Pub Date : 2024-08-01DOI: 10.1016/j.chbah.2024.100100
Donggyu Kim, Zituo Wang
This study examines the differences between social media influencers and virtual influencers in influencer marketing, focusing on their impact on marketing effectiveness. Using a between-subjects experimental design, the research explores how human influencers (HIs), human-like virtual influencers (HVIs), and anime-like virtual influencers (AVIs) affect perceptions of authenticity, source credibility, and overall marketing effectiveness. The study evaluates these influencer types across both for-profit and not-for-profit messaging contexts to determine how message intent influences audience reactions. The findings reveal that HVIs can be as effective as human influencers, especially in not-for-profit messaging, where their authenticity and source credibility are higher. However, when the messaging shifts to for-profit motives, the advantage of HVIs diminishes, aligning more closely with AVIs, which consistently show lower effectiveness. The study highlights the critical role that both authenticity and source credibility play in mediating the relationship between the type of influencer and advertising effectiveness.
本研究探讨了影响者营销中社交媒体影响者和虚拟影响者之间的差异，重点关注他们对营销效果的影响。研究采用主体间实验设计，探讨人类影响者（HIs）、类人虚拟影响者（HVIs）和动漫虚拟影响者（AVIs）如何影响对真实性、来源可信度和整体营销效果的感知。研究评估了这些影响者类型在营利性和非营利性信息传播中的作用，以确定信息意图如何影响受众的反应。研究结果表明，HVI 与人类影响者一样有效，尤其是在非营利性信息中，其真实性和来源可信度更高。然而，当信息传播转向营利动机时，HVI 的优势就会减弱，与 AVI 更加接近，而 AVI 一直显示出较低的有效性。这项研究强调了真实性和来源可信度在影响者类型与广告效果之间的中介作用。
{"title":"Social media influencer vs. virtual influencer: The mediating role of source credibility and authenticity in advertising effectiveness within AI influencer marketing","authors":"Donggyu Kim, Zituo Wang","doi":"10.1016/j.chbah.2024.100100","DOIUrl":"10.1016/j.chbah.2024.100100","url":null,"abstract":"<div><div>This study examines the differences between social media influencers and virtual influencers in influencer marketing, focusing on their impact on marketing effectiveness. Using a between-subjects experimental design, the research explores how human influencers (HIs), human-like virtual influencers (HVIs), and anime-like virtual influencers (AVIs) affect perceptions of authenticity, source credibility, and overall marketing effectiveness. The study evaluates these influencer types across both for-profit and not-for-profit messaging contexts to determine how message intent influences audience reactions. The findings reveal that HVIs can be as effective as human influencers, especially in not-for-profit messaging, where their authenticity and source credibility are higher. However, when the messaging shifts to for-profit motives, the advantage of HVIs diminishes, aligning more closely with AVIs, which consistently show lower effectiveness. The study highlights the critical role that both authenticity and source credibility play in mediating the relationship between the type of influencer and advertising effectiveness.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"2 2","pages":"Article 100100"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.chbah.2024.100085
Sophia C. Steinhaeusser, Birgit Lugrin
Social robots as storytellers combine advantages of human storytellers – such as embodiment, gestures, and gaze – and audio books – large repertoire of voices, sound effects, and background music. However, research on adding non-speech sounds to robotic storytelling is yet in its infancy. The current series of four online studies investigates the influence of sound effects and background music in robotic storytelling on recipients’ storytelling experience and enjoyment, robot perception, and emotion induction across different story genres, i.e. horror, detective, romantic and humorous stories. Results indicate increased enjoyment for romantic stories and a trend for decreased fatigue for all genres when adding sound effects and background music to the robotic storytelling. Of the four genres examined, horror stories seem to benefit the most from the addition of non-speech sounds. Future research should provide guidelines for the selection of music and sound effects to improve the realization of non-speech sound-accompanied robotic storytelling. In conclusion, our ongoing research suggests that the integration of sound effects and background music holds promise for enhancing robotic storytelling, and our genre comparison provides first guidance of when to use them.
