Reading Users' Minds With Large Language Models: Mental Inference for Artificial Empathy in Design
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md_147_6_061401.pdf
Description
Published version
Size
886.89 KB
Format
Adobe PDF
Checksum (MD5)
dbf535704fe15959b104cb53691d4636
Author(s)
Zhu, Qihao
Chong, Leah
Yang, Maria
Luo, Jianxi
Date Issued
January 15, 2025
Journal
Journal of Mechanical Design
Publisher
ASME International
Citation
Zhu, Q., Chong, L., Yang, M., and Luo, J. (January 15, 2025). "Reading Users' Minds With Large Language Models: Mental Inference for Artificial Empathy in Design." ASME. J. Mech. Des. June 2025; 147(6): 061401.
Version
Final published version
Abstract
In human-centered design, developing a comprehensive and in-depth understanding of user experiences—empathic understanding—is paramount for designing products that truly meet human needs. Nevertheless, accurately comprehending the real underlying mental states of a large human population remains a significant challenge today. This difficulty mainly arises from the tradeoff between depth and scale of user experience research: gaining in-depth insights from a small group of users does not easily scale to a larger population, and vice versa. This paper investigates the use of large language models (LLMs) for performing mental inference tasks, specifically inferring users' underlying goals and fundamental psychological needs (FPNs). Baseline and benchmark datasets were collected from human users and designers to develop an empathic accuracy metric for measuring the mental inference performance of LLMs. The empathic accuracy of inferring goals and FPNs of different LLMs with varied zero-shot prompt engineering techniques are experimented against that of human designers. Experimental results suggest that LLMs can infer and understand the underlying goals and FPNs of users with performance comparable to that of human designers, suggesting a promising avenue for enhancing the scalability of empathic design approaches through the integration of advanced artificial intelligence technologies. This work has the potential to significantly augment the toolkit available to designers during human-centered design, enabling the development of both large-scale and in-depth understanding of users' experiences.
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1115/1.4067527
