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HCC:Small:Mind Perception in AI Companionship:Testing the Assumptions of Social Theories

项目来源

美国国家科学基金(NSF)

项目主持人

Jaime Banks

项目受资助机构

Syracuse University

项目编号

2401591

立项年度

2025

立项时间

未公开

项目级别

国家级

研究期限

未知 / 未知

受资助金额

596868.00美元

学科

未公开

学科代码

未公开

基金类别

Standard Grant

关键词

HCC-Human-Centered Computing ; SMALL PROJECT ; Cyber-Human Systems

参与者

Caleb T Carr

参与机构

SYRACUSE UNIVERSITY

项目标书摘要：ificial intelligence(AI)is increasingly a part of everyday life for functional purposes(like interpreting x-rays or recommending entertainment),and also for companionship(like chatting or even just sitting together).Companionship is a positive state of close connection with someone or something that unfolds over time and is valued for itself.Companionship is important to human life because it enhances well-being---for instance through reduced loneliness,enhanced emotional resilience,and finding elevated meaning in life.Evidence shows that people can see AI agents as mindful entities,and scientists and technologists often assume that creating more human-like,seemingly mindful AI,is necessary to foster companionship benefits.However,there are no scientific studies demonstrating that seeing a machine as a“someone”actually enables or enhances companionship benefits.Without a full understanding of the link between machine-mind perception and companionship outcomes,we may be developing and using technologies that carry unnecessary risks to privacy and may even diminish well-being.This project determines how to best measure the notion of mind perception,companionship,and well-being in human-AI relations.A series of studies then assesses the assumed link between perceiving AI systems as mindful entities and their efficacy as companions across different kinds of AI applications.By answering the fundamental question of whether mind perception plays a role in AI-companionship benefits,the work will ultimately help technologists make better decisions about AI design,public health officials make better decisions about AI policies,and everyday users make better decisions about whether and how they want to interact with AI companions.To accomplish the desired outcomes,the project pursues three objectives:1)Develop and validate measurements for AI mind perception,companionship,and relational benefits;2)build a data-driven model of relationships between those variables;and 3)test the model in short-and long-term human-AI relations.Objective 1 will be achieved by analyzing public conversations about AI companions,generating and evaluating self-report measurement tools,validating existing measurements for use in human-AI contexts,and exploring behavioral indicators of mind perception,companionship,and well-being.Objective 2 will be achieved through studies designed to identify direct or indirect relationships between mind perception,companionship,and well-being—experiments test the causal influence of mind perception on companionship experiences and subjective well-being.Objective 3 will be achieved by longitudinally testing the identified causal effects(via real-time surveys of experience)over short-term and long-term companionship interactions.This work advances the science of social-psychological processes and AI companionship.It comes at a time when companionship,as a key component in fulfilled human life,is increasingly addressed by social AI.This project lays the evidential groundwork to determine whether and how current theories of human mind perception apply to AI companionship.The research advances understanding of whether or not we must see someone in the machine for them to meaningfully contribute to human well-being.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人员信息

Jaime Banks(Principal Investigator)：banks@syr.edu；Caleb T Carr(Former Co-Principal Investigator)：ctcarr@ilstu.edu；

机构信息

【Syracuse University(Performance Institution)】StreetAddress：900 S CROUSE AVE,SYRACUSE,New York,United States/ZipCode：132440001；【SYRACUSE UNIVERSITY】StreetAddress：900 S CROUSE AVE,SYRACUSE,New York,United States/PhoneNumber：3154432807/ZipCode：132444407；

项目主管部门

Directorate for Computer and Information Science and Engineering(CSE)-Division of Information&Intelligent Systems(IIS)

项目官员

Scott Robertson(Email：sroberts@nsf.gov；Phone：7032922971)

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  • 1.Measuring machine companionship experiences: Scale development and validation for AI companions

    • 关键词：
    • Measurement; Human-machine communication; Artificial intelligence;Friendship; Romantic relationship; Utilitarian motives; Autotelicexperience;LONELINESS; DIMENSIONS; SELF
    • Banks, Jaime
    • 《COMPUTERS IN HUMAN BEHAVIOR》
    • 2026年
    • 179卷
    • 期刊

    The mainstreaming of companionable machines-customizable artificial agents designed to participate in ongoing, idiosyncratic, socioemotional relationships-is met with relative theoretical and empirical disarray, according to recent systematic reviews. In particular, the conceptualization and measurement of machine companionship (MC) is inconsistent or sometimes altogether missing. This study starts to bridge that gap by developing and initially validating a novel measurement to capture MC experiences-the unfolding, autotelic, positively experienced, coordinated connection between human and machine-with AI companions (AICs). After systematic generation and expert review of an item pool (including items pertaining to dyadism, coordination, autotelicity, temporality, and positive valence), N = 467 people interacting AICs responded to the item pool and to construct validation measures. Through exploratory factor analysis, two factors were induced: Eudaimonic Exchange and Connective Coordination. Construct validation analyses indicate the factors function largely as expected (and confirmed in a second sample; N = 249). Post-hoc analyses of deviations suggests two different templates for MC with AICs: One socioinstrumental and one autotelic.

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  • 2.Conceptualization, operationalization, and measurement of machine companionship: a scoping review

    • 关键词：
    • interpersonal processes; emotion; artificial intelligence; socialcognition; intimacy; anthropocentrism; companionship;AI; LONELINESS; FUTURE; TERM
    • Banks, Jaime;Li, Zhixin
    • 《JOURNAL OF COMPUTER-MEDIATED COMMUNICATION》
    • 2026年
    • 31卷
    • 2期
    • 期刊

    The notion of machine companions has long been embedded in socio-technological imaginaries. Recent advances in AI have moved those media musings into believable sociality manifested in interfaces, robotic bodies, and devices. Those machines are often referred to colloquially as "companions," yet there is little careful engagement of machine companionship (MC) as a formal concept or measured variable. This PRISMA-guided scoping review systematically samples, surveys, and synthesizes current scholarly works on MC (N = 71; 2017-2025). Works varied widely in considerations of MC according to guiding theories, dimensions of a priori specified properties (subjectively positive, sustained over time, co-active, autotelic), and in measured concepts (with more than 50 distinct measured variables). We ultimately offer a literature-guided definition of MC as an autotelic, coordinated connection between human and machine that unfolds over time and is subjectively positive; through a facet-theoretical lens, we suggest how this definition can scaffold future research.Social machines have become a part of everyday life. There are many forms of social machines, including social robots, videogame characters, voice assistants, and social AI. Because social machines can communicate, people sometimes feel they are friends, family, or romantic partners. In other words, they are machines that can be built and used for companionship. These companion technologies are becoming quite popular, but the science is limited in understanding how machine companionship happens, how people experience it, and whether it has positive or negative outcomes. To map out the current science of this situation, we systematically reviewed 71 articles to better understand how scholars are defining and sometimes measuring "companionship" with machines. We find there is not much consistency among all of these academic works, so the knowledge we have about machine companionship is not especially cohesive or reliable. That is, we don't have well-supported claims we can make about the topic. To address this inconsistency, we take what we learned from this investigation to offer a conceptual definition of machine companionship that should help scholars move forward in studying the phenomenon.

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