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    Reading Users' Minds With Large Language Models: Mental Inference for Artificial Empathy in Design

    Source: Journal of Mechanical Design:;2025:;volume( 147 ):;issue: 006::page 61401-1
    Author:
    Zhu, Qihao
    ,
    Chong, Leah
    ,
    Yang, Maria
    ,
    Luo, Jianxi
    DOI: 10.1115/1.4067527
    Publisher: The American Society of Mechanical Engineers (ASME)
    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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      Reading Users' Minds With Large Language Models: Mental Inference for Artificial Empathy in Design

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    contributor authorZhu, Qihao
    contributor authorChong, Leah
    contributor authorYang, Maria
    contributor authorLuo, Jianxi
    date accessioned2025-04-21T10:33:11Z
    date available2025-04-21T10:33:11Z
    date copyright1/15/2025 12:00:00 AM
    date issued2025
    identifier issn1050-0472
    identifier othermd_147_6_061401.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4306427
    description abstractIn 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.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleReading Users' Minds With Large Language Models: Mental Inference for Artificial Empathy in Design
    typeJournal Paper
    journal volume147
    journal issue6
    journal titleJournal of Mechanical Design
    identifier doi10.1115/1.4067527
    journal fristpage61401-1
    journal lastpage61401-11
    page11
    treeJournal of Mechanical Design:;2025:;volume( 147 ):;issue: 006
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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