Computers in Human Behavior: Artificial Humans最新文献_第5页

Comparing the audience effect of anthropomorphic robots and humans in economic games 拟人机器人与真人在经济博弈中的受众效应比较

Computers in Human Behavior: Artificial Humans

Pub Date : 2025-10-06 DOI: 10.1016/j.chbah.2025.100215

Charlotte Stinkeste , Anna Dreber , Jonas Olofsson , Gabriel Skantze

Research in human–agent interaction increasingly examines how advancements in AI systems, often designed to mimic human-like traits, impact human behavior. This study specifically examines whether the audience effect, which refers to changes in behavior when people know they are being observed, could be used to measure the influence of AI agents on human decisions related to generosity, fairness, and honesty. A between-group study (N=289) was designed, where economic games (Dictator, Ultimatum, and Mind Games) were played in front of one of three agents: a Computer (minimum expected audience effect), an anthropomorphic Robot, or a Human (maximum expected audience effect). Our first objective was to determine if these games can be used to observe and study the audience effect, by comparing interactions with the Computer and Human agents. Our second aim was to assess whether an anthropomorphic social robot could impact decision-making by comparing interactions with the Robot and Computer agents. Results showed no differences in the Ultimatum and Mind Games when comparing the Human and Computer conditions. In contrast, donations to charities in the Dictator Game were more generous in the presence of a Human than a Computer, suggesting that this game can detect the audience effect of an agent. Importantly, however, no difference emerged between the Robot and Computer conditions, indicating that anthropomorphic design features alone are insufficient to trigger an audience effect. These findings identify the Dictator Game with charities as a method for studying social facilitation effects of an agent, while highlighting the limits of anthropomorphism.

人类与智能体交互的研究越来越多地探讨了人工智能系统的进步是如何影响人类行为的。人工智能系统通常被设计成模仿人类的特征。这项研究专门研究了观众效应，即人们知道自己被观察时的行为变化，是否可以用来衡量人工智能代理对人类慷慨、公平和诚实决策的影响。设计了一项组间研究（N=289），其中经济游戏（独裁者，最后通牒和心理游戏）在三种代理中的一种面前进行：计算机（最小预期受众效应），拟人化机器人或人类（最大预期受众效应）。我们的第一个目标是通过比较计算机和人类代理的互动，确定这些游戏是否可以用来观察和研究受众效应。我们的第二个目标是通过比较机器人和计算机代理的交互来评估拟人化社交机器人是否会影响决策。结果显示，在最后通牒和心理游戏时，比较人类和计算机的条件没有差异。相比之下，在“独裁者游戏”中，当有人类在场时，捐赠给慈善机构的钱比有电脑在场时更慷慨，这表明该游戏可以检测到代理人的受众效应。然而，重要的是，机器人和计算机条件之间没有出现差异，这表明拟人化设计特征本身不足以引发受众效应。这些发现确定了独裁者游戏与慈善机构作为研究代理人的社会促进效应的一种方法，同时强调了拟人化的局限性。

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引用次数: 0

The early wave of ChatGPT research: A review and future agenda ChatGPT研究的早期浪潮：回顾与未来议程

