Early-stage interdisciplinary research ideation is often challenged by limited expert access, uncertainty about what to ask, and the cognitive burden of synthesizing unfamiliar domain perspectives. This paper presents Perspectra, a forum-style multi-agent system that structures and visualizes deliberation among LLM-simulated domain experts to support exploration and refinement of emerging research ideas, while encouraging critical thinking and reflections. The interface design combines 1) a threaded canvas for parallel topic exploration with visualization of agent discourse dynamics informed by argumentation theory to aid sensemaking; and 2) feature that enables users to invite multiple self-chosen agents into an ongoing discussion. We conducted a user study with 18 participants, comparing Perspectra against a vanilla chat baseline given a task for the user to develop a short research proposal. Our findings show that Perspectra’s design elicits significantly more higher-order critical thinking behaviors during interactions with agents when compared to a traditional chat interface. We also observed more interdisciplinary user replies via forum-styled design, and more frequent and structured proposal revisions (rather than unstructured note-taking). Based on our findings, we further contribute interaction design implications of using multi-agent deliberation for complex ideation and knowledge search, combining flexibility with structured exploration to support user sensemaking and critical thinking.

When exploring data, analysts construct narratives about what the data means by asking questions, generating visualizations, reflecting on patterns, and revising their interpretations as new insights emerge. Yet existing analysis tools treat narrative as an afterthought, breaking the link between reasoning, reflection, and the evolving story from exploration. Consequently, analysts lose the ability to see how their reasoning evolves, making it harder to reflect systematically or build coherent explanations. To address this gap, we propose Narrative Scaffolding (NS), a framework for narrative-driven exploration that positions narrative construction as the primary interface for exploration and reasoning. We implemented this framework in a system that externalizes iterative reasoning through narrative-first entry, semantically aligned view generation, and reflection support via insight provenance and inquiry tracking. In a within-subject study (N = 20), we demonstrated that narrative scaffolding facilitates broader exploration, deeper reflection, and more defensible narratives. An evaluation with visualization literacy experts (N = 6) confirmed that the system produced outputs aligned with narrative intent and facilitated intentional exploration.

Students often take digital notes during live lectures, but current methods can be slow when capturing information from lecture slides or the instructor's speech, and require them to focus on their devices, leading to distractions and missing important details. This paper explores supporting live lecture note-taking with mixed reality (MR) to quickly capture lecture information and take notes while staying engaged with the lecture. A survey and interviews with university students revealed common note-taking behaviors and challenges to inform the design. We present MaRginalia to provide digital note-taking with a stylus tablet and MR headset. Students can take notes with an MR representation of the tablet, lecture slides, and audio transcript without looking down at their device. When preferred, students can also perform detailed interactions by looking at the physical tablet. We demonstrate the feasibility and usefulness of MaRginalia and MR-based note-taking in a user study with 12 students.

This study examines how anecdotal stories from friends, peers, and online sources influence non-experts' perceptions and behaviors toward smart home IoT devices.We surveyed 263 participants, collecting narratives that either positively or negatively influenced their perception of IoT devices, which they retold in text and comic formats to encourage deeper reflection.Thematic analysis of the narratives, combined with quantitative survey data, reveals that stories significantly impact trust and willingness to use and adopt IoT devices.Negative stories, particularly those concerning security, privacy, and device unreliability, reduced trust and usage, while positive stories about home safety through monitoring and improved quality of life increased interest in IoT devices.Perceptions of different IoT devices varied based on the themes associated with the stories.The findings highlight the powerful role of storytelling in driving consumer acceptance of technology. CCS Concepts• Human-centered computing → Empirical studies in HCI; • Security and privacy → Social aspects of security and privacy.

Thanks to their generative capabilities, large language models (LLMs) have become an invaluable tool for creative processes. These models have the capacity to produce hundreds and thousands of visual and textual outputs, offering abundant inspiration for creative endeavors. But are we harnessing their full potential? We argue that current interaction paradigms fall short, guiding users towards rapid convergence on a limited set of ideas, rather than empowering them to explore the vast latent design space in generative models. To address this limitation, we propose a framework that facilitates the structured generation of design space in which users can seamlessly explore, evaluate, and synthesize a multitude of responses. We demonstrate the feasibility and usefulness of this framework through the design and development of an interactive system, Luminate, and a user study with 14 professional writers. Our work advances how we interact with LLMs for creative tasks, introducing a way to harness the creative potential of LLMs.

This paper adopted an iterative design process to gain insights into programmers’ strategies when using LLMs for programming. We proposed CoLadder, a novel system that supports programmers by facilitating hierarchical task decomposition, direct code segment manipulation, and result evaluation during prompt authoring. A user study with 12 experienced programmers showed that CoLadder is effective in helping programmers externalize their problem-solving intentions flexibly, improving their ability to evaluate and modify code across various abstraction levels, from their task’s goal to final code implementation.

Science 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.