{"title":"Integrating sound effects and background music in Robotic storytelling – A series of online studies across different story genres","authors":"Sophia C. Steinhaeusser, Birgit Lugrin","doi":"10.1016/j.chbah.2024.100085","DOIUrl":"10.1016/j.chbah.2024.100085","url":null,"abstract":"<div><p>Social robots as storytellers combine advantages of human storytellers – such as embodiment, gestures, and gaze – and audio books – large repertoire of voices, sound effects, and background music. However, research on adding non-speech sounds to robotic storytelling is yet in its infancy. The current series of four online studies investigates the influence of sound effects and background music in robotic storytelling on recipients’ storytelling experience and enjoyment, robot perception, and emotion induction across different story genres, i.e. horror, detective, romantic and humorous stories. Results indicate increased enjoyment for romantic stories and a trend for decreased fatigue for all genres when adding sound effects and background music to the robotic storytelling. Of the four genres examined, horror stories seem to benefit the most from the addition of non-speech sounds. Future research should provide guidelines for the selection of music and sound effects to improve the realization of non-speech sound-accompanied robotic storytelling. In conclusion, our ongoing research suggests that the integration of sound effects and background music holds promise for enhancing robotic storytelling, and our genre comparison provides first guidance of when to use them.</p></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"2 2","pages":"Article 100085"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949882124000458/pdfft?md5=39926971bcbec336bf3117e22eb44704&pid=1-s2.0-S2949882124000458-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.chbah.2024.100097
Anja Bodenschatz
Autonomous systems (ASs) decide upon ethical dilemmas and their artificial intelligence as well as situational settings become more and more complex. However, to study common-sense morality concerning ASs abstracted dilemmas on autonomous vehicle (AV) accidents are a common tool. A special case of ethical dilemmas is when the AS’s users are affected. Many people want AVs to adhere to utilitarian programming (e.g., to save the larger group), or egalitarian programming (i.e., to treat every person equally). However, they want their own AV to protect them instead of the “greater good”. That people reject utilitarian programming as an AS’s user while supporting the idea from an impartial perspective has been termed the “social dilemma of AVs”. Meanwhile, preferences for another technical capability, which would implement egalitarian programming, have not been elicited for dilemmas involving self-interest: decision randomization. This paper investigates normative and descriptive preferences for a self-protective, self-sacrificial, or randomized choice by an AS in a dilemma where people are the sole passenger of an AV, and their survival stands against the survival of several others. Results suggest that randomization may mitigate the “social dilemma of AVs” by bridging between a societally accepted programming and the urge of ASs’ users for self-protection.
自主系统（AS）决定伦理困境，其人工智能和情境设置变得越来越复杂。然而，研究有关自动驾驶汽车（AV）事故的常识性道德抽象困境是一种常用工具。当自动驾驶汽车的用户受到影响时，就会出现特殊的道德困境。许多人希望自动驾驶汽车遵守功利主义程序（如拯救大群体）或平等主义程序（即平等对待每个人）。然而，他们希望自己的 AV 保护自己，而不是保护 "更大的利益"。人们拒绝作为自动机用户的功利主义编程，但又从公正的角度支持这一想法，这被称为 "自动机的社会困境"。与此同时，在涉及自身利益的困境中，人们对另一种可实现平等主义编程的技术能力--决策随机化--的偏好尚未被激发出来。本文研究了在人们是视听设备的唯一乘客，而他们的生存与其他几个人的生存息息相关的困境中，人们对自动驾驶汽车的自我保护、自我牺牲或随机选择的规范性和描述性偏好。结果表明，随机化可以在社会认可的程序和自动驾驶汽车用户自我保护的冲动之间架起一座桥梁，从而缓解 "自动驾驶汽车的社会困境"。
