As generative AI tools increasingly influence creative practice, they raise longstanding HCI questions about how creatives learn complex software and how they can be better supported. We conducted an interview study with artists and hobbyists (n=8) and a follow-up survey (n=159) to understand how this population approaches and seeks guidance for GenAI image tools. We found that creatives commonly use either self-experimentation or tutorials to explore GenAI tools, yet many struggle with confusing AI terminology. To gain further insight into creatives’ learning experiences, we developed a research probe to elicit creatives’ perceptions of structured guidance. Our user study with 17 creatives revealed that, even when creatives described the guidance as helpful for understanding AI, many still preferred self-experimentation, feeling that guidance could limit their creativity. Our findings highlight a central tension in supporting AI literacy for creatives: balancing guidance and promoting literacy while preserving creative freedom.

Learning to use feature-rich software is a persistent challenge, but generative AI tools promise to lower this barrier by replacing complex navigation with natural language prompts. We investigated how people approach prompt-based tools for 3D modeling in an observational study with 26 participants (14 casuals, 12 professionals). Consistent with earlier work, participants skipped tutorials and manuals, relying on trial and error. What differed in the generative AI context was where and how they sought support: the prompt box became the entry point for learning, collapsing onboarding into immediate action, while some casual users turned to external LLMs for prompts. Professionals used 3D expertise to refine iterations and critically evaluated outputs, often discarding models that did not meet their standards, whereas casual users settled for “good enough.” We contribute empirical insights into how generative AI reshapes help-seeking, highlighting new practices of onboarding, recursive AI-for-AI support, and shifting expertise in interpreting outputs.

Developing AI literacy is increasingly urgent as generative AI reshapes creative practice. Yet most AI literacy frameworks are top-down and expert-driven, overlooking how literacy emerges organically in creative communities. To address this gap, we performed a large-scale analysis of 122k Reddit conversations from 80 creative-oriented subreddits over a time period of three years. Our analysis identified four consistent themes in AI literacy-related discussions, and we further traced how discourse shifted alongside major AI events. Surprisingly, creators primarily frame AI literacy around how to use tools effectively—foregrounding practice and task skills—while discussions of AI capabilities and ethics surge only around high-profile events. Our findings suggest that AI literacy is dynamic, practice-driven, and event-responsive rather than static or purely conceptual. This study provides insights for researchers, designers, and policymakers to develop learning resources, community support, and policies that better promote AI literacy in creative communities.

Large Language Model (LLM)-based in-application assistants, or copilots, can automate software tasks, but users often prefer learning by doing, raising questions about the optimal level of automation for an effective user experience. We investigated two automation paradigms by designing and implementing a fully automated copilot (AutoCopilot) and a semi-automated copilot (GuidedCopilot) that automates trivial steps while offering step-by-step visual guidance. In a user study (N=20) across data analysis and visual design tasks, GuidedCopilot outperformed AutoCopilot in user control, software utility, and learnability, especially for exploratory and creative tasks, while AutoCopilot saved time for simpler visual tasks. A follow-up design exploration (N=10) enhanced GuidedCopilot with task-and state-aware features, including in-context preview clips and adaptive instructions. Our findings highlight the critical role of user control and tailored guidance in designing the next generation of copilots that enhance productivity, support diverse skill levels, and foster deeper software engagement.

Informal learners of computational skills often find it difficult to self-direct their learning pursuits, which may be spread across different mediums and study sessions. Inspired by self-monitoring interventions from domains such as health and productivity, we investigate key requirements for helping informal learners better self-reflect on their learning experiences. We carried out two elicitation studies with article-based and interactive probes to explore a range of manual, automatic, and semi-automatic design approaches for capturing and presenting a learner's data. We found that although automatically generated visual overviews of learning histories are initially promising for increasing awareness, learners prefer having controls to manipulate overviews through personally relevant filtering options to better reflect on their past, plan for future sessions, and communicate with others for feedback. To validate our findings and expand our understanding of designing self-monitoring tools for use in real settings, we gathered further insights from experts, who shed light on factors to consider in terms of data collection techniques, designing for reflections, and carrying out field studies. Our findings have several implications for designing learner-centered self-monitoring interventions that can be both useful and engaging for informal learners.

