Asynchronous data-driven explanations often fail because the content and presentation are not tailored to the target audience, and they provide limited opportunities for active audience engagement. We present a vision for Contextualized Dynamic Explanations (CODEX), an agentic approach to dynamically generating contextualized multi-modal information interfaces for effective data-driven explanations based on an evolving audience model and a predefined communication intent. The premise underlying CODEX is that it is impossible for communicators to anticipate the full range of interactive scenarios involving the target audience. This observation motivates a set of research challenges focused on developing autonomous agents capable of evaluating communication progress, making context-sensitive decisions, and producing effective information representations.

翻译：异步数据驱动型解释常常失败，因为其内容与呈现方式未针对目标受众进行定制化设计，且受众参与互动的机会极为有限。我们提出情境化动态解释（CODEX）的愿景——这是一种基于演进式受众模型和预设沟通意图，通过智能体方法动态生成情境化多模态信息界面，以实现高效数据驱动型解释的路径。CODEX的核心前提在于：传播者无法预判目标受众可能涉及的全部交互场景。这一观察催生了若干研究挑战，重点在于开发能够评估沟通进程、做出上下文敏感决策并生成有效信息表征的自主代理。