Computer Science > Human-Computer Interaction
arXiv:2603.09020 (cs)
[Submitted on 9 Mar 2026]
Title:AI Phenomenology for Understanding Human-AI Experiences Across Eras
View a PDF of the paper titled AI Phenomenology for Understanding Human-AI Experiences Across Eras, by Bhada Yun and 4 other authors
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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.
| Comments: | |
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.09020 [cs.HC] |
| (or arXiv:2603.09020v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09020
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View a PDF of the paper titled AI Phenomenology for Understanding Human-AI Experiences Across Eras, by Bhada Yun and 4 other authors
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