The ability to reason about the general purpose and behavior of code, rather than simply focusing on specific inputs and outputs, is an essential skill to teach to novice programmers. However, effectively teaching this skill can be a challenging task. In light of recent research on using concreteness fading and comics for teaching programming, we aim to develop a tool that employs a gradual transition from concrete to abstract code representations and investigate whether it enhances students’ ability to recognize patterns and foster the development of associated programming skills, such as code tracing and writing.

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.

In this Birds of a Feather (BoF) session, we aim to create and foster a safe and open environment that allows graduate students share experiences and issues with graduate studies in CSEd research. We invite genuine and honest conversations about the challenges facing rising academics and professionals in this area. We have three goals: (1) to foster a community of support among CSEd grad students from around the world, (2) to connect students with new opportunities and ways of thinking about CSEd, and (3) to cultivate mentorship among participants and send them off with potential collaborators, supporters, and advocates.

Comics is emerging as a popular, promising medium for teaching programming. Recent research on coding strip, a form of comic strip that has a direct correspondence to its corresponding code, led to a novel visual programming environment that generates comics from code and an experience report demonstrating various ways coding strips can be used to benefit students' learning and their experiences. However, how teachers can leverage these findings and tools has not yet been documented in a concise, accessible format to facilitate their adoption. To fill this gap, we developed a quick reference guide to summarize comic-based pedagogical techniques and tools teachers can use to augment their teaching. To develop this guide, we analyzed 26 coding strips, 30 design patterns, and their use cases. Based on this analysis, we delineated how comics can facilitate the teaching of programming concepts, languages, and procedures. To evaluate our reference guide, we presented it to seven high school CS teachers at an annual conference for computer studies educators and asked them to rate its readability, usefulness, organization, and their interest in using it for their teaching. Our analysis suggests this reference guide is easy to read/understand, useful, well-structured, and interests teachers to further explore how they can incorporate comics into their teaching.

People are increasingly turning to large language models (LLMs) for complex information tasks like academic research or planning a move to another city. However, while they often require working in a nonlinear manner — e.g., to arrange information spatially to organize and make sense of it, current interfaces for interacting with LLMs are generally linear to support conversational interaction. To address this limitation and explore how we can support LLM-powered exploration and sensemaking, we developed Sensecape, an interactive system designed to support complex information tasks with an LLM by enabling users to (1) manage the complexity of information through multilevel abstraction and (2) switch seamlessly between foraging and sensemaking. Our within-subject user study reveals that Sensecape empowers users to explore more topics and structure their knowledge hierarchically, thanks to the externalization of levels of abstraction. We contribute implications for LLM-based workflows and interfaces for information tasks.

With privacy-related concepts often abstract and difficult to define, comics can be an effective visual storytelling medium for explaining and raising awareness about privacy. However, existing privacy and security educational comics do not support content creation. To address this, we contribute PrivacyToon, a comic-based authoring tool that leverages concept-driven storytelling and ideation cards to help users create customizable privacy-related visual content. Our exploratory user study with 23 students and teachers shows PrivacyToon’s potential as a creative tool for communicating privacy concepts and stories. Our results show that a wide range of creativity preferences and contexts must be considered when designing systems that integrate ideation card-based design processes.

Visual programming environments such as Scratch and Alice contributed to lowering the barrier to computer programming. They use virtual characters that users can manipulate with code, enabling creative activities, such as creating animations and interactive games and stories. We introduce CodeToon, a new visual programming environment that uses comics to visualize code. In the environment, users can generate stories from code and produce comics. We showcase the environment, the authoring process, how teachers and students can use it to teach and learn programming, and the benefits of using comics as the visual representation. Expected Audience: Instructors at various levels (K-12 to undergraduate) interested in improving their programming courses with comics; researchers interested in the coding strip research.

Recent work demonstrated how we can design and use coding strips, a form of comic strips with corresponding code, to enhance teaching and learning in programming. However, creating coding strips is a creative, time-consuming process. Creators have to generate stories from code (code↦story) and design comics from stories (story↦comic). We contribute CodeToon, a comic authoring tool that facilitates this code-driven storytelling process with two mechanisms: (1) story ideation from code using metaphor and (2) automatic comic generation from the story. We conducted a two-part user study that evaluates the tool and the comics generated by participants to test whether CodeToon facilitates the authoring process and helps generate quality comics. Our results show that CodeToon helps users create accurate, informative, and useful coding strips in a significantly shorter time. Overall, this work contributes methods and design guidelines for code-driven storytelling and opens up opportunities for using art to support computer science education.

One of the main challenges with teaching and learning programming is the abstract nature of its concepts and procedures. To address this, recent work suggested using comics. Specifically, they proposed coding strip, a form of comic strip accompanied by its corresponding code, and demonstrated how they can be designed and used in classrooms. Their subsequent in-class study revealed that students enjoy learning with coding strips and benefit from them in various ways. Unfortunately, creating comics can be a time-consuming and challenging task; this can compromise the usefulness and adoption of coding strips. Thus in this demo, we introduce CodingToon, an authoring tool for coding strips, and provide a brief tutorial to help interested instructors use coding strips in their classrooms. The authoring process shown in the demo follows a concept-driven storytelling process that can be extended to support the creation of explanations on concepts beyond programming concepts. A laptop or tablet is recommended but not required.

