There is no 'ordinary' when it comes to AI. The human-AI experience is extraordinarily complex and specific to each person, yet dominant measures such as usability scales and engagement metrics flatten away nuance. We argue for AI phenomenology: a research stance that asks "How did it feel?" beyond the standard questions of "How well did it perform?" when interacting with AI systems. AI phenomenology acts as a paradigm for bidirectional human-AI alignment as it foregrounds users' first-person perceptions and interpretations of AI systems over time. We motivate AI phenomenology as a framework that captures how alignment is experienced, negotiated, and updated between users and AI systems. Tracing a lineage from Husserl through postphenomenology to Actor-Network Theory, and grounding our argument in three studies-two longitudinal studies with "Day", an AI companion, and a multi-method study of agentic AI in software engineering-we contribute a set of replicable methodological toolkits for conducting AI phenomenology research: instruments for capturing lived experience across personal and professional contexts, three design concepts (translucent design, agency-aware value alignment, temporal co-evolution tracking), and a concrete research agenda. We offer this toolkit not as a new paradigm but as a practical scaffold that researchers can adapt as AI systems-and the humans who live alongside them-continue to co-evolve.

翻译：在人工智能领域，不存在所谓的“寻常”。人机交互体验具有非凡的复杂性且因人而异，然而主导性的评估方法（如可用性量表和参与度指标）往往消解了其中的细微差别。我们提出“AI现象学”这一研究立场：在评估AI系统时，除了“表现如何”这一标准问题，更应追问“感受如何”。AI现象学作为一种双向人机对齐范式，强调用户在历时性维度中对AI系统的第一人称感知与阐释。我们主张将AI现象学构建为一种理论框架，用以捕捉用户与AI系统之间如何体验、协商并更新对齐状态。通过追溯从胡塞尔到后现象学乃至行动者网络理论的思想谱系，并基于三项实证研究（包括两项针对AI伴侣“Day”的纵向研究，以及一项关于软件工程中具身AI的多方法研究），我们提出一套可复现的AI现象学研究方法工具包：涵盖个人与专业场景的体验捕捉工具、三项设计理念（半透明设计、能动性感知的价值对齐、时序协同演化追踪）以及具体的研究议程。本工具包并非旨在建立新范式，而是为研究者提供可适配的实践框架，以应对AI系统及其共生人类持续协同演化的未来图景。