instances of crip technoscience happening in the wild.We discuss how our findings may lead to expanded definitions of CSTs to include networks of interdependence and how artists with disabilities negotiate and foreground bodily boundaries in their practice.While our participants rejected the notion of adapting their tools, they did misuse existing tools when making art about their disabled experiences.Finally, we offer suggestions for CST designers to re-examine implicit norms around their tools' goals, input modalities, and if a new accessible tool is even needed in the first place.

Advances in AI have opened up the potential for creativity tools to computationally generate design feedback. In a future when designers can request feedback anytime on demand, how would the timing of these requests impact novices’ creative learning processes? What are the tradeoffs of providing access to feedback throughout a design task (in-action) versus only providing feedback after (on-action)? We explored these questions through a Wizard-of-Oz study (N=20) using an interactive design probe, where participants could request feedback either throughout the design process or only after they complete a full draft. We found that in-action participants frequently request feedback, resulting in better improvements as indicated by a greater decrease in issues in their final design. However, we saw that in-action feedback can also risk users overly relying on feedback instead of engaging in more holistic self-evaluation. We discuss the implications of our insights on designing tools for creative feedback.

Advances in AI, particularly large language models (LLMs), can transform creative work. When developing a new idea, LLMs can help designers gather information, find competitors, and generate alternatives. However, LLM responses tend to be long-winded or contain inaccuracies, placing a burden on users to carefully synthesize information. In our formative studies with 52 students and five instructors, we find that novice designers typically lack guidance on how to compose prompts, reflect critically on LLM responses, and extract key information to help shape an idea. Building on these insights, we explore an alternative approach for interacting with LLMs, not via chat, but rather through structured templates. Collaborative design templates are a well-established strategy for helping novices think, organize information, and reflect on creative work. Developed as a digital whiteboard plugin, Jamplate integrates LLM capabilities into design templates, streamlining the collection and organization of user-generated content and LLM responses within the template structure. In a preliminary study with 8 novice designers, participants expressed that Jamplate’s reflective questions and in-situ guidance improved their ability to think critically and improve ideas more effectively. We discuss the potential of designing LLM-enhanced templates to instigate critical reflection.

Traditional design galleries enable users to search for examples based on surface attributes (e.g., color or style), and largely obscure underlying principles (e.g., hierarchy or readability). We conducted three studies to explore how galleries could be constructed to help novices learn key design principles. Study 1 revealed that novices gain perspective by observing how designs evolve throughout a process. Study 2 found that novices are better at identifying design issues when viewing iterations that show improvements for just one principle at a time, rather than multiple. Building on these insights, we created ProcessGallery, a tool that enables users to browse contrasting pairs of early-and-late iterations of designs that highlight key improvements organized by design principles. In Study 3, a within-subjects experiment, sixteen participants iterated on a seed design after viewing examples in ProcessGallery versus a traditional gallery. Using ProcessGallery, participants found more appropriate examples, assessed designs better, and preferred ProcessGallery for learning compared to a traditional gallery.

Unit visualizations have been widely used in data storytelling within interactive articles and videos. However, authoring data stories that contain animated unit visualizations is challenging due to the tedious, time-consuming process of switching back and forth between writing a narrative and configuring the accompanying visualizations and animations. To streamline this process, we present DataParticles, a block-based story editor that leverages the latent connections between text, data, and visualizations to help creators flexibly prototype, explore, and iterate on a story narrative and its corresponding visualizations. To inform the design of DataParticles, we interviewed 6 domain experts and studied a dataset of 44 existing animated unit visualizations to identify the narrative patterns and congruence principles they employed. A user study with 9 experts showed that DataParticles can significantly simplify the process of authoring data stories with animated unit visualizations by encouraging exploration and supporting fast prototyping.

We describe the use of comics to teach computing by having learners create, design, and arrange comic panels. We designed comic-based learning toolkits, guided by the following research question: How do we support the informal learning of computing concepts for elementary school learners through a physical comic-based learning toolkit? This question emerged as a result of our partnership with a community organization that teaches art to elementary school learners through the production and distribution of art subscription boxes. Subscription boxes contain art materials and instruction manuals that learners can use to create artistic artifacts at home. Partnering with the organization, we explored how to teach computing through art activities and designed a subscription box for comic creation activities that used materials such as paper comic panels, coloring pens, magnets, and activity manuals. Our learning toolkits guide learners to use computing concepts in the story-crafting process, for example: decomposing narratives with comic panels, sequencing comic panels to create a narrative flow, using conditionals (e.g., if-else) for character decision-making within the story, using loops to repeat comic story events, and iterating on or refining the comic to create and develop a cohesive narrative flow.