Computers in Human Behavior: Artificial Humans

Pub Date : 2025-10-04 DOI: 10.1016/j.chbah.2025.100213

Peter André Busch , Geir Inge Hausvik , Jeppe Agger Nielsen

Researchers and practitioners are increasingly engaged in discussions about the hopes and fears of artificial intelligence (AI). In this article, we critically examine the early scholarly response to one prominent form of generative and conversational AI: ChatGPT. The launch of ChatGPT has sparked a surge in research, resulting in a fast-growing but fragmented body of literature. Against this backdrop, we undertook a systematic literature review of 192 empirical articles about ChatGPT to examine, synthesize, and evaluate the foci and gaps in this early wave of research to capture the dominating and immediate scholarly reactions to ChatGPT's release. Our analytical focus covered the following main aspects: perspectives on the purpose, usage, attitudes, and impacts of ChatGPT, as well as the theories and methods scholars apply in studying ChatGPT. Most studies in our sample focus on performance tests of ChatGPT, highlighting its strengths in remembering, understanding, and analyzing content, while revealing limitations in its capacity to generate novel ideas and its hallucination habit. Although the initial wave of ChatGPT research has generated valuable first insights, much of this early research remains a-theoretical, descriptive, and narrowly scoped, with limited attention to broader social, ethical, and institutional implications. These patterns reflect both the rapid publication pace and the early stage of scholarly engagement with this emerging technology. In response, we propose a conceptual model that maps key focus areas of ChatGPT research and suggest ways of strengthening ChatGPT research by proposing a research agenda aimed at advancing more theoretically informed, contextually grounded, and socially responsive studies of generative and conversational AI.

研究人员和实践者越来越多地参与到关于人工智能（AI）的希望和恐惧的讨论中。在本文中，我们批判性地研究了早期学术界对一种突出的生成式和会话式人工智能的反应：ChatGPT。ChatGPT的推出引发了研究热潮，导致了一个快速增长但支离破碎的文献体系。在此背景下，我们对192篇关于ChatGPT的实证文章进行了系统的文献综述，以检查、综合和评估这一早期研究浪潮中的焦点和差距，以捕捉对ChatGPT发布的主要和直接的学术反应。我们的分析重点包括以下几个主要方面：对ChatGPT的目的、使用、态度和影响的看法，以及学者们研究ChatGPT的理论和方法。我们样本中的大多数研究都集中在ChatGPT的性能测试上，突出了它在记忆、理解和分析内容方面的优势，同时揭示了它在产生新想法和产生幻觉习惯方面的局限性。尽管ChatGPT研究的最初浪潮产生了有价值的初步见解，但这些早期研究的大部分仍然是理论性的、描述性的、范围狭窄的，对更广泛的社会、伦理和制度影响的关注有限。这些模式既反映了快速的出版速度，也反映了与这种新兴技术的早期学术接触。作为回应，我们提出了一个概念模型，该模型绘制了ChatGPT研究的关键重点领域，并提出了加强ChatGPT研究的方法，提出了一个研究议程，旨在推进生成式和会话式人工智能的更多理论依据、情境基础和社会响应性研究。

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引用次数: 0

Navigating the human-AI divide: Boundary work in the age of generative AI 跨越人类与人工智能的鸿沟：生成式人工智能时代的边界工作

Computers in Human Behavior: Artificial Humans

Pub Date : 2025-10-04 DOI: 10.1016/j.chbah.2025.100214

Young Ji Kim , Ceciley Xinyi Zhang , Chengyu Fang

Generative artificial intelligence (GenAI), such as ChatGPT, has recently attracted vast public attention for its remarkable ability to produce sophisticated, human-like content. As these technologies increasingly blur the boundaries between artificial and human intelligence, understanding how users perceive and manage this boundary becomes essential. Drawing on the concept of boundary work, this paper examines how GenAI users discursively and practically navigate the ontological boundaries between human intelligence and GenAI. Through a qualitative analysis of nine focus groups involving 45 college students from diverse academic backgrounds, this study identifies three types of human-GenAI boundaries: complementary, competitive, and co-evolving. Complementary boundaries highlight GenAI's supportive and instrumental role and competitive boundaries emphasize human superiority and concerns over GenAI's threats, while co-evolving boundaries acknowledge dynamic interplay and reflective collaboration between humans and GenAI. The paper contributes theoretically by demonstrating that human-machine boundaries are dynamic, multifaceted, and actively negotiated. Practically, it offers insights into user strategies and implications for responsible adoption of GenAI technologies in educational and organizational contexts.

