Computers in Human Behavior: Artificial Humans最新文献_第7页

Trust in AI news, AI literacy, and the mediating role of artificial intelligence attitudes: A longitudinal study across diverse societies 对人工智能新闻的信任、人工智能素养以及人工智能态度的中介作用：一项跨不同社会的纵向研究

Computers in Human Behavior: Artificial Humans Pub Date : 2026-03-01 Epub Date: 2026-02-26 DOI: 10.1016/j.chbah.2026.100279

Manuel Goyanes , Sonja Utz , Homero Gil de Zúñiga

{"title":"Trust in AI news, AI literacy, and the mediating role of artificial intelligence attitudes: A longitudinal study across diverse societies","authors":"Manuel Goyanes , Sonja Utz , Homero Gil de Zúñiga","doi":"10.1016/j.chbah.2026.100279","DOIUrl":"10.1016/j.chbah.2026.100279","url":null,"abstract":"<div><div>The rise of generative artificial intelligence (AI) tools, such as ChatGPT, has introduced new ways for people to access and interact with news. Yet, little is known about the factors that shape citizens' trust in AI-generated news. Drawing on a two-wave cross-national panel survey conducted in the United States (<em>n</em> = 1815), Spain (<em>n</em> = 1811), and Chile (<em>n</em> = 1802), this study investigates how AI literacy and attitudes toward AI influence trust in AI-generated news over time. We propose and test a mediation model in which AI literacy affects news trust indirectly through attitudes toward AI. Results show that while AI literacy does not exert a direct influence on trust in AI-generated news, it fosters more positive attitudes toward AI, which in turn enhance trust. This indirect effect is consistent across all three countries, suggesting an attitudinal mechanism linking literacy and trust in AI news trust. Overall, our findings highlight the key role of positive attitudinal evaluations in shaping public trust in communicative AI and underscore that fostering trust requires both improving citizens’ understanding of AI and addressing their broader attitudes toward this technology.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100279"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

(work)Flow bots vs. No bots: Workflow dynamics and AI utilization in teams （工作）流程机器人vs.无机器人：团队中的工作流动态和AI利用

Computers in Human Behavior: Artificial Humans Pub Date : 2026-03-01 Epub Date: 2026-02-10 DOI: 10.1016/j.chbah.2026.100264

Sean M. Fitzhugh

{"title":"(work)Flow bots vs. No bots: Workflow dynamics and AI utilization in teams","authors":"Sean M. Fitzhugh","doi":"10.1016/j.chbah.2026.100264","DOIUrl":"10.1016/j.chbah.2026.100264","url":null,"abstract":"<div><div>Teams face an ongoing challenge managing workflow by aligning team members with tasks that must be completed. The increasing prevalence of AI-based team members has important implications for workflow, as humans and AIs have vastly different capabilities. However, it remains unclear how real-world teams incorporate AI-based team members into workflows, and how this shapes team behaviors such as communication, which may be needed to enhance explainability of AI decision-making. This study uses a sample of 22k human-AI teams and 113k human teams on GitHub over the course of a month, and represents their workflows as dynamic networks of discrete, timestamped interactions between team members and the actions they perform. A relational event model uncovered structural patterns underlying the workflow network dynamics for each team, and standardized coefficients from those models were used to estimate whether a team included an AI team member and the level of activity of AI team members. Results showed that human-AI teams’ workflows are more routinized, overlapping, decentralized, and dominated by a handful of disproportionately active individuals; these features were stronger in teams with higher levels of AI activity. By contrast, human teams were more structurally segmented, with relatively even workflows across team members. Additionally, human-AI teams engaged in more communication, although communication levels were higher in teams with less AI activity. Results show clear distinctions between the types of activities performed and the structure of workflows between human and human-AI teams, suggesting distinct trajectories for development of key states and processes.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100264"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Do AI voices follow social nuances? The case of politeness and speech rate 人工智能的声音是否遵循社会的细微差别？礼貌与言语率的案例

Computers in Human Behavior: Artificial Humans Pub Date : 2026-03-01 Epub Date: 2026-02-13 DOI: 10.1016/j.chbah.2026.100256

Eyal Rabin , Zohar Elyoseph , Rotem Israel-Fishelson , Adi Dali , Ravit Nussinson