Color filters are ubiquitous across visual digital media due to their transformative effect. However, it can be difficult to understand how a color filter will affect an image, especially for novices. In order to become experts, we argue that novices need to develop Goodwin’s notion of Professional Vision [29]. Then, they can "see" and interpret their work in terms of their domain knowledge like experts. Using the theory of Professional Vision, we present two design objectives for systems that aim to help users develop expertise. These goals were used to develop Color Field, an interactive visualization of color filters as a vector field over the Hue-Saturation-Lightness color space. We conducted an exploratory user study in which five color grading novices and four experts were asked to analyze color filters. We found that Color Field enabled multiple strategies to make sense of filters (e.g. reviewing the overall shape of the vector field) and discuss them (e.g. using spatial language). We conclude with other applications of Color Field and future work to leverages Professional Vision in HCI.

Photographic composition is often taught as alignment with composition grids-most commonly, the rule of thirds. Professional photographers use more complex grids, like the harmonic armature, to achieve more diverse dynamic compositions. We are interested in understanding whether these complex grids are helpful to amateurs.

With the increased popularity of cameras, more and more people are interested in learning photography. People are willing to invest in expensive cameras as a medium for their artistic expression, but few have access to in-person classes to help improve upon their artistic skills. Inspired by critique sessions common in in-person art practice classes, we propose design principles for creative learning. We focus on applying these principles to design new interfaces that provide contextual in-camera feedback to aid users in learning visual elements of photography. We interactively visualize results of image processing algorithms as additional information for the user at capture-time. In this paper, we describe our design principles, and apply these principles in the design of three guided photography interfaces: one to explore lighting options for a portrait, one to highlight overall composition, and one to aid in de-cluttering.

We present a capture-time tool designed to help casual photographers orient their subject to achieve a user-specified target facial appearance. The inputs to our tool are an HDR environment map of the scene captured using a 360 camera, and a target facial appearance, selected from a gallery of common studio lighting styles. Our tool computes the optimal orientation for the subject to achieve the target lighting using a computationally efficient precomputed radiance transfer-based approach. It then tells the photographer how far to rotate about the subject. Optionally, our tool can suggest how to orient a secondary external light source (e.g. a phone screen) about the subject's face to further improve the match to the target lighting. We demonstrate the effectiveness of our approach in a variety of indoor and outdoor scenes using many different subjects to achieve a variety of looks. A user evaluation suggests that our tool reduces the mental effort required by photographers to produce well-lit portraits.

We contribute a qualitative investigation of how teens with visual impairments (VIP) access smartphone photography, from the time they take photos through editing and sharing them on social media. We observed that they largely want to engage with photos visually, similarly to their sighted peers, and have developed strategies around photo capture, editing, sharing, and consumption that attempt to mitigate usability limitations of current photography and social media apps. We demonstrate the need for more work examining how young people with low vision engage with smartphone photography and social media, as they are heavy users of such technologies and have challenges distinct from their totally blind counterparts. We conclude with design considerations to alleviate the usability barriers we uncovered and for making smartphone photography and social media more accessible and relevant for VIPs.

We present the results of an exploration to understand the accessibility of smartphone photography for people with motor impairments. We surveyed forty-six people and interviewed twelve people about capturing, editing, and sharing photographs on smartphones. We found that people with motor impairments encounter many challenges with smartphone photography, resulting in users capturing fewer photographs than they would like. Participants described various strategies they used to overcome challenges in order to capture a quality photograph. We also found that photograph quality plays a large role in deciding which photographs users share and how often they share, with most participants rating their photographs as average or poor quality compared to photos shared on their social networks. Additionally, we created design probes of two novel photography interfaces and received feedback from our interview participants about their usefulness and functionality. Based on our findings, we propose design recommendations for how to improve the accessibility of mobile photoware for people with motor impairments.

As drones become ubiquitous, it is important to understand how cultural differences impact human-drone interaction. A previous elicitation study performed in the USA illustrated how users would intuitively interact with drones. We replicated this study in China to gain insight into how these user-defined interactions vary across the two cultures. We found that as per the US study, Chinese participants chose to interact primarily using gesture. However, Chinese participants used multi-modal interactions more than their US counterparts. Agreement for many proposed interactions was high within each culture. Across cultures, there were notable differences despite similarities in interaction modality preferences. For instance, culturally-specific gestures emerged in China, such as a T-shape gesture for stopping the drone. Participants from both cultures anthropomorphized the drone, and welcomed it into their personal space. We describe the implications of these findings on designing culturally-aware and intuitive human-drone interaction.

Personal drones are becoming popular. It is challenging to design how to interact with these flying robots. We present a Wizard-of-Oz (WoZ) elicitation study that informs how to naturally interact with drones. Results show strong agreement between participants for many interaction techniques, as when gesturing for the drone to stop. We discovered that people interact with drones as with a person or a pet, using interpersonal gestures, such as beckoning the drone closer. We detail the interaction metaphors observed and offer design insights for human-drone interactions.