Data storytelling workflows ask learners to integrate analytical, design, and narrative skills, but instructors rarely have the capacity to provide detailed feedback at each step. Computational and AI-assisted storytelling offers opportunities to support student learning, but how feedback should be structured effectively remains unclear. To address this gap, we conducted a two-phase participatory design study. Through participant observations (N=8) and interviews (N=6), the first phase explored learners and educators’ feedback needs and challenges in a data storytelling course. The second phase conducted two design workshops (N=8/10) to design and evaluate feedback strategies (frequency, seamlessness, accountability) for Story Studio: an AI-assisted narrative storytelling application. Our findings show that participants perceived on-demand and process feedback modes as effective, but automatic and outcome feedback as slightly more persuasive. We discuss implications for designing AI-augmented storytelling systems that adapt their feedback modes to the diverse needs and expectations of students.

As data storytelling—communicating insights through data—becomes increasingly essential across disciplines, the need for scalable instructional support in this area has never been greater. While the fields of information visualization and narrative design offer rich foundations, their integration into data science education remains limited. Educators are often left without adequate tools or pedagogical frameworks to teach data storytelling at scale. To address this gap, we introduce Story Studio Plus, an AI-empowered coaching tool designed to support collaborative learning among students, educators, and intelligent agents. Developed through iterative co-design with teachers, students, and domain experts, Story Studio blends principles from learning sciences, human-computer interaction, and narrative visualization. The tool provides formative feedback, scaffolded prompts, and interactive examples tailored to students' developmental stages. Uniquely, it positions AI not as a replacement for human instruction but as a collaborative partner—enhancing teacher facilitation, supporting student agency, and fostering a co-creative classroom culture. In this tutorial, participants will engage hands-on with Story Studio's latest features and explore how human educators, learners, and AI systems can co-construct knowledge through data storytelling. Participants will also contribute feedback to shape future iterations of the tool. This session offers an applied lens on how AI can be meaningfully integrated into data science education. This project is partially supported by National Science Foundation grants 2302794 and 2302795. We would like to thank software development work by Emily Jackson and other members from the UA SAIL lab and the human-centered design work led by Jennifer Posada from University of Maryland, Baltimore County.

This study introduces an AI-powered data storytelling agent designed to support data science educators by automatically curating high-quality, real-world data stories. The system streamlines the discovery of relevant instructional examples for specific teaching activities, including assignments, quizzes, classroom discussions, and case studies, by utilizing automated classification and narrative analysis. Our prototype significantly reduces instructor preparation time while improving the diversity, quality, and pedagogical alignment of curated stories. This innovation enables educators to more efficiently source, annotate, and deploy impactful data narratives tailored to their teaching and research objectives.

Trail management is a complex, distributed endeavor that requires coordination among conservancies, government agencies, and local trail clubs to ensure visitor safety, environmental stewardship, and the protection of historical resources. Despite its importance, there has been limited systematic research on understanding or supporting the collaborative workflows that sustain this work. To help bridge this gap, we conducted interviews with 10 trail managers involved in or familiar with field operations across five major U.S. long-distance trails. A thematic analysis of the transcripts reveals that trail management spans a wide array of responsibilities, including volunteer coordination, resource allocation, hazard sense-making, and on-the-ground fieldwork. We identify institutional inefficiencies that surface during four distinct stages of the temporal collaborative sense-making process as managers interpret and act upon hiker-submitted reports of trail hazards. We conclude by outlining design implications for field reporting and decision-support systems that better align with the realities of volunteer-based, resource-constrained trail organizations, supporting more timely and coordinated action across distributed stakeholders.

Data storytelling is essential in data science education but often lacks structured guidance. While students learn visualization and modeling, existing AI tools primarily generate stories automatically rather than teaching narrative construction. Few tools integrate storytelling with Jupyter Notebooks, and those that do focus on code generation rather than user-driven storytelling. StoryStudio bridges this gap by integrating with JupyterHub, allowing users to export figures and code into an interactive storytelling interface. It supports figure organization, AI-assisted insight extraction, and structured narrative generation using seven storytelling patterns. Unlike automated tools, Story Studio emphasizes active learning, helping students craft and refine their own data narratives. This poster will showcase Story Studio's role in enhancing visual literacy and data communication in data science education.

Maintaining emotional connections and fostering meaningful communication among distant family members has long been challenging. Existing communication technologies, such as instant messaging, video-sharing, and social media enable quick exchanges but often lack mechanisms to initiate appropriate conversation topics and support in-depth emotional interactions. This study explores the use of digital diary-sharing in addressing these limitations. We conduct thematic analyses on diaries from a three-week study (N=22) using DailyBean, a diary app, to examine frequent patterns in users' sharing of outdoor experiences with distant family members. We identify five key mechanisms to support connections between distant family members: topic initiation, memory recall, shared moments, joint activities, and future planning. We also highlight frequent conversation topics that facilitate emotional engagement and reflection for distant family members. We conclude our study with design recommendations for effective diary-based family communication.

We describe a seven-year longitudinal study conducted in collaboration with an indigenous community in Kenya. We detail the process of conducting research with an oral community: the deliberate practice of understanding and collecting stories; working with inter-generational community to envision and design technologies that support their ways of storytelling and story preservation; and to influence the design of other technologies. We chronicle how we contended with translating oral stories with rich metaphors to new mediums, and the dimensions of trust we have established and continue to reinforce. We offer our griot-style methodology, informed by working with the community and retrofitting existing HCI approaches: as an example model of what has worked, and the dimensions of challenges at each stage of the research work. The griot-style methodology has prompted a reflection on how we approach research, and present opportunities for other HCI research and practice of handling community stories.