{"title":"Do AI voices follow social nuances? The case of politeness and speech rate","authors":"Eyal Rabin , Zohar Elyoseph , Rotem Israel-Fishelson , Adi Dali , Ravit Nussinson","doi":"10.1016/j.chbah.2026.100256","DOIUrl":"10.1016/j.chbah.2026.100256","url":null,"abstract":"<div><div>Voice-based artificial intelligence is increasingly expected to adhere to human social conventions, but can it exhibit implicit cues that are not explicitly programmed? This study investigates whether state-of-the-art text-to-speech systems have internalized the human tendency to reduce speech rate to convey politeness - a non-obvious prosodic marker. We prompted 22 synthetic voices from two leading AI platforms (AI Studio and OpenAI) to read a fixed script under both “polite and formal” and “casual and informal” conditions and measured the resulting speech duration. Across both AI platforms, the polite prompt produced slower speech than the casual prompt with very large effect sizes, an effect that was statistically significant for all of AI Studio's voices and for a large majority of OpenAI's voices. A second study confirmed that these prosodic adjustments are perceptually salient to human listeners, who successfully distinguished between the intended polite and casual styles based on the AI's output. These results demonstrate that AI can implicitly replicate the statistical patterns of human communication, highlighting its emerging role as a social actor that can reinforce human social norms.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100256"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prosodic cues strengthen human-AI voice boundaries: Listeners do not easily perceive human speakers and AI clones as the same person 韵律线索加强了人类与AI的声音界限：听众不容易将人类说话者和AI克隆体视为同一个人

Computers in Human Behavior: Artificial Humans Pub Date : 2026-03-01 Epub Date: 2026-02-07 DOI: 10.1016/j.chbah.2026.100261

Wenjun Chen , Marc D. Pell , Xiaoming Jiang

{"title":"Prosodic cues strengthen human-AI voice boundaries: Listeners do not easily perceive human speakers and AI clones as the same person","authors":"Wenjun Chen , Marc D. Pell , Xiaoming Jiang","doi":"10.1016/j.chbah.2026.100261","DOIUrl":"10.1016/j.chbah.2026.100261","url":null,"abstract":"<div><div>Previous studies concluded that listeners struggle to discriminate AI from human voices, but these studies used monotone-like speech and did not examine prosodic expressiveness, a key advantage of human over AI speakers. This study explores whether prosodic expressiveness facilitates human-AI voice discrimination. We recorded human prosodic speech with confident and doubtful expressions, trained AI models to replicate these prosodic patterns, had AI models generate new sentences, and then had human speakers produce equivalent prosodic expressions for the same sentences. In Experiment 1, we had 48 listeners rate humanlikeness and perceived confidence in 11,808 audio samples, finding that AI speech was consistently rated as less humanlike regardless of prosody. We selected 768 audios (AI × human, confident × doubtful prosody) for Experiment 2, where 80 listeners completed an identity discrimination task, telling whether two sounds were from the same speaker. Bayesian modeling results revealed near-ceiling performance for human-human/AI-AI pairs, with inconsistent prosodies decreasing accuracy by ∼7%, while listeners do not easily categorize AI and human as sharing the same identity (∼54% accuracy when prosody matches, dropping to ∼36% when inconsistent). We observed accuracy–reaction time synchronization; in human–AI/AI–human pairs only, however, listeners relied less on distance cues when the two voices’ identities were distant beyond a certain threshold. Overall, we found that listeners perceive AI speech as lower in humanlikeness, and prosodic variation further promotes rejecting AI and human voices as sharing the same identity, indicating that human acceptance of AI voices as equivalent to human voices is limited.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100261"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A qualitative shift in AI capabilities: A “bitter lesson” for human-AI interaction research? 人工智能能力的质变：人类与人工智能交互研究的“惨痛教训”？

Computers in Human Behavior: Artificial Humans Pub Date : 2026-03-01 Epub Date: 2026-02-07 DOI: 10.1016/j.chbah.2026.100253

Kim Astor

引用次数: 0

Wall-E vs. Terminator: The relationship between physical appearance and dimensions of mind perception Wall-E vs. Terminator：外形与思维感知维度之间的关系

Computers in Human Behavior: Artificial Humans Pub Date : 2026-03-01 Epub Date: 2026-02-07 DOI: 10.1016/j.chbah.2026.100271