Motion graphics authoring is a time-intensive endeavor, demanding proficiency in various feature-rich software. Automated, example-based solutions are now being explored to simplify the motion graphics creation process. To investigate how such streamlined authoring tools impact motion designers’ workflows and perceptions of creativity, we deployed an end-to-end motion graphics authoring tool to 14 users, spanning casual to professional design expertise. Our key findings reveal a dichotomy: casual designers embraced the tool’s automation, finding empowerment in its simplicity, even at the expense of losing narrative control. Conversely, professionals expressed reservations and raised concerns about the trade-offs between efficiency and creative autonomy. Notably, the level of automation in animation emerged as a point of contention, underscoring differing expectations between the two groups. Our work contributes insights into such nuances, offering implications for designing the next generation of motion graphics authoring tools that cater to a broad spectrum of creative aspirations and abilities.

Large Language Model (LLM) assistants, such as ChatGPT, have emerged as potential alternatives to search methods for helping users navigate complex, feature-rich software. LLMs use vast training data from domain-specific texts, software manuals, and code repositories to mimic human-like interactions, offering tailored assistance, including step-by-step instructions. In this work, we investigated LLM-generated software guidance through a within-subject experiment with 16 participants and follow-up interviews. We compared a baseline LLM assistant with an LLM optimized for particular software contexts, SoftAIBot, which also offered guidelines for constructing appropriate prompts. We assessed task completion, perceived accuracy, relevance, and trust. Surprisingly, although SoftAIBot outperformed the baseline LLM, our results revealed no significant difference in LLM usage and user perceptions with or without prompt guidelines and the integration of domain context. Most users struggled to understand how the prompt’s text related to the LLM’s responses and often followed the LLM’s suggestions verbatim, even if they were incorrect. This resulted in difficulties when using the LLM’s advice for software tasks, leading to low task completion rates. Our detailed analysis also revealed that users remained unaware of inaccuracies in the LLM’s responses, indicating a gap between their lack of software expertise and their ability to evaluate the LLM’s assistance. With the growing push for designing domain-specific LLM assistants, we emphasize the importance of incorporating explainable, context-aware cues into LLMs to help users understand prompt-based interactions, identify biases, and maximize the utility of LLM assistants.

The growing need for programming and computational skills has led to a demand for teaching approaches that can appeal to a broader audience. We explore the design and creation of tangible objects and interactions to make introductory programming concepts more engaging and less tedious. We introduce TangiBooks, a platform that uses paper-based tangibles with embedded electronics and augments paper with sensory interactions like visuals, sounds, haptics, and kinesthetics to reinforce learning. The platform is implemented as proof-of-concept and offers four self-contained lessons on key concepts such as algorithms, variables, conditionals, and loops. Results from our observational study (12 adult learners, 6 instructors) showed that participants found TangiBooks to be playful, appealing to their senses, and valuable for sparking curiosity, with potential pedagogical benefits for promoting reflection among learners. TangiBooks has implications for HCI researchers in furthering the design space of paper-based tangibles in the learning domain and empowering instructors to innovate and personalize lessons.

Explainer motion graphics videos that use a combination of graphical elements and movement to convey a visual message are becoming increasingly popular among amateur creators in different domains. But, to author motion graphics videos, amateurs either have to face a steep learning curve with professional design tools or struggle with re-purposing slide-sharing tools that are easier to access but have limited animation capabilities. To simplify the process of motion graphics authoring, we present the design and implementation of Katika, an end-to-end system for creating shots based on a script, adding artworks and animation from a crowdsourced library, and editing the video using semi-automated transitions. Our observational study illustrates that participants (N=11) enjoyed using Katika and, within a one-hour session, managed to create an explainer motion graphics video. We identify opportunities for future HCI research to lower the barriers to entry and democratize the authoring of motion graphics videos.

Instructors regularly learn and customize various feature-rich software applications to meet their unique classroom needs. Although instructors often prefer social help from colleagues to navigate this complex and time-consuming learning process, it can be difficult for them to locate relevant task-specific customizations, a challenge only exacerbated by the transition to online teaching due to COVID-19. To mitigate this, we explored how instructors could use an example-based customization sharing platform to discover, try, and appropriate their colleagues’ customizations within a learning management system (LMS). Our field deployment study revealed diverse ways that ten instructors from different backgrounds used customization sharing features to streamline their workflows, improve their LMS feature awareness, and explore new possibilities for designing their courses to match student expectations. Our findings provide new knowledge about customization sharing practices, highlighting the complex interplay of expertise, software learnability, domain-specific workflows, and social perceptions.