{"title":"When own interest stands against the “greater good” – Decision randomization in ethical dilemmas of autonomous systems that involve their user’s self-interest","authors":"Anja Bodenschatz","doi":"10.1016/j.chbah.2024.100097","DOIUrl":"10.1016/j.chbah.2024.100097","url":null,"abstract":"<div><div>Autonomous systems (ASs) decide upon ethical dilemmas and their artificial intelligence as well as situational settings become more and more complex. However, to study common-sense morality concerning ASs abstracted dilemmas on autonomous vehicle (AV) accidents are a common tool. A special case of ethical dilemmas is when the AS’s users are affected. Many people want AVs to adhere to utilitarian programming (e.g., to save the larger group), or egalitarian programming (i.e., to treat every person equally). However, they want their own AV to protect them instead of the “greater good”. That people reject utilitarian programming as an AS’s user while supporting the idea from an impartial perspective has been termed the “social dilemma of AVs”. Meanwhile, preferences for another technical capability, which would implement egalitarian programming, have not been elicited for dilemmas involving self-interest: decision randomization. This paper investigates normative and descriptive preferences for a self-protective, self-sacrificial, or randomized choice by an AS in a dilemma where people are the sole passenger of an AV, and their survival stands against the survival of several others. Results suggest that randomization may mitigate the “social dilemma of AVs” by bridging between a societally accepted programming and the urge of ASs’ users for self-protection.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"2 2","pages":"Article 100097"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.chbah.2024.100089
Tenzin Doleck, Pedram Agand, Dylan Pirrotta
Generative AI applications have increasingly gained visibility in recent educational literature. Yet less is known about how access to generative tools, such as ChatGPT, influences help-seeking during complex problem-solving. In this paper, we aim to advance the understanding of learners' use of a support strategy (hints) when solving data science programming tasks in an online AI-enabled learning environment. The study compared two conditions: students solving problems in DaTu with AI assistance (N = 45) and those without AI assistance (N = 44). Findings reveal no difference in hint-seeking behavior between the two groups, suggesting that the integration of AI assistance has minimal impact on how individuals seek help. The findings also suggest that the availability of AI assistance does not necessarily reduce learners’ reliance on support strategies (such as hints). The current study advances data science education and research by exploring the influence of AI assistance during complex data science problem-solving. We discuss implications and identify paths for future research.
在最近的教育文献中，生成式人工智能应用日益受到关注。然而，人们对使用生成工具（如 ChatGPT）如何影响复杂问题解决过程中的求助行为知之甚少。在本文中，我们旨在进一步了解学习者在人工智能在线学习环境中解决数据科学编程任务时使用辅助策略（提示）的情况。研究比较了两种情况：学生在有人工智能辅助的 DaTu 中解决问题（45 人）和没有人工智能辅助的学生（44 人）。研究结果表明，两组学生在寻求提示行为上没有差异，这表明集成人工智能辅助对个人如何寻求帮助的影响微乎其微。研究结果还表明，提供人工智能辅助并不一定会减少学习者对辅助策略（如提示）的依赖。本研究通过探索人工智能辅助在复杂数据科学问题解决过程中的影响，推动了数据科学教育和研究的发展。我们讨论了研究的意义，并确定了未来研究的方向。
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Pub Date : 2024-08-01DOI: 10.1016/j.chbah.2024.100094
Aman Pathak, Veena Bansal
AI digital agents may act as decision-aid or as delegated agents. A decision-aid agent helps a user make decisions, whereas a delegated agent makes decisions on behalf of the consumer. The study determines the factors affecting the adoption intention of AI digital agents as decision aids and delegated agents. The domain of study is banking, financial services, and Insurance sector (BFSI). Due to the unique characteristics of AI digital agents, trust has been identified as an important construct in the extant literature. The study decomposed trust into social, cognitive, and affective trust. We incorporated PLS-SEM and fsQCA to examine the factors drawn from the literature. The findings from PLS-SEM suggest that perceived AI quality affects cognitive trust, perceived usefulness affects affective trust, and social trust affects cognitive and affective trust. The intention to adopt AI as a decision-aid is influenced by affective and cognitive trust. The intention to adopt AI as delegated agents is influenced by social, cognitive, and affective trust. FsQCA findings indicate that combining AI quality, perceived usefulness, and trust (social, cognitive, and affective) best explains the intention to adopt AI as a decision aid and delegated agents.
