AI Phenomenology for Understanding Human-AI Experiences Across Eras

• URL: http://arxiv.org/abs/2603.09020v1
• Date: Mon, 09 Mar 2026 23:26:46 GMT
• Title: AI Phenomenology for Understanding Human-AI Experiences Across Eras
• Abstract summary: We argue for AI phenomenology: a research stance that asks "How did it feel?" when interacting with AI systems.<n>We motivate AI phenomenology as a framework that captures how alignment is experienced, negotiated, and updated between users and AI systems.
• Score: 8.54750896635797
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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