Yasmina Giebeler , Basil Wahn , Eva Wiese

{"title":"Wall-E vs. Terminator: The relationship between physical appearance and dimensions of mind perception","authors":"Yasmina Giebeler , Basil Wahn , Eva Wiese","doi":"10.1016/j.chbah.2026.100271","DOIUrl":"10.1016/j.chbah.2026.100271","url":null,"abstract":"<div><div>Social robots are increasingly integrated into everyday environments, yet effective human-robot interaction remains challenging as robots often fail to engage social cognition the same way human partners do. Here, we examined how physical features impact whether robots are being perceived as agents \"with a mind\". We used standardized images of 251 different robots from the Anthropomorphic Robot Database (ABOT) and assessed mind perception based on ratings provided by 300 human participants. Consistent with prior findings, robots were attributed more agency (to act and plan) than experience (to sense and feel). Body components significantly explained variance in perceived agency, especially in interaction with facial features (adj. R<sup>2</sup> = 0.57). For experience, variance was best explained by a combination of body components, face and surface details (adj. R<sup>2</sup> = 0.64). Increasing human-likeness boosted perceptions of both dimensions, but the trajectories differed: experience followed a cubic function, plateauing at medium levels of human-likeness, while agency followed a quartic function with a dip around 75% human-likeness, a pattern resembling the uncanny valley. Our results indicate that specific levels of agency and experience may not be achieved by mere increase of general human-likeness. Instead, when high agency is desired (e.g., for surgery robots), design should primarily emphasize body components, supported by facial component, whereas enhancing experience (e.g., for companion robots) is best achieved using a combination of all three components. Our ratings linking physical robot features to dimensions of mind perception are made available, offering a comprehensive and accessible resource for experimentally manipulating perceptions of agency and experience.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100271"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Can an AI agent lead human teams? 人工智能代理能领导人类团队吗？

Computers in Human Behavior: Artificial Humans Pub Date : 2026-03-01 Epub Date: 2026-02-24 DOI: 10.1016/j.chbah.2026.100278

James Simpson , Gaurav Patil , Hamish Stening , Ayman Bin Kamruddin , Daniel Somerville , Sigrid Seage , Patrick Nalepka , Mark Dras , Simon G. Hosking , Rachel W. Kallen , Michael J. Richardson , Deborah Richards

{"title":"Can an AI agent lead human teams?","authors":"James Simpson , Gaurav Patil , Hamish Stening , Ayman Bin Kamruddin , Daniel Somerville , Sigrid Seage , Patrick Nalepka , Mark Dras , Simon G. Hosking , Rachel W. Kallen , Michael J. Richardson , Deborah Richards","doi":"10.1016/j.chbah.2026.100278","DOIUrl":"10.1016/j.chbah.2026.100278","url":null,"abstract":"<div><div>The use of artificial intelligent (AI) agents to assist or even augment humans performing a task has been an area of research in the field of AI. Most recently, there has been growing interest in developing assistive agents for team applications using Large Language Models (LLMs). However, little seems to be known about whether humans would want to be led by a conversational agent, and what impact such agents might have on team performance and communication dynamics. Here we report the findings which examine this question using a multiplayer video game involving a collaborative search task, where teams of players were led by either a human drone operator or one of two AI drone operators: a simple heuristic (rule) based model and one based on a fine-tuned large language model. While teams led by either AI agent did not perform as well as those led by a human drone operator overall, subtleties in team performance measures observed. Specifically, teams led by the LLM operator had comparable containment performance as those led by the human operator, yet were unsuccessful in completing trials in the presence of fog. Taken together, our findings suggest that AI agents hold the potential of leading human teams engaged in a collaborative tasks and provide guidelines for the development of future collaborative conversational agents adopting team leadership roles.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100278"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Who listens to ChatGPT and when should they? A two-study examination of AI-assisted decision making 谁会听ChatGPT，什么时候应该听？人工智能辅助决策的两项研究

Computers in Human Behavior: Artificial Humans Pub Date : 2026-03-01 Epub Date: 2026-03-06 DOI: 10.1016/j.chbah.2026.100283

Matthew J. Grawitch, Steven L. Winton, Srikanth P. Mudigonda

{"title":"Who listens to ChatGPT and when should they? A two-study examination of AI-assisted decision making","authors":"Matthew J. Grawitch, Steven L. Winton, Srikanth P. Mudigonda","doi":"10.1016/j.chbah.2026.100283","DOIUrl":"10.1016/j.chbah.2026.100283","url":null,"abstract":"<div><div>As large language models (LLMs) become increasingly integrated into decision-making contexts, understanding when, why, and how individuals engage with AI-generated input is critical. Across two experimental studies, we examined psychological and contextual factors that influence AI consultation, application, and performance outcomes during a structured decision-making task. In Study 1, participants chose whether to seek input from ChatGPT and how to apply it; in Study 2, all participants received AI recommendations, but input quality was manipulated. Across both studies, applying AI suggestions—rather than merely consulting them—was the key determinant of performance. Trust in AI consistently predicted both input seeking and application, while higher self-perceived expertise reduced reliance on AI—even when participants lacked relevant knowledge. Notably, performance gains only occurred when AI output was high quality and applied effectively; in contrast, applying low-quality recommendations harmed performance. These findings underscore the need for AI systems that not only foster appropriate trust, but also support users in critically evaluating the input they receive.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100283"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The reasoning-like capabilities of large language models across different languages: Insights from representational similarity analysis 跨不同语言的大型语言模型的类似推理的能力：来自表示相似性分析的见解