University-level introductory programming courses (e.g., CS1) offer a structured and formal approach for learning programming where instructors design their own curriculum and materials to help students learn difficult concepts. But, there are a myriad of free resources increasingly available online for learning programming that university students can easily access at their fingertips. In this paper, we investigate to what extent students in CS1 make use of resources curated by their instructor vs. online resources that they locate on their own. We conducted surveys and interviews in two consecutive CS1-equivalent classes at a large North-American university and explored how students made selections and assessed the relevance and utility of web-based resources. We observed that students had a strong desire for quick answers and looked for 'exact' matches for their queries in forums rather than consulting videos or other resources that provide higher level explanations more suitable for novices. However, given that these students had a limited vocabulary related to CS1 topics, they struggled in locating the desired information and relied on shallow trial-and-error processes without a clear strategy or self-reflection. Interestingly, despite the lack of perceived success, students still considered their pursuit of online resources as more convenient and less costly than asking for help. Our main contribution is in presenting insights about CS1 students' motivations and behaviours in using web-based resources on their own. We reflect on these findings by drawing upon theories from learning sciences and information sciences.

In recent years, more people from different backgrounds are trying to informally learn Machine Learning (ML) using a plethora of online resources, yet we know little about their motivations and learning strategies. We carried out interviews with 22 informal learners of ML from diverse job roles and backgrounds, including Computer Science, Medicine, Finance, and others, to understand their approaches, preferences, and challenges in locating and interacting with different resources to manage their learning. We analyzed our findings using the framework of self-directed learning and found that these informal learners struggled in all stages of self-direction, including identifying learning goals and selecting resources, and that their challenges were most acute in the last stage of gauging progress and evaluating outcomes. We identify several opportunities for future research to better understand and support informal learners of ML (and other complex technical skills). In particular, there is a need to foster more self-monitoring and self-reflection techniques that can help informal learners become more self-aware and effective in directing their learning.

Motion graphics videos offer a powerful means of communicating complex concepts through engaging visuals and animation. While filmmaking, marketing, or video games industries use these videos to tell compelling stories, others such as educators or domain experts face a steep learning curve. Creating even a short motion graphics video can be an arduous process that requires competency in scriptwriting, graphic design, animation, and skills in using various feature-rich software applications. We interviewed 19 casual and professional motion designers working on a range of motion graphics projects to understand their design processes and challenges. Our results reveal several difficulties that new motion designers face in getting started in the field and how they struggle to devise workarounds. We identify opportunities for HCI to lower entry barriers by designing user-centered tools that simplify the motion design process and incorporate example-based learning and collaborative approaches.

With the recent surge in online teaching, increasing numbers of instructors are using learning management systems (LMSs) to migrate their courses online and adapt their teaching methods. Given the steep learning curve for these feature-rich software applications, many instructors turn to online Q&A forums for help with learning and troubleshooting their LMS. We analyzed the content of 250 posts from a community-based Q&A forum for Canvas, a widely-used LMS, finding several recurring themes that illustrate how instructors are setting up courses for the first time, facilitating shared experiences between their students, and even seeking innovative ways to customize their course delivery. Our findings shed light on key barriers driving instructors to seek help and to what extent their help needs are actually addressed by the community. We discuss several opportunities for improving the design of LMSs and for better understanding and supporting instructors' custom needs to enhance their course delivery.

When breakdowns occur during a human-chatbot conversation, the lack of transparency and the “black-box” nature of task-oriented chatbots can make it difficult for end users to understand what went wrong and why. Inspired by recent HCI research on explainable AI solutions, we explored the design of in-application explainable chatbot interfaces (ChatrEx) that explain the underlying working of a chatbot during a breakdown. ChatrEx-VINC provides visual example-based step-by-step explanations in-context of the chat window whereas ChatrEx-VST provides explanations as a visual tour overlaid on the application interface. We implemented these chatbots for complex spreadsheet tasks and our comparative observational study (N=14) showed that the explanations provided by both ChatrEx-VINC and ChatrEx-VST enhanced users' understanding of the reasons for a breakdown and improved users' perceptions of usefulness, transparency, and trust. We identify several opportunities for future research to exploit explainable chatbot interfaces and better support human-chatbot interaction.

People increasingly access cross-device applications from their smartphones while on the go. Yet, they do not fully use the mobile versions for complex tasks, preferring the desktop version of the same application. We conducted a survey (N=77) to identify challenges when switching back and forth between devices. We discovered significant cross-device learnability issues, including that users often find exploring the mobile version frustrating, which leads to prematurely giving up on using the mobile version. Based on the findings, we created four design concepts as video prototypes to explore how to support cross-device learnability. The concepts vary in four key dimensions: the device involved, automation, temporality, and learning approach. Interviews (N=20) probing the design concepts identified individual differences affecting cross-device learning preferences, and that users are more motivated to use cross-device applications when offered the right cross-device learnability support. We conclude with future design directions for supporting seamless cross-device learnability.