像ChatGPT这样的生成式人工智能（GenAI）最近因其产生复杂的、类似人类的内容的卓越能力而引起了公众的广泛关注。随着这些技术日益模糊人工智能和人类智能之间的界限，了解用户如何感知和管理这一界限变得至关重要。利用边界工作的概念，本文研究了GenAI用户如何在人类智能和GenAI之间的本体论边界上进行论述和实际导航。通过对来自不同学术背景的45名大学生的9个焦点小组的定性分析，本研究确定了三种类型的人类-基因边界：互补、竞争和共同进化。互补边界强调GenAI的支持性和工具性作用，竞争边界强调人类的优势和对GenAI威胁的关注，而共同进化边界承认人类和GenAI之间的动态相互作用和反思性合作。本文从理论上证明了人机边界是动态的、多方面的、积极协商的。实际上，它为在教育和组织环境中负责任地采用GenAI技术的用户策略和含义提供了见解。

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引用次数: 0

Investigating choices regarding the accuracy-transparency trade-off of AI-based systems across contexts 调查关于基于人工智能的系统跨上下文的准确性和透明度权衡的选择

Computers in Human Behavior: Artificial Humans

Pub Date : 2025-10-04 DOI: 10.1016/j.chbah.2025.100216

Tim Hunsicker , Cornelius J. König , Markus Langer

Artificial intelligence (AI) is increasingly used in decision-making. However, choosing between different algorithmic methods underlying AI-based systems involves trade-offs. The accuracy-transparency trade-off is one of the most prominent: the most accurate approaches are often the least transparent, and the most transparent ones are the least accurate. This study examined how individuals navigate this trade-off from a deployer perspective. In an experimental between-participants online study (N = 468), we examined how framing (framing the system performance as accuracy rate vs. error rate), accountability (being able to justify a decision vs. no need to justify a decision), and the context of use (medicine, hiring, finance, law) affect choosing between different versions of systems underlying the accuracy-transparency trade-off. We also investigated whether the experimental manipulations and system choice affected trustworthiness and trust perceptions. Regarding the system choice (i.e., a preference for accuracy at the expense of transparency or a preference for transparency at the expense of accuracy), framing and accountability did not affect system choice. As expected, participants favored high performance in medicine compared to the other contexts. The results also supported the expected relationship between system choice and perceptions of different system trustworthiness facets, as well as framing effects on perceived trustworthiness and trust. We conclude that the context of use is critical for deployer preferences regarding system accuracy and transparency. Additionally, we identified person-related factors influencing such choices. Furthermore, a simple change in wording (i.e., without changing the system properties) can affect individuals’ perceived trustworthiness of AI-based systems.

人工智能（AI）越来越多地用于决策。然而，在基于人工智能系统的不同算法方法之间进行选择涉及权衡。准确性和透明度之间的权衡是最突出的问题之一：最准确的方法往往是最不透明的，而最透明的方法往往是最不准确的。本研究从部署人员的角度考察了个人如何处理这种权衡。在一项参与者之间的在线实验研究中（N = 468），我们检查了框架（将系统性能定义为准确率与错误率）、问责制（能够证明决策的合理性与不需要证明决策的合理性）和使用背景（医学、招聘、金融、法律）如何影响在不同版本的系统之间进行选择，这些系统是基于准确性与透明度之间的权衡。我们还研究了实验操作和系统选择是否影响可信度和信任感知。关于系统选择（即，以牺牲透明度为代价的准确性偏好或以牺牲准确性为代价的透明度偏好），框架和问责制并不影响系统选择。不出所料，与其他环境相比，参与者更喜欢在医学方面表现出色。结果还支持了制度选择与不同制度可信度感知之间的预期关系，以及感知可信度和信任的框架效应。我们得出结论，使用上下文对于部署者关于系统准确性和透明度的偏好至关重要。此外，我们确定了影响这些选择的个人相关因素。此外，措辞的简单改变（即不改变系统属性）会影响个人对基于ai的系统的感知可信度。