{"title":"AI as decision aid or delegated agent: The effects of trust dimensions on the adoption of AI digital agents","authors":"Aman Pathak, Veena Bansal","doi":"10.1016/j.chbah.2024.100094","DOIUrl":"10.1016/j.chbah.2024.100094","url":null,"abstract":"<div><div>AI digital agents may act as decision-aid or as delegated agents. A decision-aid agent helps a user make decisions, whereas a delegated agent makes decisions on behalf of the consumer. The study determines the factors affecting the adoption intention of AI digital agents as decision aids and delegated agents. The domain of study is banking, financial services, and Insurance sector (BFSI). Due to the unique characteristics of AI digital agents, trust has been identified as an important construct in the extant literature. The study decomposed trust into social, cognitive, and affective trust. We incorporated PLS-SEM and fsQCA to examine the factors drawn from the literature. The findings from PLS-SEM suggest that perceived AI quality affects cognitive trust, perceived usefulness affects affective trust, and social trust affects cognitive and affective trust. The intention to adopt AI as a decision-aid is influenced by affective and cognitive trust. The intention to adopt AI as delegated agents is influenced by social, cognitive, and affective trust. FsQCA findings indicate that combining AI quality, perceived usefulness, and trust (social, cognitive, and affective) best explains the intention to adopt AI as a decision aid and delegated agents.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"2 2","pages":"Article 100094"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142426883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.chbah.2024.100104
Satoshi Nishida
Recent advancements in artificial intelligence (AI) have not eased human anxiety about AI. If such anxiety diminishes human preference for AI-synthesized visual information, the preference should be reduced solely by the belief that the information is synthesized by AI, independently of its appearance. This study tested this hypothesis by asking experimental participants to rate the attractiveness of faces synthesized by an artificial neural network, under the false instruction that some faces were real and others were synthetic. This experimental design isolated the impact of belief on attractiveness ratings from the actual facial appearance. Brain responses were also recorded with fMRI to examine the neural basis of this belief effect. The results showed that participants rated faces significantly lower when they believed them to be synthetic, and this belief altered the responsiveness of fMRI signals to facial attractiveness in the right fusiform cortex. These findings support the notion that human preference for visual information is reduced solely due to the belief that the information is synthesized by AI, suggesting that AI and robot design should focus not only on enhancing appearance but also on alleviating human anxiety about them.
{"title":"Behavioral and neural evidence for the underestimated attractiveness of faces synthesized using an artificial neural network","authors":"Satoshi Nishida","doi":"10.1016/j.chbah.2024.100104","DOIUrl":"10.1016/j.chbah.2024.100104","url":null,"abstract":"<div><div>Recent advancements in artificial intelligence (AI) have not eased human anxiety about AI. If such anxiety diminishes human preference for AI-synthesized visual information, the preference should be reduced solely by the belief that the information is synthesized by AI, independently of its appearance. This study tested this hypothesis by asking experimental participants to rate the attractiveness of faces synthesized by an artificial neural network, under the false instruction that some faces were real and others were synthetic. This experimental design isolated the impact of belief on attractiveness ratings from the actual facial appearance. Brain responses were also recorded with fMRI to examine the neural basis of this belief effect. The results showed that participants rated faces significantly lower when they believed them to be synthetic, and this belief altered the responsiveness of fMRI signals to facial attractiveness in the right fusiform cortex. These findings support the notion that human preference for visual information is reduced solely due to the belief that the information is synthesized by AI, suggesting that AI and robot design should focus not only on enhancing appearance but also on alleviating human anxiety about them.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"2 2","pages":"Article 100104"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142660938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.chbah.2024.100101
Samuel Westby , Richard J. Radke , Christoph Riedl , Brook Foucault Welles
Voice assistants are increasingly prevalent, from personal devices to team environments. This study explores how voice type and contribution quality influence human–agent team performance and perceptions of anthropomorphism, animacy, intelligence, and trustworthiness. By manipulating both, we reveal mechanisms of perception and clarify ambiguity in previous work. Our results show that the human resemblance of a voice assistant’s voice negatively interacts with the helpfulness of an agent’s contribution to flip its effect on perceived anthropomorphism and perceived animacy. This means human teammates interpret the agent’s contributions differently depending on its voice. Our study found no significant effect of voice on perceived intelligence, trustworthiness, or team performance. We find differences in these measures are caused by manipulating the helpfulness of an agent. These findings suggest that function matters more than form when designing agents for high-performing human–agent teams, but controlling perceptions of anthropomorphism and animacy can be unpredictable even with high human resemblance.