Recent work investigated the potential of comics to support the teaching and learning of programming concepts and suggested several ways coding strips, a form of comic strip with its corresponding code, can be used. Building on this work, we tested the recommended use cases of coding strip in an undergraduate introductory computer science course at a large comprehensive university. At the end of the course, we surveyed students to assess their experience and found they benefited in various ways. Our work contributes a demonstration of the various ways comics can be used in introductory CS courses and an initial understanding of benefits and challenges with using comics in computing education gleaned from an analysis of students' survey responses and code submissions.

Over the years, concreteness fading has been used to design learning materials and educational tools for children. Unfortunately, it remains an underspecified technique without a clear guideline on how to design it, resulting in varying forms of concreteness fading and conflicting results due to the design inconsistencies. To our knowledge, no research has analyzed the existing designs of concreteness fading implemented across different settings, formulated a generic framework, or explained the design dimensions of the technique. This poses several problems for future research, such as lack of a shared vocabulary for reference and comparison, as well as barriers to researchers interested in learning and using this technique. Thus, to inform and support future research, we conducted a systematic literature review and contribute: (1) an overview of the technique, (2) a discussion of various design dimensions and challenges, and (3) a synthesis of key findings about each dimension. We open source our dataset to invite other researchers to contribute to the corpus, supporting future research and discussion on concreteness fading.

How can we express programming concepts in a more accessible form and manner? To address this question, my research explores ways to design, create, and use coding strip, a form of comic strip that offers corresponding code for learners to understand programming concepts in both concrete and abstract context. The motivation that drives this research is my belief that the key to efficient and effective learning lies in enabling dynamic interplay between high-level and low-level abstractions. Coding strip is proposed as the first step towards the goal of understanding how to design, create, and use tools that support such interplay.

The abstract nature of programming makes learning to code a daunting undertaking for many novice learners. In this work, we advocate the use of comics-a medium capable of presenting abstract ideas in a concrete, familiar way-for introducing programming concepts. Particularly, we propose a design process and related tools to help students and teachers create coding strips, a form of comic strips that are associated with a piece of code. We conducted two design workshops with students and high school computer science teachers to evaluate our design process and tools. We find that our design process and tools are effective at supporting the design of coding strips and that both students and teachers are excited about using coding strip as a tool for learning and teaching programming concepts.

Concreteness fading is a technique for teaching abstract concepts, where a given concept is re-introduced in three stages with decreasing levels of concreteness. Over the years, its effectiveness has been empirically and theoretically supported in mathematics and science education, encouraging the recent adoption of the technique in computing education research. My research aims to advance our understanding of this technique and use it to support learning in computing education. The motivation that drives this research is my belief that the concreteness fading approach can have a significant impact on how we model and design learning process in computing education, and broad implications for how we design learning interfaces and systems.

Nonnegative matrix factorization (NMF) has been increasingly popular for topic modeling of large-scale documents. However, the resulting topics often represent only general, thus redundant information about the data rather than minor, but potentially meaningful information to users. To tackle this problem, we propose a novel ensemble model of nonnegative matrix factorization for discovering high-quality local topics. Our method leverages the idea of an ensemble model to successively perform NMF given a residual matrix obtained from previous stages and generates a sequence of topic sets. The novelty of our method lies in the fact that it utilizes the residual matrix inspired by a state-of-the-art gradient boosting model and applies a sophisticated local weighting scheme on the given matrix to enhance the locality of topics, which in turn delivers high-quality, focused topics of interest to users.

Nonnegative matrix factorization (NMF) has been widely applied in many domains. In document analysis, it has been increasingly used in topic modeling applications, where a set of underlying topics are revealed by a low-rank factor matrix from NMF. However, it is often the case that the resulting topics give only general topic information in the data, which tends not to convey much information. To tackle this problem, we propose a novel ensemble model of nonnegative matrix factorization for discovering high-quality local topics. Our method leverages the idea of an ensemble model, which has been successful in supervised learning, into an unsupervised topic modeling context. That is, our model successively performs NMF given a residual matrix obtained from previous stages and generates a sequence of topic sets. Our algorithm for updating the input matrix has novelty in two aspects. The first lies in utilizing the residual matrix inspired by a state-of-the-art gradient boosting model, and the second stems from applying a sophisticated local weighting scheme on the given matrix to enhance the locality of topics, which in turn delivers high-quality, focused topics of interest to users. We evaluate our proposed method by comparing it against other topic modeling methods, such as a few variants of NMF and latent Dirichlet allocation, in terms of various evaluation measures representing topic coherence, diversity, coverage, computing time, and so on. We also present qualitative evaluation on the topics discovered by our method using several real-world data sets.