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引用次数: 0

Attractive synthetic voices 迷人的合成声音

Computers in Human Behavior: Artificial Humans

Pub Date : 2025-10-03 DOI: 10.1016/j.chbah.2025.100211

Camila Bruder , Pamela Breda , Pauline Larrouy-Maestri

With recent advances in Artificial Intelligence (AI), synthetic voices have become increasingly prevalent in our everyday soundscape. This study examined listeners’ perception of human and neural Text-To-Speech (TTS) voices. In an online experiment, 75 participants listened to different versions of a short utterance spoken by eight different voices (half human, half TTS), each presented in four expressed emotions (neutral, happy, sad, angry). For each stimulus, participants rated voice attractiveness and willingness to interact, and selected the perceived emotion from a forced-choice list. In a second part, participants were asked to classify each voice as human or AI-generated. Results revealed that participants were often “fooled” by the TTS voices, misidentifying them as human. Voice ratings were influenced by the perceived emotion regardless of the voice type, with happy-sounding voices rated more positively than those perceived as sad or angry. However, TTS voices were rated as less attractive and socially appealing overall, though with large individual differences. These findings indicate that TTS voices are approaching human ones in how they are perceived by listeners, highlighting progress in their naturalness.

随着人工智能（AI）的发展，合成声音在我们的日常音景中变得越来越普遍。这项研究考察了听者对人类和神经文本到语音（TTS）声音的感知。在一项在线实验中，75名参与者听了8个不同的声音（一半是人类，一半是TTS）说的不同版本的简短话语，每个声音都有四种表达的情绪（中性、快乐、悲伤、愤怒）。对于每个刺激，参与者对声音的吸引力和互动意愿进行评分，并从强制选择列表中选择感知到的情绪。在第二部分中，参与者被要求将每个声音分类为人类或人工智能生成的。结果显示，参与者经常被TTS的声音“愚弄”，误以为他们是人。无论声音类型如何，声音评分都会受到感知到的情绪的影响，听起来快乐的声音比听起来悲伤或愤怒的声音更积极。然而，TTS的声音总体上被评为不那么有吸引力和社会吸引力，尽管存在很大的个体差异。这些发现表明，TTS的声音在听者的感知上正在接近人类的声音，突出了其自然性的进步。

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引用次数: 0

Harnessing large language models for identification and treatment of obsessive-compulsive disorder 利用大型语言模型来识别和治疗强迫症

Computers in Human Behavior: Artificial Humans

Pub Date : 2025-10-02 DOI: 10.1016/j.chbah.2025.100212

Inbar Levkovich

Obsessive-Compulsive Disorder (OCD) is a mental health condition characterized by recurrent intrusive thoughts and repetitive behaviors, causing significant distress and disruption to daily life. Early identification and intervention are crucial for improving outcomes. This research compared the performance of four AI models (ChatGPT-3.5, ChatGPT-4, Claude 3.5 Sonnet, and Gemini 1.5 Pro) to that of human mental health professionals in recognizing OCD, recommending evidence-based therapies, and assessing stigma attributions. Using six vignettes, the study analyzed 480 AI evaluations and 315 assessments by mental health professionals. The AI models demonstrated superior performance in OCD recognition (100 % vs. 87 % accuracy for humans) and in recommending evidence-based interventions (60–100 % vs. 61.9 % for humans). Additionally, the AI models showed lower stigma and danger estimations. These findings indicating that AI demonstrated higher accuracy in vignette-based recognition tasks highlight the potential for AI integration into mental health care, particularly for enhancing OCD diagnosis and treatment.

强迫症（OCD）是一种心理健康状况，其特征是反复出现侵入性思想和重复行为，导致严重的痛苦和日常生活中断。早期识别和干预对改善结果至关重要。本研究比较了四种人工智能模型（ChatGPT-3.5、ChatGPT-4、Claude 3.5 Sonnet和Gemini 1.5 Pro）在识别强迫症、推荐循证治疗和评估耻辱归因方面的表现与人类心理健康专业人员的表现。该研究使用6个小插曲分析了480项人工智能评估和315项心理健康专业人员的评估。人工智能模型在强迫症识别方面表现优异（人类的准确率为100%，而人类为87%），在推荐基于证据的干预措施方面表现优异（60 - 100%，而人类为61.9%）。此外，人工智能模型显示出较低的污名和危险估计。这些研究结果表明，人工智能在基于图像的识别任务中表现出更高的准确性，这凸显了人工智能融入精神卫生保健的潜力，特别是在增强强迫症诊断和治疗方面。