Computers in Human Behavior: Artificial Humans Pub Date : 2026-03-01 Epub Date: 2026-01-20 DOI: 10.1016/j.chbah.2026.100250

Chris M. Stolle , Rongjun Yu , Yi Huang

{"title":"The reasoning-like capabilities of large language models across different languages: Insights from representational similarity analysis","authors":"Chris M. Stolle , Rongjun Yu , Yi Huang","doi":"10.1016/j.chbah.2026.100250","DOIUrl":"10.1016/j.chbah.2026.100250","url":null,"abstract":"<div><div>Recent research shows that Large Language Models (LLMs) demonstrate human-comparable performance on various cognitive tasks, suggesting reasoning-like capabilities. However, the language dependency of these capabilities and the contribution of their neural network states remain underexplored. This study investigates how different prompts and languages influence the reasoning performance of LLMs compared to humans, while exploring the internal cognitive-like processes of LLMs through representational similarity analysis (RSA). Using scenario-based and mathematical Cognitive Reflection Test (CRT) questions across four languages, we evaluated the reasoning capabilities of LLM Qwen 2.5 (including Gemma 2.9 and Llama 3.1 replications). Results showed that language significantly impacts performance in scenario-based CRT that requires nuanced semantic processing. However, RSA of the inner state activations revealed that the LLM processed identical questions similarly across languages, suggesting that the model encodes semantics in a language-independent latent space. Additionally, the LLM's performance improved when it verbalised its reasoning, and this verbalisation increased similarity in activations. Layer-wise analyses revealed a U-shaped similarity pattern across early to late layers in Qwen and Gemma but not Llama. Furthermore, scenario-based and equivalent mathematical CRT versions elicited similar activation patterns for the paired questions, even after controlling for input and output confounds, pointing to format-agnostic reasoning mechanisms. These results highlight that while LLMs exhibit language-invariant semantic representations and format-agnostic reasoning, their performance remains sensitive to linguistic nuances and self-generated verbalisations, offering insights into both the strengths and limitations of their cognitive-like processing.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100250"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-supported problem-based learning for enhancing computational thinking skills in STEM education 人工智能支持的基于问题的学习，以提高STEM教育中的计算思维技能

Computers in Human Behavior: Artificial Humans Pub Date : 2026-03-01 Epub Date: 2026-02-09 DOI: 10.1016/j.chbah.2026.100263

Musa Adekunle Ayanwale , Christian Basil Omeh

{"title":"AI-supported problem-based learning for enhancing computational thinking skills in STEM education","authors":"Musa Adekunle Ayanwale , Christian Basil Omeh","doi":"10.1016/j.chbah.2026.100263","DOIUrl":"10.1016/j.chbah.2026.100263","url":null,"abstract":"<div><div>By 2030, over 80% of skilled jobs will require higher order thinking such as computational thinking skills and problem-solving capabilities, yet most university students, especially in developing countries remain underprepared for such demands. This skills gap is particularly evident in computer robotics programming education, where traditional pedagogies fall short in fostering computational thinking (CT) and academic achievement. This study investigates the effectiveness of Artificial Intelligence-supported Problem-Based Learning (AI-PBL) in enhancing students' CT and performance in computer robotics programming within a Nigerian university context. Grounded in Vygotsky's Social Constructivist Theory, the study positions AI tools as “more capable peers,” offering adaptive scaffolding through intelligent systems embedded in inquiry-driven instruction. A quasi-experimental design with pre-test–post-test non-equivalent groups was employed to randomly assign the students. A total of 103 students were randomly assigned to experimental (AI-PBL) and control (conventional PBL) groups. Data was analyzed using ANCOVA and simple main effects tests. Results showed that students in the AI-PBL group significantly outperformed their peers in both posttest CT and academic achievement, controlling baseline scores. While gender did not significantly moderate the overall effects, both male and female students benefited from the AI-PBL approach. These results affirm the pedagogical potential of AI-enhanced PBL in STEM education, particularly in under-resourced contexts. The integration of intelligent systems not only improves student learning outcomes but also aligns with future workforce needs. The study calls for institutional and policy-level adoption of AI-PBL frameworks, investments in teacher training, and further research to ensure scalability. AI-supported pedagogy is not just innovative; it is essential for equitable skill acquisition and making student future-ready for world of work.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100263"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0