Learning to use a learning management system (LMS) can often be complex and challenging for instructors who have little time to explore the application interface. We ran formative interviews (N=10) suggesting that instructors often prefer to consult colleagues to seek examples of customizations and their explanations. Based on our findings, we designed and developed Customizer, an in-context example-based customization sharing platform that runs atop a widely-used LMS and facilitates discovery of relevant customizations shared by peers. Customizer allows instructors to experiment with shared customizations in familiar contexts by copying their course content into on-the-fly testing environments, minimizing any risk of breaking their live course setups. Our usability evaluation (N=10) showed that most users found Customizer intuitive and useful, and that its exploratory interface was helpful in a variety of use cases. Furthermore, many participants saw potential for Customizer to improve their workflows in other applications beyond educational contexts.

Augmented Reality (AR) and Virtual Reality (VR) devices are becoming easier to access and use, but the barrier to entry for creating AR/VR applications remains high. Although the recent spike in HCI research on novel AR/VR tools is promising, we lack insights into how AR/VR creators use today's state-of-the-art authoring tools as well as the types of challenges that they face. We interviewed 21 AR/VR creators, which we grouped into hobbyists, domain experts, and professional designers. Despite having a variety of motivations and skillsets, they described similar challenges in designing and building AR/VR applications. We synthesize 8 key barriers that AR/VR creators face nowadays, starting from prototyping the initial experiences to dealing with "the many unknowns" during implementation, to facing difficulties in testing applications. Based on our analysis, we discuss the importance of considering end-user developers as a growing population of AR/VR creators, how we can build learning opportunities into AR/VR tools, and the need for building AR/VR toolchains that integrate debugging and testing.

Feature-rich applications such as word processors and spreadsheets are not only being used by adults but increasingly by children and older adults as well. Learning these applications is challenging as they offer hundreds of commands throughout the interface. We investigate how newcomers from different age groups explore the user interface of a feature-rich application to determine, locate, and use relevant features. We conducted an in-lab observational study with 10 children (10-12), 10 adults (20-35) and 10 older adults (60-75) who were first-time users of Microsoft OneNote. Our results illustrate key exploration differences across age groups, including that children were careful and performed as efficiently as the adults, whereas older adults spent a longer time and repeated sequences of failed selections. Further, their exploration style was negatively influenced by their past knowledge of similar applications. We discuss design interventions to accommodate these exploration differences and to improve software onboarding for newcomers.

Design professionals increasingly need to learn and use multiple feature-rich software to design complex artifacts in a variety of domains. In this work, we aimed to understand how design professionals learn new software features and seek help in today's modern workplace of interconnected spaces and increased access to shared online learning resources. We conducted 20 interviews with design professionals working in different disciplines such as architecture and planning, construction, structural design, and broadcast media, who used complex feature-rich applications on a daily basis. We found that despite the wide availability of learning resources on the web and decades of research dedicated to workplace knowledge management tools, design professionals still rely heavily on interpersonal help-seeking. Furthermore, while companies have devised a variety of technological channels to answer employees' help requests, such intermediate tools were often abandoned, and help-seeking conversations shifted to face-to-face learning episodes. Our findings point to design opportunities and shortcomings in the design of remote communication tools when seeking help for feature-rich software applications.

Online step-by-step text and video tutorials play an integral role in learning feature-rich software applications. However, when searching, users can find it difficult to assess whether a tutorial is designed for their level of software expertise. Novice users can struggle when a tutorial is out of their reach, whereas more advanced users can end up wasting time with overly simple, first-principles instruction. To assist users in selecting tutorials, we investigate the feasibility of using machine-learning techniques to automatically assess a tutorial's difficulty. Using Photoshop as our primary testbed, we develop a set of distinguishable tutorial features, and use these features to train a classifier that can label a tutorial as either Beginner or Advanced with 85% accuracy. To illustrate a potential application, we developed a tutorial browsing interface called TutVis. Our initial user evaluation provides insight into TutVis's ability to support users in a range of tutorial selection scenarios.

