The rapid advancement of AI is transforming human-centered systems, with profound implications for human-AI interaction, human-data interaction, and visual analytics. In the AI era, data analysis increasingly involves large-scale, heterogeneous, and multimodal data that is predominantly unstructured, as well as foundation models such as LLMs and VLMs, which introduce additional uncertainty into analytical processes. These shifts expose persistent challenges for human-data interactive systems, including perceptually misaligned latency, scalability constraints, limitations of existing interaction and exploration paradigms, and growing uncertainty regarding the reliability and interpretability of AI-generated insights. Responding to these challenges requires moving beyond conventional efficiency and scalability metrics, redefining the roles of humans and machines in analytical workflows, and incorporating cognitive, perceptual, and design principles into every level of the human-data interaction stack. This paper investigates the challenges introduced by recent advances in AI and examines how these developments are reshaping the ways users engage with data, while outlining limitations and open research directions for building human-centered AI systems for interactive data analysis in the AI era.

翻译：人工智能的快速发展正在变革以人为中心的系统，对人-人工智能交互、人-数据交互及可视分析领域产生深远影响。在人工智能时代，数据分析日益涉及大规模、异构、多模态且主要为非结构化的数据，以及诸如LLMs和VLMs等基础模型，这些都给分析过程引入了额外的不确定性。这些转变揭示了人-数据交互系统面临的持续挑战，包括感知错位的延迟、可扩展性限制、现有交互与探索范式的局限性，以及对人工智能生成见解的可靠性与可解释性日益增长的不确定性。应对这些挑战需要超越传统的效率与可扩展性度量标准，重新定义人类与机器在分析工作流中的角色，并将认知、感知与设计原则融入人-数据交互栈的各个层面。本文探讨了人工智能最新进展所带来的挑战，审视了这些发展如何重塑用户与数据交互的方式，同时概述了在人工智能时代构建用于交互式数据分析的以人为中心的人工智能系统所面临的局限性与开放研究方向。