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引用次数: 0

Let robots tell stories: Using social robots as storytellers to promote language learning among young children 让机器人讲故事：利用社交机器人讲故事，促进幼儿的语言学习

Computers in Human Behavior: Artificial Humans

Pub Date : 2025-09-23 DOI: 10.1016/j.chbah.2025.100210

Zhaoji Wang , Tammy Sheung-Ting Law , Susanna Siu Sze Yeung

Robot-Assisted Language Learning (RALL) has emerged as an innovative method to support children's language development. However, limited research has examined how its effectiveness is compared to other digital and human-led storytelling approaches, particularly among young learners. This study involved 81 children (M _age = 5.58), who were randomly assigned to one of three storyteller conditions: a researcher-developed social robot (Joey), a tablet, or a human instructor. The study examined outcomes across three domains: linguistic (expressive vocabulary, story comprehension), cognitive (attention), and affective (perceptions of the storytelling activity). Results showed that children in robot condition demonstrated better story comprehension and reported significantly more positive speaking and reading perceptions than those in the tablet group. For attention, both the robot group maintained significantly higher levels than the human and tablet groups. However, for expressive vocabulary, no significant groups differences were identified. These findings suggest that while social robots may not be able to fully replace human instructors, they offer prominent benefits in certain aspects of language learning and may serve as a potential tool in early childhood educational settings.

机器人辅助语言学习（RALL）是一种支持儿童语言发展的创新方法。然而，有限的研究调查了它与其他数字和人为主导的讲故事方法的有效性，特别是在年轻学习者中。这项研究涉及81名儿童（年龄5.58岁），他们被随机分配到三种讲故事的环境中：研究人员开发的社交机器人（乔伊）、平板电脑或真人讲师。该研究考察了三个领域的结果：语言（表达词汇、故事理解）、认知（注意力）和情感（对讲故事活动的感知）。结果表明，机器人组的孩子比平板组的孩子表现出更好的故事理解能力，并报告了更积极的说话和阅读观念。在注意力方面，机器人组的水平都明显高于人类组和平板电脑组。然而，在表达性词汇方面，没有发现显著的组间差异。这些发现表明，虽然社交机器人可能无法完全取代人类教师，但它们在语言学习的某些方面提供了显著的好处，并且可能作为早期儿童教育环境的潜在工具。

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引用次数: 0

Artistic turing test: The challenge of differentiating human and AI-generated art 艺术图灵测试：区分人类艺术和人工智能艺术的挑战

Computers in Human Behavior: Artificial Humans

Pub Date : 2025-09-18 DOI: 10.1016/j.chbah.2025.100209

Costanza Cenerini, Flavio Keller, Giorgio Pennazza, Marco Santonico, Luca Vollero

This paper investigates the increasing overlap of artificial intelligence (AI) capabilities with human creativity, focusing on the production of art. We present a unique study in which AI algorithms were tasked with generating art from prompts derived from children's drawings. The participants, comprising both humans and AI, were presented with a test focused on discerning the origins of these art forms, distinguishing between those created by humans and AI. Intriguingly, human participants were unable to accurately distinguish between the two, whereas the AI exhibited a discerning ability, suggesting that AI can now generate art forms that are remarkably indistinguishable from human-made creations to the human eye, yet discernible by the AI itself. The implications of these findings are discussed with regard to the evolving boundaries between human and AI creativity.

本文研究了人工智能（AI）能力与人类创造力日益重叠的问题，重点是艺术的生产。我们提出了一项独特的研究，其中人工智能算法的任务是从儿童绘画中获得的提示生成艺术。参与者包括人类和人工智能，他们接受了一项测试，重点是辨别这些艺术形式的起源，区分人类和人工智能创造的艺术形式。有趣的是，人类参与者无法准确区分这两者，而人工智能却表现出了识别能力，这表明人工智能现在可以生成与人眼无法区分的艺术形式，但人工智能本身却可以分辨出来。这些发现的含义讨论了关于人类和人工智能创造力之间不断发展的界限。