Question and answer (Q&A) sites can capture a range of user perspectives on using complex, feature-rich software. Little is known, however, on who is contributing to the sites. We look at contribution diversity from the perspective of gender in a domain with near gender parity: graphic design. Through content analysis of 330 answers from two popular Q&A sites and semi-structured interviews with 24 graphic designers, we examine who is contributing, what content, how the community shows appreciation towards their answers, and perceived motivations and barriers to participation. We find that despite gender balance in the field, women contribute far less frequently than men. We also see gender differences in contribution styles and user appreciation. Our interviews shed further light on how Q&A community cultures might be impacting men and women differently and how design choices made by the sites? developers might be exacerbating these differences. We suggest implications for design for improving gender inclusivity.

For most modern feature-rich software, considerable external help and learning resources are available on the web (e.g., documentation, tutorials, videos, Q&A forums). But, how do users new to an application discover and make use of such resources? We conducted in-lab and diary studies with 26 software newcomers from a variety of different backgrounds who were all using Fusion 360, a 3D modeling application, for the first time. Our results illustrate newcomers' diverse needs, perceptions, and help-seeking behaviors. We found a number of distinctions in how technical and non-technical users approached help-seeking, including: when and how they initiated the help-seeking process, their struggles in recognizing relevant help, the degree to which they made coordinated use of the application and different resources, and in how they perceived the utility of different help formats. We discuss implications for moving beyond "one-size-fits-all" help resources towards more structured, personalized, and curated help and learning materials.

Although modern classrooms are increasingly moving towards digital immersion and personalized learning, we have few insights into K-12 teachers' current practices, motivations, and barriers in setting up their digital classroom ecosystems. We interviewed 20 teachers on their process of discovering and integrating a vast range of productivity software and educational platforms in their classrooms, with a particular focus on how they personalize the UI and content of these tools (e.g., with plugins, templates, or option menus). We found that teachers largely depended on their own experimentation and professional circles to find, personalize, and troubleshoot software tools to support student needs or their own preferences. Teachers were often hesitant to attempt more advanced personalizations due to concerns over student confusion and increased troubleshooting load. We derive several design implications for HCI to better support teachers in sharing their personalized setups and helping their students benefit from digital immersion.

Learners increasingly refer to online videos for learning new technical concepts, but often overlook or forget key details. We investigated how retrieval practice, a learning strategy commonly used in education, could be designed to reinforce key concepts in online videos. We began with a formative study to understand users' perceptions of cued and free-recall retrieval techniques. We next designed a new in-context flashcard-based technique that provides expert-curated retrieval exercises in context of a video's playback. We evaluated this technique with 14 learners and investigated how learners engage with flashcards that are prompted automatically at predefined intervals or flashcards that appear on-demand. Our results overall showed that learners perceived automatically prompted flashcards to be less effortful and made the learners feel more confident about grasping key concepts in the video. However, learners found that on-demand flashcards gave them more control over their learning and allowed them to personalize their review of content. We discuss the implications of these findings for designing hybrid automatic and on-demand in-context retrieval exercises for online videos.

Instructors of hardware computing face many challenges including maintaining awareness of student progress, allocating their time adequately between lecturing and helping individual students, and keeping students engaged even while debugging problems. Based on formative interviews with 5 electronics instructors, we found that many circuit style behaviors could help novice users prevent or efficiently debug common problems. Drawing inspiration from the software engineering practice of coding style, these circuit style behaviors consist of best-practices and guidelines for implementing circuit prototypes that do not interfere with the functionality of the circuit, but help a circuit be more readable, less error-prone, and easier to debug. To examine if these circuit style behaviors could be peripherally enforced, aid an in-person instructor's ability to facilitate a workshop, and not monopolize instructor's attention, we developed CircuitStyle, a teaching aid for in-person hardware computing workshops. To evaluate the effectiveness of our tool, we deployed our system in an in-person maker-space workshop. The instructor appreciated CircuitStyle's ability to provide a broad understanding of the workshop's progress and the potential for our system to help instructors of various backgrounds better engage and understand the needs of their classroom.

The need for learning about emerging technologies and technical concepts among the general population is increasing, but formal explanations from online encyclopedias, textbooks, and articles are often rich in technical jargon and can be challenging to grasp. In this paper, we explore a novel approach for explaining technical concepts to non-technical users through the design of JargonLite, an interactive dictionary that shows how technical concepts can be used in everyday conversations. We evaluated curated conversation-driven explanations with 12 users who had little or no technical training and wanted to learn about big data concepts. We found that users perceived conversation-driven explanations to be easy to understand and these explanations helped them to maintain focus. We discuss the potential of using conversation-driven explanations as a learning tool for non-technical users and reflect on the advantages and disadvantages of this approach.

