Effective real-time data presentation is essential in small-group interactive contexts, where discussions evolve dynamically and presenters must adapt visualizations to shifting audience interests. However, most existing interactive visualization systems rely on fixed mappings between user actions and visualization commands, limiting their ability to support richer operations such as changing visualization types, adjusting data transformations, or incorporating additional datasets on the fly during live presentations. This work-in-progress paper presents VisAider, an AI-assisted interactive data presentation prototype that continuously analyzes the live presentation context, including the available dataset, active visualization, ongoing conversation, and audience profile, to generate ranked suggestions for relevant visualization aids. Grounded in a formative study with experienced data analysts, we identified key challenges in adapting visual content in real time and distilled design considerations to guide system development. A prototype implementation demonstrates the feasibility of this approach in simulated scenarios, and preliminary testing highlights challenges in inferring appropriate data transformations, resolving ambiguous visualization tasks, and achieving low-latency responsiveness. Ongoing work focuses on addressing these limitations, integrating the system into presentation environments, and preparing a summative user study to evaluate usability and communicative impact.

翻译：在小组互动情境中，有效的实时数据演示至关重要，因为讨论动态演进，演示者必须根据观众兴趣的变化调整可视化呈现。然而，现有的大多数交互式可视化系统依赖于用户操作与可视化命令之间的固定映射，限制了其在现场演示过程中支持更丰富操作的能力，例如动态更改可视化类型、调整数据转换或整合额外数据集。这篇进行中的研究论文介绍了VisAider，一个AI辅助的交互式数据演示原型系统。该系统持续分析实时演示情境，包括可用数据集、当前可视化状态、进行中的对话及观众画像，从而生成相关可视化辅助工具的排序建议。基于对有经验数据分析师的初步研究，我们识别了实时调整可视化内容的关键挑战，并提炼出指导系统开发的设计考量。原型实现证明了该方法在模拟场景中的可行性，初步测试则突显了在推断合适数据转换、消解模糊可视化任务以及实现低延迟响应等方面面临的挑战。当前工作重点在于解决这些局限性，将系统集成到演示环境中，并准备开展总结性用户研究以评估其可用性和传播效果。