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.

As demand for data scientists has increased to inform decision-making across multiple fields of societal importance, postsecondary institutions have expanded data science course offerings. Despite such growth, educators struggle to teach students all the skills central to data science. They focus on programming and statistical tools and lack time for mentoring students in data storytelling. This working paper reviewed literature and interviewed experts to model the domain knowledge of data storytelling to inform the design of intelligent technology to support data storytelling instruction at scale. The paper closes with a recommendation of two ways that artificial intelligence tools can support the development of students' data storytelling knowledge and skills: ''direct'' feedback to students on routine data science tasks and ''facilitated'' summaries of students' data story progress to inform instructors' feedback. We intend to apply these insights to the design of intelligent coaching in an online platform to support the development of storytelling competency at scale.

The demand for data storytelling, or communication with data, has surged significantly in recent years. Despite its growing recognition, large-scale coaching support tools for students and instructors remain lacking. The field of information visualization has explored data visualization and storytelling. Still, this knowledge has yet to be integrated with learning science in classroom settings to enhance student learning. Post-secondary data science educators require specialized tools and guidance to teach data storytelling on a larger scale. This tutorial invites those interested in the pedagogy of data storytelling to explore the initial version of a new coaching tool, Story Studio, developed by our team. The tool's design is based on our understanding of the data storytelling process and the application of relevant learning science principles, incorporating insights from experts, educators, and students. During the tutorial, participants will engage in hands-on experiences with the tool and interact with the research and development team, providing valuable feedback to support the tool's iterative improvement. This session aims to bridge the gap between data storytelling research and educational practice, equipping educators with the necessary resources to effectively teach this critical skill set. This material is based upon work supported by the National Science Foundation under Grant No. 2302794 and 2302795.

Data storytelling is the skill to communicate data effectively and efficiently. Effective data storytelling goes beyond data visualization and focuses on explanation with clear rhetorical functions. It starts with a set of data insights collected from the data science workflow and involves iterative and interactive processes of filtering those insights into story slices, from which data stories can be created through ordering, organizing and narration. Data storytelling is an integral component of a well-rounded data science education, which complements foundational skills like quantitative reasoning and programming. Despite its significance, solid understanding of the theory and practice of developing data storytelling competency is lacking. Data storytelling is often perceived as a mythical process where quantitative information magically transforms into compelling narratives. Designing scalable coaching tools for data storytelling requires leveraging multidisciplinary expertise from learning science, computer science, data science, communication science, and human-centered design. In this workshop, we will share some initial findings and reflections from our interdisciplinary team searching for effective coaching methods and tools to support coaching data storytelling at scale. We will present results from literature reviews and expert interviews which will be packaged into a set of foundational tools such as mental model, cognitive processes and schema for story construction, assessment strategy, as well as preliminary ideas of tools to support data storytelling coaching. We hope to use this workshop to build a community of researchers and practitioners in coaching data storytelling in postsecondary formal and informal learning context.

A series of reports on Innovating Pedagogy were launched in 2012 to look at the trends that show how practitioners may engage in innovation in pedagogy. This paper looks at the latest set of trends, and highlights four 2015 trends that seem particularly rich for researchers to explore in the next five years.

AR-Mentor is a wearable real time Augmented Reality (AR) mentoring system that is configured to assist in maintenance and repair tasks of complex machinery, such as vehicles, appliances, and industrial machinery. The system combines a wearable Optical-See-Through (OST) display device with high precision 6-Degree-Of-Freedom (DOF) pose tracking and a virtual personal assistant (VPA) with natural language, verbal conversational interaction, providing guidance to the user in the form of visual, audio and locational cues. The system is designed to be heads-up and hands-free allowing the user to freely move about the maintenance or training environment and receive globally aligned and context aware visual and audio instructions (animations, symbolic icons, text, multimedia content, speech). The user can interact with the system, ask questions and get clarifications and specific guidance for the task at hand. A pilot application with AR-Mentor was successfully built to instruct a novice to perform an advanced 33-step maintenance task on a training vehicle. The initial live training tests demonstrate that AR-Mentor is able to help and serve as an assistant to an instructor, freeing him/her to cover more students and to focus on higher-order teaching.