3D design software is increasingly available to children through libraries, maker spaces, and for free on the web. This unprecedented availability has the potential to unleash children's creativity in cutting edge domains, but is limited by the steep learning curve of the software. Unfortunately, there is little past work studying the breakdowns faced by children in this domain-most past work has focused on adults in professional settings. In this paper, we present a study of online learning resources and help-seeking strategies available to children starting out with 3D design software. We find that children face a range of challenges when trying to learn 3D design independently-tutorials present instructions at a granularity that leads to overlooked and incorrectly-performed actions, and online help-seeking is largely ineffective due to challenges with query formulation and evaluating found information. Based on our findings, we recommend design directions for next-generation help and learning systems tailored to children.

Conversational programmers represent a class of learners who are not required to write any code, yet try to learn programming to improve their participation in technical conversations. We carried out interviews with 23 conversational programmers to better understand the challenges they face in technical conversations, what resources they choose to learn programming, how they perceive the learning process, and to what extent learning programming actually helps them. Among our key findings, we found that conversational programmers often did not know where to even begin the learning process and ended up using formal and informal learning resources that focus largely on programming syntax and logic. However, since the end goal of conversational programmers was not to build artifacts, modern learning resources usually failed these learners in their pursuits of improving their technical conversations. Our findings point to design opportunities in HCI to invent learner-centered approaches that address the needs of conversational programmers and help them establish common ground in technical conversations.

We investigate how novice 3D modelers can remotely leverage real-time expert help to aid their learning tasks. We first carried out an observational study of remote novice-expert pairs of 3D modelers to understand traditional chat-based assistance in the context of learning 3D modeling. Next, we designed MarmalAid, a web-based 3D modeling tool with a novel real-time, in-context help feature that allows users to embed real-time chat conversations at any location within the 3D geometry of their models. Our user study with 12 novices who used both MarmalAid's real-time, in-context chat and an external chat tool to seek help, showed that novices found the real-time, in-context chat to be more useful and easier to use, and that experts asked for fewer clarifications, allowing the novices to ask more task-related questions. Our findings suggest to several design opportunities to utilize and extend the real-time, in-con-text heln concept in 3D modeline aoolications and bevond.

Users can often find it difficult to sift through dense help pages, tutorials, Q&A sites, blogs, and wikis to locate useful task-specific instructions for feature-rich applications. We present Social CheatSheet, an interactive information overlay that can appear atop any existing web application and retrieve relevant step-by-step instructions and tutorials curated by other users. Based on results of our formative study, the system offers several features for users to search, browse, filter, and bookmark community-generated help content and to ask questions and clarifications. Furthermore, Social CheatSheet includes embedded curation features for users to generate their own annotated notes and tutorials that can be kept private or shared with the user community. A weeklong deployment study with 15 users showed that users found Social CheatSheet to be useful and they were able to easily both add their own curated content and locate content generated by other users. The majority of users wanted to keep using the system beyond the deployment. We discuss the potential of Social CheatSheet as an application-independent platform driven by community curation efforts to lower the barriers in finding relevant help and instructions.

Recent research suggests that some students learn to program with the goal of becoming conversational programmers: they want to develop programming literacy skills not to write code in the future but mainly to develop conversational skills and communicate better with developers and to improve their marketability. To investigate the existence of such a population of conversational programmers in practice, we surveyed professionals at a large multinational technology company who were not in software development roles. Based on 3151 survey responses from professionals who never or rarely wrote code, we found that a significant number of them (42.6%) had invested in learning programming on the job. While many of these respondents wanted to perform traditional end-user programming tasks (e.g., data analysis), we discovered that two top motivations for learning programming were to improve the efficacy of technical conversations and to acquire marketable skillsets. The main contribution of this work is in empirically establishing the existence and characteristics of conversational programmers in a large software development context.

Interest in understanding and facilitating 3D digital fabrication is growing in the HCI research community. However, most of our insights about end-user interaction with fabrication are currently based on interactions of professional users, makers, and technology enthusiasts. We present a study of casual makers, users who have no prior experience with fabrication and mainly explore walk-up-and-use 3D printing services at public print centers, such as libraries, universities, and schools. We carried out 32 interviews with casual makers, print center operators, and fabrication experts to understand the motivations, workflows, and barriers in appropriating 3D printing technologies. Our results suggest that casual makers are deeply dependent on print center operators throughout the process from bootstrapping their 3D printing workflow, to seeking help and troubleshooting, to verifying their outputs. However, print center operators are usually not trained domain experts in fabrication and cannot always address the nuanced needs of casual makers. We discuss implications for optimizing 3D design tools and interactions that can better facilitate casual makers' workflows.

