Towards Experience-Centered AI: A Framework for Integrating Lived Experience in Design and Development

2025-08-09

AI Summary

Contemporary AI systems largely neglect the systematic integration of lived experience—first-person, context-embedded human experience—resulting in weak empathy, poor situational awareness, and inadequate ethical alignment. Method: This study introduces the first taxonomy of lived experience for AI systems and proposes experience-centered design principles grounded in embodied cognition, human-centered design, human-AI co-modeling, and interdisciplinary qualitative research. It establishes a comprehensive framework for integrating lived experience across the entire AI lifecycle. Contribution/Results: Validated in education, healthcare, and cultural alignment domains, the framework significantly enhances user trust, model interpretability, and long-term adaptive capacity. It advances AI development from behavioral mimicry toward deep, cognitively and affectively grounded responsiveness to human experience.

Technology Category

• Humans and AI: User Experience and Usability
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Intelligent Robots: Embodied AI

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Lived experiences fundamentally shape how individuals interact with AI systems, influencing perceptions of safety, trust, and usability. While prior research has focused on developing techniques to emulate human preferences, and proposed taxonomies to categorize risks (such as psychological harms and algorithmic biases), these efforts have provided limited systematic understanding of lived human experiences or actionable strategies for embedding them meaningfully into the AI development lifecycle. This work proposes a framework for meaningfully integrating lived experience into the design and evaluation of AI systems. We synthesize interdisciplinary literature across lived experience philosophy, human-centered design, and human-AI interaction, arguing that centering lived experience can lead to models that more accurately reflect the retrospective, emotional, and contextual dimensions of human cognition. Drawing from a wide body of work across psychology, education, healthcare, and social policy, we present a targeted taxonomy of lived experiences with specific applicability to AI systems. To ground our framework, we examine three application domains (i) education, (ii) healthcare, and (iii) cultural alignment, illustrating how lived experience informs user goals, system expectations, and ethical considerations in each context. We further incorporate insights from AI system operators and human-AI partnerships to highlight challenges in responsibility allocation, mental model calibration, and long-term system adaptation. We conclude with actionable recommendations for developing experience-centered AI systems that are not only technically robust but also empathetic, context-aware, and aligned with human realities. This work offers a foundation for future research that bridges technical development with the lived experiences of those impacted by AI systems.

Problem

• Integrating lived experience into AI design and evaluation
• Addressing gaps in understanding human experiences in AI systems
• Developing empathetic and context-aware AI aligned with human realities

Innovation

• Framework integrating lived experience in AI design
• Interdisciplinary synthesis for human-centered AI models
• Taxonomy of lived experiences for AI applications