Data tables play a central role in scientific papers. However, their meaning is often co-constructed with surrounding text through narrative interplay, making comprehension cognitively demanding for readers. In this work, we explore how interfaces can better support this reading process. We conducted a formative study that revealed key characteristics of text-table narrative interplay, including linking mechanisms, multi-granularity alignments, and mention typologies, as well as a layered framework of readers’ intents. Informed by these insights, we present TableTale, an augmented reading interface that enriches text with data tables at multiple granularities, including paragraphs, sentences, and mentions. TableTale automatically constructs a document-level linking schema within the paper and progressively renders cascade visual cues on text and tables that unfold as readers move through the text. A within-subject study with 24 participants showed that TableTale reduced cognitive workload and improved reading efficiency, demonstrating its potential to enhance paper reading and inform future reading interface design.

Abstract Comprehending visualizations requires readers to interpret visual encoding and the underlying meanings actively. This poses challenges for visualization novices, particularly when interpreting distributional visualizations that depict statistical uncertainty. Advancements in LLM‐based conversational interfaces show promise in promoting visualization comprehension. However, they fail to provide contextual explanations at fine‐grained granularity, and chart readers are still required to mentally bridge visual information and textual explanations during conversations. Our formative study highlights the expectations for both lexical and visual feedback, as well as the importance of explicitly linking these two modalities throughout the conversation. The findings motivate the design of VizTA, a visualization teaching assistant that leverages the fusion of visual and lexical feedback to help readers better comprehend visualization. VizTA features a semantic‐aware conversational agent capable of explaining contextual information within visualizations and employs a visual‐lexical fusion design to facilitate chart‐centered conversation. A between‐subject study with 24 participants demonstrates the effectiveness of VizTA in supporting the understanding and reasoning tasks of distributional visualization across multiple scenarios.

Tour guidance in virtual museums encourages multi-modal interactions to boost user experiences, concerning engagement, immersion, and spatial awareness. Nevertheless, achieving the goal is challenging due to the complexity of comprehending diverse user needs and accommodating personalized user preferences. Informed by a formative study that characterizes guidance-seeking contexts, we establish a multi-modal interaction design framework for virtual tour guidance. We then design VirtuWander, a two-stage innovative system using domain-oriented large language models to transform user inquiries into diverse guidance-seeking contexts and facilitate multi-modal interactions. The feasibility and versatility of VirtuWander are demonstrated with virtual guiding examples that encompass various touring scenarios and cater to personalized preferences. We further evaluate VirtuWander through a user study within an immersive simulated museum. The results suggest that our system enhances engaging virtual tour experiences through personalized communication and knowledgeable assistance, indicating its potential for expanding into real-world scenarios.

Semantic typographic logos harmoniously blend typeface and imagery to represent semantic concepts while maintaining legibility. Conventional methods using spatial composition and shape substitution are hindered by the conflicting requirement for achieving seamless spatial fusion between geometrically dissimilar typefaces and semantics. While recent advances made AI generation of semantic typography possible, the end-to-end approaches exclude designer involvement and disregard personalized design. This paper presents TypeDance, an AI-assisted tool incorporating design rationales with the generative model for personalized semantic typographic logo design. It leverages combinable design priors extracted from uploaded image exemplars and supports type-imagery mapping at various structural granularity, achieving diverse aesthetic designs with flexible control. Additionally, we instantiate a comprehensive design workflow in TypeDance, including ideation, selection, generation, evaluation, and iteration. A two-task user evaluation, including imitation and creation, confirmed the usability of TypeDance in design across different usage scenarios.

Story maps have been widely utilized to provide a visual and spatial framework for storytelling. However, existing story map tools have limitations in creating diverse narrative structures and providing interactive options, and cannot effectively render maps for polar regions due to tile-based mapping constraints. In this paper, we propose a graphic-based approach to overcome these challenges and develop a workflow for creating story maps specifically designed for polar regions. A primary contribution is to provide heuristic strategies for story map design and explore the potential of story maps in visualizing and disseminating polar culture. We summarize the main design tasks involved in story map creation and introduce three map-based visual narrative strategies, i.e., attention cue, linkage of map and other visual elements, and cartographic interaction. Additionally, we delve into the importance of storyboard design, taking into account logic, time order, and map granularity. To demonstrate the effectiveness of our proposed story map design method, we create story map cases focusing on the exploration history of Antarctica. These cases showcase the diverse and interactive nature of the story maps created using our approach. We discuss the limitations and challenges in creating story maps and identify potential research opportunities from our study.