引用次数: 0

Homogenizing effect of large language models (LLMs) on creative diversity: An empirical comparison of human and ChatGPT writing 大型语言模型（LLMs）对创造性多样性的均质化效应：人类和ChatGPT写作的实证比较

Computers in Human Behavior: Artificial Humans

Pub Date : 2025-09-15 DOI: 10.1016/j.chbah.2025.100207

Kibum Moon, Adam E. Green, Kostadin Kushlev

Generative AI systems, especially Large Language Models (LLMs) such as ChatGPT, have recently emerged as significant contributors to creative processes. While LLMs can produce creative content that might be as good as or even better than human-created content, their widespread use risks reducing creative diversity across groups of people. In the present research, we aimed to quantify this homogenizing effect of LLMs on creative diversity, not only at the individual level but also at the collective level. Across three preregistered studies, we analyzed 2,200 college admissions essays. Using a novel measure—the diversity growth rate—we showed that each additional human-written essay contributed more new ideas than did each additional GPT-4 essay. Notably, this difference became more pronounced as more essays were included in the analysis and persisted despite efforts to enhance AI-generated content through both prompt and parameter modifications. Overall, our findings suggest that, despite their potential to enhance individual creativity, the widespread use of LLMs could diminish the collective diversity of creative ideas.

生成式人工智能系统，尤其是像ChatGPT这样的大型语言模型（llm），最近已经成为创造性过程的重要贡献者。虽然法学硕士可以产生与人类创造的内容一样好的创造性内容，甚至比人类创造的内容更好，但它们的广泛使用可能会减少群体之间创造性的多样性。在目前的研究中，我们旨在量化法学硕士对创造性多样性的同质化效应，不仅在个人层面，而且在集体层面。在三项预先注册的研究中，我们分析了2200份大学入学申请文书。使用一种新颖的测量方法——多样性增长率——我们发现，每一篇额外的人工写作论文比每一篇额外的GPT-4论文贡献了更多的新想法。值得注意的是，随着更多的文章被纳入分析，这种差异变得更加明显，尽管通过提示和参数修改来增强人工智能生成的内容，这种差异仍然存在。总的来说，我们的研究结果表明，尽管法学硕士有增强个人创造力的潜力，但法学硕士的广泛使用可能会减少创造性思想的集体多样性。

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引用次数: 0

The role of AI in shaping educational experiences in computer science: A systematic review 人工智能在塑造计算机科学教育经验中的作用：系统回顾

Computers in Human Behavior: Artificial Humans

Pub Date : 2025-09-13 DOI: 10.1016/j.chbah.2025.100199

Anahita Golrang, Kshitij Sharma

The integration of artificial intelligence (AI) in computer science education (CSE) has earned significant attention due to its potential to enhance learning experiences and outcomes. This systematic literature review provides one of the first domain-specific and methodologically robust syntheses of AI applications in undergraduate CSE. Through a comprehensive analysis of 40 peer-reviewed studies, we offer a fine-grained categorization of course contexts, AI methods, and data types. Our findings reveal a predominant use of supervised learning, ensemble methods, and deep learning, with notable gaps in generative and explainable AI. The review highlights the post-pandemic increase in AI-driven programming education and the growing recognition of AI’s role in addressing educational challenges. This study offers technical and pedagogical insights that inform future research and practice at the intersection of AI and computer science education.

人工智能（AI）在计算机科学教育（CSE）中的整合由于其提高学习体验和成果的潜力而受到了极大的关注。这篇系统的文献综述提供了第一个特定领域和方法上强大的人工智能在本科CSE应用的综合。通过对40项同行评议研究的全面分析，我们提供了课程背景、人工智能方法和数据类型的细粒度分类。我们的研究结果揭示了监督学习、集成方法和深度学习的主要使用，在生成和可解释的人工智能方面存在显着差距。该审查报告强调，大流行后人工智能驱动的编程教育有所增加，人们日益认识到人工智能在应对教育挑战方面的作用。本研究为人工智能与计算机科学教育交叉领域的未来研究和实践提供了技术和教学见解。

引用次数: 0