Measuring instructors' adoption of learning management system (LMS) tools is a critical first step in evaluating the efficacy of online teaching and learning at scale. Existing models for LMS adoption are often qualitative, learner-centered, and difficult to leverage towards institutional support. We propose depth-of-use (DOU): an intuitive measurement model for faculty's utilization of a university-wide LMS and their needs for institutional support. We hypothesis-test the relationship between DOU and course attributes like modality, participation, logistics, and outcomes. In a large-scale analysis of metadata from 30000+ courses offered at Virginia Tech over two years, we find that a pervasive need for scale, interoperability and ubiquitous access drives LMS adoption by university instructors. We then demonstrate how DOU can help faculty members identify the opportunity-cost of transition from legacy apps to LMS tools. We also describe how DOU can help instructional designers and IT organizational leadership evaluate the impact of their support allocation, faculty development and LMS evangelism initiatives.

Pokémon GO is a popular location-based mobile game that seeks to inspire players to be more active, socialize physically and virtually, and spend more time outside. With the onset of the COVID-19 pandemic, several game mechanics of Pokémon GO were changed to accommodate socially-distanced play. This research aims to understand the impacts of the pandemic and subsequent game adjustments on user perceptions of the game. We used an exploratory mixed-method approach, a machine learning technique (Latent Dirichlet Allocation) for topic modeling, and thematic analysis for qualitative coding of top-level Reddit comments to identify whether and how the social distancing approach changes the players' behaviors. The results demonstrate that players were less physically active, less eager to discover, and more interested in remote social practices. We discuss which players leverage social distancing changes and reflect on key game features that provide a better gaming experience in the age of remote play.

Trust in a recommendation system (RS) is often algorithmically incorporated using implicit or explicit feedback of user-perceived trustworthy social neighbors, and evaluated using user-reported trustworthiness of recommended items. However, real-life recommendation settings can feature group disparities in trust, power, and prerogatives. Our study examines a complementary view of trust which relies on the editorial power relationships and attitudes of all stakeholders in the RS application domain. We devise a simple, first-principles metric of editorial authority, i.e., user preferences for recommendation sourcing, veto power, and incorporating user feedback, such that one RS user group confers trust upon another by ceding or assigning editorial authority. In a mixed-methods study at Virginia Tech, we surveyed faculty, teaching assistants, and students about their preferences of editorial authority, and hypothesis-tested its relationship with trust in algorithms for a hypothetical ‘Suggested Readings’ RS. We discover that higher RS editorial authority assigned to students is linked to the relative trust the course staff allocates to RS algorithm and students. We also observe that course staff favors higher control for the RS algorithm in sourcing and updating the recommendations long-term. Using content analysis, we discuss frequent staff-recommended student editorial roles and highlight their frequent rationales, such as perceived expertise, scaling the learning environment, professional curriculum needs, and learner disengagement. We argue that our analyses highlight critical user preferences to help detect editorial power asymmetry and identify RS use-cases for supporting teaching and research.

Learning management system (LMS) tools are increasingly relevant to scaling computing pedagogies. Measuring their utilization and impact at scale, however, remains computationally expensive. We examine the problem of estimating the utilization of a department-wide LMS, and its impact on the design, management and outcomes of Computer Science courses. We introduce 'depth-of-use' (DOU): a first-principles, resource-specific metric of LMS utilization. We then hypothesis-test the relationship between DOU and course attributes like modality (course level, mode-of-delivery, third-party app use), participation (enrollment, viewership), logistics (teaching support, digital skills training) and outcomes (average GPA, DFW rate). Experiments on metadata from over 1300 Computer Science courses taught at Virginia Tech between 2015 and 2019 suggest that our framing of DOU helps identify resource-level preferences of micro-cohorts of courses, linked to their content, logistics and pedagogies. We discover that, across the Computer Science department at Virginia Tech, overall LMS use is consistently linked to favorable learning outcomes. We also discover that a complex interaction between the needs for scale, ubiquitous access and interoperability drives strong LMS utilization, with graduate and online-only courses faring highest in their aggregate use of LMS services. Finally, we describe two key applications of our analyses. One, we demonstrate how DOU can help CS faculty identify the relative impact of transition from legacy apps to LMS services. Two, we describe how DOU can help instructional designers evaluate and improve their design interventions.

University students routinely use the tools provided by online course ranking forums to share and discuss their satisfaction with the quality of instruction and content in a wide variety of courses. Student perception of the efficacy of pedagogies employed in a course is a reflection of a multitude of decisions by professors, instructional designers and university administrators. This complexity has motivated a large body of research on the utility, reliability and behavioral correlates of course rankings. There is, however, little investigation of the (potential) implicit student bias on these forums towards desirable course outcomes at the institution level. To that end, we examine the connection between course outcomes (student-reported GPA) and the overall ranking of the primary course instructor, as well as rating disparity by nature of course outcomes, based on data from two popular academic rating forums. Our experiments with ranking data about over ten thousand courses taught at Virginia Tech and its 25 SCHEV-approved peer institutions indicate that there is a discernible albeit complex bias towards course outcomes in the professor ratings registered by students.