{"title":"How voice and helpfulness shape perceptions in human–agent teams","authors":"Samuel Westby , Richard J. Radke , Christoph Riedl , Brook Foucault Welles","doi":"10.1016/j.chbah.2024.100101","DOIUrl":"10.1016/j.chbah.2024.100101","url":null,"abstract":"<div><div>Voice assistants are increasingly prevalent, from personal devices to team environments. This study explores how voice type and contribution quality influence human–agent team performance and perceptions of anthropomorphism, animacy, intelligence, and trustworthiness. By manipulating both, we reveal mechanisms of perception and clarify ambiguity in previous work. Our results show that the human resemblance of a voice assistant’s voice negatively interacts with the helpfulness of an agent’s contribution to flip its effect on perceived anthropomorphism and perceived animacy. This means human teammates interpret the agent’s contributions differently depending on its voice. Our study found no significant effect of voice on perceived intelligence, trustworthiness, or team performance. We find differences in these measures are caused by manipulating the helpfulness of an agent. These findings suggest that function matters more than form when designing agents for high-performing human–agent teams, but controlling perceptions of anthropomorphism and animacy can be unpredictable even with high human resemblance.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"2 2","pages":"Article 100101"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142660939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.chbah.2024.100105
Emmanuele Tidoni , Emily S. Cross , Richard Ramsey , Michele Scandola
The shape and texture of humans and humanoid robots provide perceptual information that help us to appropriately categorise these stimuli. However, it remains unclear which features and attributes are driving the assignment into human and non-human categories. To explore this issue, we ran a series of five preregistered experiments wherein we presented stimuli that varied in their appearance (i.e., humans, humanoid robots, non-human primates, mannequins, hammers, musical instruments) and asked participants to complete a match-to-category task (Experiments 1-2-3), a priming task (Experiment 4), or to rate each category along four dimensions (i.e., similarity, liveliness, body association, action association; Experiment 5). Results indicate that categorising human bodies and humanoid robots requires the integration of both the analyses of their physical shape and visual texture (i.e., to identify a humanoid robot we cannot only rely on its visual shape). Further, our behavioural findings suggest that human bodies may be represented as a special living category separate from non-human animal entities (i.e., primates). Moreover, results also suggest that categorising humans and humanoid robots may rely on a network of information typically associated to human being and inanimate objects respectively (e.g., humans can play musical instruments and have a mind while robots do not play musical instruments and do have not a human mind). Overall, the paradigms introduced here offer new avenues through which to study the perception of human and artificial agents, and how experiences with humanoid robots may change the perception of humanness along a robot—human continuum.
{"title":"Are humanoid robots perceived as mindless mannequins?","authors":"Emmanuele Tidoni , Emily S. Cross , Richard Ramsey , Michele Scandola","doi":"10.1016/j.chbah.2024.100105","DOIUrl":"10.1016/j.chbah.2024.100105","url":null,"abstract":"<div><div>The shape and texture of humans and humanoid robots provide perceptual information that help us to appropriately categorise these stimuli. However, it remains unclear which features and attributes are driving the assignment into human and non-human categories. To explore this issue, we ran a series of five preregistered experiments wherein we presented stimuli that varied in their appearance (i.e., humans, humanoid robots, non-human primates, mannequins, hammers, musical instruments) and asked participants to complete a match-to-category task (Experiments 1-2-3), a priming task (Experiment 4), or to rate each category along four dimensions (i.e., similarity, liveliness, body association, action association; Experiment 5). Results indicate that categorising human bodies and humanoid robots requires the integration of both the analyses of their physical shape and visual texture (i.e., to identify a humanoid robot we cannot only rely on its visual shape). Further, our behavioural findings suggest that human bodies may be represented as a special living category separate from non-human animal entities (i.e., primates). Moreover, results also suggest that categorising humans and humanoid robots may rely on a network of information typically associated to human being and inanimate objects respectively (e.g., humans can play musical instruments and have a mind while robots do not play musical instruments and do have not a human mind). Overall, the paradigms introduced here offer new avenues through which to study the perception of human and artificial agents, and how experiences with humanoid robots may change the perception of humanness along a robot—human continuum.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"2 2","pages":"Article 100105"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.chbah.2024.100095
Hilda Hadan, Derrick M. Wang, Reza Hadi Mogavi, Joseph Tu, Leah Zhang-Kennedy, Lennart E. Nacke
Generative AI (GenAI) use in research writing is growing fast. However, it is unclear how peer reviewers recognize or misjudge AI-augmented manuscripts. To investigate the impact of AI-augmented writing on peer reviews, we conducted a snippet-based online survey with 17 peer reviewers from top-tier HCI conferences. Our findings indicate that while AI-augmented writing improves readability, language diversity, and informativeness, it often lacks research details and reflective insights from authors. Reviewers consistently struggled to distinguish between human and AI-augmented writing but their judgements remained consistent. They noted the loss of a “human touch” and subjective expressions in AI-augmented writing. Based on our findings, we advocate for reviewer guidelines that promote impartial evaluations of submissions, regardless of any personal biases towards GenAI. The quality of the research itself should remain a priority in reviews, regardless of any preconceived notions about the tools used to create it. We emphasize that researchers must maintain their authorship and control over the writing process, even when using GenAI's assistance.