Thingiverse is the largest 3D design-sharing online community with millions of users. Thingiverse provides a low-barrier-to-entry for exploring 3D printing as users can quickly download premade 3D designs and ask design-specific questions. In this paper, we investigate users' activities on Thingiverse and their conversations by using quantitative and qualitative analyses. Our findings shed light on various barriers in using, customizing, and printing premade 3D designs. The results suggest that although Thingiverse plays a key role in helping users get started with basic 3D printing, there are many opportunities to streamline the design-download-customize-print workflows. In particular, opportunities exist for designers to provide richer metadata, clarifications, and expert tips to help users succeed in printing objects and customizing existing 3D designs.

As we increasingly strive for scientific rigor and generalizability in HCI research, should we entertain any hope that by doing good science, our discoveries will eventually be more transferrable to industry? We present an in-depth case study of how an HCI research innovation goes through the process of transitioning from a university project to a revenue-generating startup financed by venture capital. The innovation is a novel contextual help system for the Web, and we reflect on the different methods used to evaluate it and how research insights endure attempted dissemination as a commercial product. Although the extent to which any innovation succeeds commercially depends on a number of factors like market forces, we found that our HCI innovation with user-centered origins was in a unique position to gain traction with customers and garner buy-in from investors. However, since end users were not the buyers of our product, a strong user-centered focus obfuscated other critical needs of the startup and pushed out perspectives of non-user-centered stakeholders. To make the research-to-product transition, we had to focus on adoption-centered design, the process of understanding and designing for adopters and stakeholders of the product. Our case study raises questions about how we evaluate the novelty and research contributions of HCI innovations with respect to their potential for commercial impact.

The names, icons, and tooltips of commands in feature-rich software are an important source of guidance when locating and selecting amongst commands. Unfortunately, these cues can mislead users into believing that a command is appropriate for a given task, when another command would be more appropriate, resulting in wasted time and frustration. In this paper, we present command disambiguation techniques that inform the user of alternative commands before, during, and after an incorrect command has been executed. To inform the design of these techniques, we define categories of false-feedforward errors caused by misleading interface cues, and identify causes for each. Our techniques are the first designed explicitly to solve this problem in feature-rich software. A user study showed enthusiasm for the techniques, and revealed their potential to play a key role in learning of feature-rich software.

Despite the enthusiasm and initiatives for making programming accessible to students outside Computer Science (CS), unfortunately, there are still many unanswered questions about how we should be teaching programming to engineers, scientists, artists or other non-CS majors. We present an in-depth case study of first-year management engineering students enrolled in a required introductory programming course at a large North American university. Based on an inductive analysis of one-on-one interviews, surveys, and weekly observations, we provide insights into students' motivations, career goals, perceptions of programming, and reactions to the Java and Processing languages. One of our key findings is that between the traditional classification of non-programmers vs. programmers, there exists a category of conversational programmers who do not necessarily want to be professional programmers or even end-user programmers, but want to learn programming so that they can speak in the “programmer's language” and improve their perceived job marketability in the software industry.

Help-seekers on community-based software help forums often face difficulty in composing queries or troubleshooting requests that bring immediate resolution, forcing help givers to request clarification that delays diagnosis. We investigate the characteristics of a forum post that trigger these requests for clarification from help givers (e.g., missing information, unclear goals, non-standard terminology). We created a classification scheme based on such triggers and applied it to 1000 Q&A pairs from four popular consumer software help forums to understand the prevalence of these triggers across different applications. Even though the user interface for posting questions on the four forums that we studied was largely uniform, we found a large difference in the presence of these triggers across the forums. Our findings suggest that instead of trying to create universal automated tools and recommendations for improving question quality on software forums, we should take into account the unique characteristics of the software and its user community.

Users often make continued and sustained use of online resources to complement use of a desktop application. For example, users may reference online tutorials to recall how to perform a particular task. While often used in a coordinated fashion, the browser and desktop application provide separate, independent mechanisms for helping users find and re-find task-relevant information. In this paper, we describe InterTwine, a system that links information in the web browser with relevant elements in the desktop application to create interapplication information scent. This explicit link produces a shared interapplication history to assist in re-finding information in both applications. As an example, InterTwine marks all menu items in the desktop application that are currently mentioned in the front-most web page. This paper introduces the notion of interapplication information scent, demonstrates the concept in InterTwine, and describes results from a formative study suggesting the utility of the concept.

