Theory of Mind (ToM) -- the ability to infer what others are thinking (e.g., intentions) from observable cues -- is traditionally considered fundamental to human social interactions. This has sparked growing efforts in building and benchmarking AI's ToM capability, yet little is known about how such capability could translate into the design and experience of everyday user-facing AI products and services. We conducted 13 co-design sessions with 26 U.S.-based AI practitioners to envision, reflect, and distill design recommendations for ToM-enabled everyday AI products and services that are both future-looking and grounded in the realities of AI design and development practices. Analysis revealed three interrelated design recommendations: ToM-enabled AI should 1) be situated in the social context that shape users' mental states, 2) be responsive to the dynamic nature of mental states, and 3) be attuned to subjective individual differences. We surface design tensions within each recommendation that reveal a broader gap between practitioners' envisioned futures of ToM-enabled AI and the realities of current AI design and development practices. These findings point toward the need to move beyond static, inference-driven approach to ToM and toward designing ToM as a pervasive capability that supports continuous human-AI interaction loops.

翻译：心智理论（Theory of Mind, ToM）——即通过可观察的线索推断他人想法（例如意图）的能力——传统上被认为是人类社会互动的基础。这激发了越来越多关于构建和评估人工智能心智理论能力的研究，然而，对于这种能力如何转化为日常面向用户的人工智能产品与服务的设计及体验，目前知之甚少。我们与26位美国人工智能从业者进行了13场协同设计会议，旨在构想、反思并提炼出既面向未来又扎根于人工智能设计与开发实践现实的、具备心智理论能力的日常人工智能产品与服务的相关设计建议。分析揭示了三条相互关联的设计建议：具备心智理论能力的人工智能应 1) 植根于塑造用户心理状态的社会情境中，2) 对心理状态的动态特性做出响应，以及 3) 适应主观的个体差异。我们在每条建议中都揭示了其内部存在的设计张力，这些张力反映了从业者对具备心智理论能力的人工智能的未来构想与当前人工智能设计及开发实践现实之间更广泛的差距。这些发现表明，我们需要超越静态的、基于推理的心智理论方法，转向将心智理论设计为一种支持持续人机交互循环的普适能力。