{"title":"The great AI witch hunt: Reviewers’ perception and (Mis)conception of generative AI in research writing","authors":"Hilda Hadan, Derrick M. Wang, Reza Hadi Mogavi, Joseph Tu, Leah Zhang-Kennedy, Lennart E. Nacke","doi":"10.1016/j.chbah.2024.100095","DOIUrl":"10.1016/j.chbah.2024.100095","url":null,"abstract":"<div><div>Generative AI (GenAI) use in research writing is growing fast. However, it is unclear how peer reviewers recognize or misjudge AI-augmented manuscripts. To investigate the impact of AI-augmented writing on peer reviews, we conducted a snippet-based online survey with 17 peer reviewers from top-tier HCI conferences. Our findings indicate that while AI-augmented writing improves readability, language diversity, and informativeness, it often lacks research details and reflective insights from authors. Reviewers consistently struggled to distinguish between human and AI-augmented writing but their judgements remained consistent. They noted the loss of a “human touch” and subjective expressions in AI-augmented writing. Based on our findings, we advocate for reviewer guidelines that promote impartial evaluations of submissions, regardless of any personal biases towards GenAI. The quality of the research itself should remain a priority in reviews, regardless of any preconceived notions about the tools used to create it. We emphasize that researchers must maintain their authorship and control over the writing process, even when using GenAI's assistance.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"2 2","pages":"Article 100095"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.chbah.2024.100084
Scott Monteith , Tasha Glenn , John R. Geddes , Eric D. Achtyes , Peter C. Whybrow , Michael Bauer
The emphasis on artificial intelligence (AI) is rapidly increasing across many diverse aspects of society. This manuscript discusses some of the key topics related to the expansion of AI. These include a comparison of the unique cognitive capabilities of human intelligence with AI, and the potential risks of using AI in clinical medicine. The general public attitudes towards AI are also discussed, including patient perspectives. As the promotion of AI in high-risk situations such as clinical medicine expands, the limitations, risks and benefits of AI need to be better understood.
{"title":"Differences between human and artificial/augmented intelligence in medicine","authors":"Scott Monteith , Tasha Glenn , John R. Geddes , Eric D. Achtyes , Peter C. Whybrow , Michael Bauer","doi":"10.1016/j.chbah.2024.100084","DOIUrl":"10.1016/j.chbah.2024.100084","url":null,"abstract":"<div><p>The emphasis on artificial intelligence (AI) is rapidly increasing across many diverse aspects of society. This manuscript discusses some of the key topics related to the expansion of AI. These include a comparison of the unique cognitive capabilities of human intelligence with AI, and the potential risks of using AI in clinical medicine. The general public attitudes towards AI are also discussed, including patient perspectives. As the promotion of AI in high-risk situations such as clinical medicine expands, the limitations, risks and benefits of AI need to be better understood.</p></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"2 2","pages":"Article 100084"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949882124000446/pdfft?md5=de42c1e5a75fbb492e2bc6a082094c1f&pid=1-s2.0-S2949882124000446-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141853511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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