Many generative AI systems as well as decision-support systems (DSSs) provide operators with predictions or recommendations. Various studies show, however, that people can mistakenly adopt the erroneous results presented by those systems. Hence, it is crucial to promote critical thinking and reflection during interaction. One approach we are focusing on involves encouraging reflection during machine-assisted decision-making by presenting decision-makers with data-driven questions. In this short paper, we provide a brief overview of our work in that regard, namely: 1) the development of a question taxonomy, 2) the development of a prototype in the medical domain and the feedback received from clinicians, 3) a method for generating questions using a large language model, and 4) a proposed scale for measuring cognitive engagement in human-AI decision-making. In doing so, we contribute to the discussion about the design, development, and evaluation of tools for thought, i.e., AI systems that provoke critical thinking and enable novel ways of sense-making.

翻译：许多生成式人工智能系统以及决策支持系统(DSSs)为操作人员提供预测或建议。然而，多项研究表明，人们可能会错误地采纳这些系统呈现的错误结果。因此，在交互过程中促进批判性思维和反思至关重要。我们关注的一种方法是通过向决策者呈现数据驱动的问题来鼓励其在机器辅助决策过程中的反思。在这篇短文中，我们简要概述了我们在该领域的工作，具体包括：1) 问题分类体系的构建，2) 医学领域原型的开发及临床医生的反馈，3) 利用大语言模型生成问题的方法，以及4) 衡量人机协同决策中认知参与度的评估量表。通过上述工作，我们为思考工具（即能够激发批判性思维并实现新型意义建构过程的人工智能系统）的设计、开发与评估相关讨论做出了贡献。