We present a multi-site field study to evaluate LemonAid, a crowdsourced contextual help approach that allows users to retrieve relevant questions and answers by making selections within the interface. We deployed LemonAid on 4 different web sites used by thousands of users and collected data over several weeks, gathering over 1,200 usage logs, 168 exit surveys, and 36 one-on-one interviews. Our results indicate that over 70% of users found LemonAid to be helpful, intuitive, and desirable for reuse. Software teams found LemonAid easy to integrate with their sites and found the analytics data aggregated by LemonAid a novel way of learning about users' popular questions. Our work provides the first holistic picture of the adoption and use of a crowdsourced contextual help system and offers several insights into the social and organizational dimensions of implementing such help systems for real-world applications.

Web-based technical support such as discussion forums and social networking sites have been successful at ensuring that most technical support questions eventually receive helpful answers. Unfortunately, finding these answers is still quite difficult, since users' textual queries are often incomplete, imprecise, or use different vocabularies to describe the same problem. We present LemonAid, a new approach to help that allows users to find help by instead selecting a label, widget, link, image or other user interface (UI) element that they believe is relevant to their problem. LemonAid uses this selection to retrieve previously asked questions and their corresponding answers. The key insight that makes LemonAid work is that users tend to make similar selections in the interface for similar help needs and different selections for different help needs. Our initial evaluation shows that across a corpus of dozens of tasks and thousands of requests, LemonAid retrieved a result for 90% of help requests based on UI selections and, of those, over half had relevant matches in the top 2 results.

Despite being an important channel for end-user assistance, few studies have directly investigated the interactions that occur in modern-day practice of software product support. We present results from a multi-dimensional analysis of product support activities at a leading design software company. We carried out a quantitative analysis of existing support requests, a survey with product support specialists, and follow-up interviews to understand the current practices in product support. In particular, we investigated the utility of different multimedia formats that modern web-based support systems enable. Our results showed that despite the value that these formats bring to support tasks, support specialists still face bottlenecks in remotely resolving software problems. We conclude by highlighting several opportunities in HCI for improving diagnosis and resolution of software issues over the web.

Despite the growing research on usability in the pre-development phase, we know little about post-deployment usability activities. To characterize these activities, we surveyed 333 full-time usability professionals and consultants working in large and small corporations from a wide range of industries. Our results show that, as a whole, usability professionals are currently not playing a substantial role in the post-deployment phase compared to other phases of user-centered design, but when they do, practitioners find their interactions quite valuable. We highlight opportunities in HCI research and practice to bridge this gap by working more closely with software support and maintenance teams. We also raise the need to understand what might be called 'usability maintenance,' that is, the process and procedures, by which usability is maintained after deployment.

Although usability methods are widely used for evaluating conventional graphical user interfaces and websites, there is a growing concern that current approaches are inadequate for evaluating complex, domain-specific tools. We interviewed 21 experienced usability professionals, including in-house experts, external consultants, and managers working in a variety of complex domains, and uncovered the challenges commonly posed by domain complexity and how practitioners work around them. We found that despite the best efforts by usability professionals to get familiar with complex domains on their own, the lack of formal domain expertise can be a significant hurdle for carrying out effective usability evaluations. Partnerships with domain experts lead to effective results as long as domain experts are willing to be an integral part of the usability team. These findings suggest that for achieving usability in complex domains, some fundamental educational changes may be needed in the training of usability professionals.

Many power users that contribute to open source projects have no intention of becoming regular contributors; they just want a bug fixed or a feature implemented. How often do these users participate in open source projects and what do they contribute? To investigate these questions, we analyzed the reports of Mozilla contributors who reported problems but were never assigned problems to fix. These analyses revealed that over 11 years and millions of reports, most of these 150,000 users reported non-issues that devolved into technical support, redundant reports with little new information, or narrow, expert feature requests. Reports that did lead to changes were reported by a comparably small group of experienced, frequent reporters, mostly before the release of Firefox 1. These results suggest that the primary value of open bug reporting is in recruiting talented reporters, and not in deriving value from the masses.

Open bug reporting allows end-users to express a vast array of unwanted software behaviors. However, users’ expectations often clash with developers’ implementation intents. We created a classification of seven common expectation violations cited by end-users in bug report descriptions and applied it to 1,000 bug reports from the Mozilla project. Our results show that users largely described bugs as violations of their own personal expectations, of specifications, or of the user community’s expectations. We found a correlation between a reporter’s expression of which expectation was being violated and whether or not the bug would eventually be fixed. Specifically, when bugs were expressed as violations of community expectations rather than personal expectations, they had a better chance of being fixed.