Toward Artificial Empathy for Human-Centered Design: A Framework
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Abstract (arXiv)
In the early stages of the design process, designers explore opportunities by discovering unmet needs and developing innovative concepts as potential solutions. From a human-centered design perspective, designers must develop empathy with people to truly understand their needs. However, developing empathy is a complex and subjective process that relies heavily on the designer's empathic capability. Therefore, the development of empathic understanding is intuitive, and the discovery of underlying needs is often serendipitous. This paper aims to provide insights from artificial intelligence research to indicate the future direction of AI-driven human-centered design, taking into account the essential role of empathy. Specifically, we conduct an interdisciplinary investigation of research areas such as data-driven user studies, empathic understanding development, and artificial empathy. Based on this foundation, we discuss the role that artificial empathy can play in human-centered design and propose an artificial empathy framework for human-centered design. Building on the mechanisms behind empathy and insights from empathic design research, the framework aims to break down the rather complex and subjective concept of empathy into components and modules that can potentially be modeled computationally. Furthermore, we discuss the expected benefits of developing such systems and identify current research gaps to encourage future research efforts.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 20 of its 122 references that are in the map. First posted on arXiv 19 Mar 2023.
Builds on
20 of 122 references.
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8 more
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Built on by
Citing papers per year: 2024: 3; 2025: 2; 2026: 1.
6 papers.
- 2024Elicitron: An LLM Agent-Based Simulation Framework for Design Requirements Elicitationcited by 19
- 2025SENSE-7: Taxonomy and Dataset for Measuring User Perceptions of Empathy in Sustained Human-AI Conversationscited by 9
- 2024SynFER: Towards Boosting Facial Expression Recognition with Synthetic Datacited by 6
- 2024Reading Users' Minds from What They Say: An Investigation into LLM-based Empathic Mental Inferencecited by 3
- 2025Intelligent Design 4.0: Paradigm Evolution Toward the Agentic AI Eracited by 0
- 2026When AI Says "I have been in similar situations": Synthetic Lived Experience in Peer-Like Caregiver Supportcited by 0
- 2026When AI Says "I have been in similar situations": Synthetic Lived Experience in Peer-Like Caregiver Support16 Jun 2026
- 2025SENSE-7: Taxonomy and Dataset for Measuring User Perceptions of Empathy in Sustained Human-AI Conversations19 Sep 2025
- 2025Intelligent Design 4.0: Paradigm Evolution Toward the Agentic AI Era11 Jun 2025
- 2024SynFER: Towards Boosting Facial Expression Recognition with Synthetic Data13 Oct 2024
- 2024Elicitron: An LLM Agent-Based Simulation Framework for Design Requirements Elicitation4 Apr 2024
- 2024Reading Users' Minds from What They Say: An Investigation into LLM-based Empathic Mental Inference20 Mar 2024