Computers in Human Behavior: Artificial Humans最新文献

{"title":"Task context matters: Gender dynamics and robot abuse in task-based interactions","authors":"Chandni Bagchi,&nbsp;Maartje M.A. de Graaf","doi":"10.1016/j.chbah.2026.100267","DOIUrl":"10.1016/j.chbah.2026.100267","url":null,"abstract":"<div><div>As robots become increasingly embedded in social roles, concerns have emerged about gender bias and abuse in human-robot interaction (HRI). This study investigated how robot gender cues, participant gender, and task type shape abusive behavior and mind attribution toward robots. Sixty-four participants interacted with a humanoid robot presented with either masculine or feminine cues across two task contexts: equation-solving (analytical) and emotion-recognition (social). Participants could reward or punish the robot based on its performance, providing behavioral measures of praise and punishment. Results showed no effects of robot gender cues or participant gender on praise and punishment. However, task context influenced punishment, with participants punishing the robot more during the equation-solving task than during the emotion-recognition task. Mind attribution revealed a gender-alignment effect, with participants attributing more mind to robots whose gender cues matched their own. These findings suggest that task context, not robot gender cues, plays a more prominent role in shaping abusive responses in HRI, while gender alignment subtly shapes social perception. Together, the results underscore the need for context-sensitive and socially aware robot design.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100267"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396367","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}

{"title":"A scoping review of hybrid intelligence systems for human-centred AI in education","authors":"Solomon Sunday Oyelere","doi":"10.1016/j.chbah.2026.100273","DOIUrl":"10.1016/j.chbah.2026.100273","url":null,"abstract":"<div><div>This scoping review explores the emergence and application of Hybrid Intelligence systems in education, integrative human-AI ecosystems designed to enhance learning outcomes through collaboration, co-evolution, and contextual adaptability. Drawing on 42 peer-reviewed and scholarly sources published between 2015 and 2025, this review examines the conceptual foundations, implementation frameworks, educational outcomes, and ethical implications of hybrid intelligence. Guided by Arksey and O'Malley's framework and PRISMA-ScR guidelines, the study employed a systematic search across six databases, followed by rigorous screening and thematic synthesis. The review identifies three core components of hybrid intelligence, collectivity, superior outcomes, and continuous learning, together with two cross-cutting design priorities: adaptivity and ethics. Evidence from included studies demonstrates that hybrid intelligence systems support cognitive gains, metacognitive awareness, self-regulated learning, and social-emotional development. Case studies highlight innovative use cases, from AI-supported writing tools and embodied learning environments to AI-augmented collaborative learning platforms. However, ethical and equity considerations remain underdeveloped. Issues such as algorithmic bias, data privacy, and the digital divide require urgent attention. The review underscores the need for participatory design, transparency, and educator agency in hybrid intelligence system development. By advancing a co-evolutionary perspective, where humans and AI adapt reciprocally, hybrid intelligence offers a transformative vision for future education. This review provides a conceptual framework, maps current knowledge, and outlines research gaps to inform future interdisciplinary inquiry and the responsible integration of hybrid intelligence in diverse educational settings.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100273"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396449","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}

{"title":"Impact of hyper-realistic human likeness on purchase intention","authors":"Claude Chien-hung Liu ,&nbsp;Chris Sheng-chi Chen","doi":"10.1016/j.chbah.2026.100282","DOIUrl":"10.1016/j.chbah.2026.100282","url":null,"abstract":"<div><div>The rapid adoption of digital human streamers in livestream commerce has reshaped how consumers interact with AI-driven marketing agents. While hyper-realistic digital humans are increasingly deployed to address labour shortages, extend livestreaming hours, and enhance promotional effectiveness, it remains unclear whether higher human-likeness strengthens or weakens consumers’ purchase intention. Drawing on anthropomorphism theory and the Uncanny Valley framework, this study examines how hyper-realistic human likeness influences purchase intention both directly and indirectly through perceived marketing effectiveness and affective responses. Using a survey experiment featuring digital humans with varying levels of realism, data were collected from 316 Chinese livestream commerce users and analysed using PLS-SEM. Results show that hyper-realistic human likeness does not directly increase purchase intention. Instead, its effects operate through two distinct pathways. First, greater realism enhances perceived marketing effectiveness—reflected in perceived assortment, price advantages, and channel transparency—which strongly predicts purchase intention. Second, higher realism significantly reduces perceived eeriness, indirectly improving purchase intention, although perceived uneasiness shows no meaningful influence. These findings advance livestream commerce research by demonstrating that hyper-realism functions as an indirect catalyst rather than a direct persuasive cue. The study provides strategic guidance for firms designing AI-driven livestreaming solutions and deploying hyper-realistic digital human streamers.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100282"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396350","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}

{"title":"Behaviorism rebooted as the foundation of human digital twins: A systematic review of applications in human-robot collaboration and healthcare","authors":"Endale Mitiku Adere","doi":"10.1016/j.chbah.2026.100280","DOIUrl":"10.1016/j.chbah.2026.100280","url":null,"abstract":"<div><div>Digital twin (DT) technology has recently risen to prominence as a crucial subject in research. DT can be described as the creation of a digital representation of an object, which modifies its states in response to changes in the actual object through bidirectional information exchanges. Recently, advancements in this field have led to the emergence of Human Digital Twin (HDT), which aims to create a digital counterpart for humans, encompassing their full body, body parts, tools, activities, working environments, and any issues that can be addressed through the design of its digital counterpart. In this paper, we review the literature regarding HDT by searching for articles on ScienceDirect, IEEE Xplore, and Springer Nature Link using the keyword 'human digital twin.' Our initial evaluation of the search results indicates that the literature primarily concentrates on manufacturing and healthcare. In the manufacturing sector, the main focus is on human-robot collaboration. Therefore, to provide a thorough analysis of the literature, we emphasize human-robot collaboration and healthcare. Moreover, we examine the theoretical foundations that underpin the design and development of HDT. Our review findings suggest that authors employ HDT in human-robot collaboration for five primary objectives: controlling collaborations, ensuring safety, enhancing operator well-being, establishing multi-robot collaboration, and achieving various other goals. Additionally, in healthcare, HDT is being utilized to achieve benefits such as personalized medications for patients, providing remote medical services, reflecting individuals' health conditions; providing additional information about how the human body reacts to various treatment methods; and modeling factors that influence a patient's circumstances, among others. Furthermore, we discovered that behaviorism, in its two versions—methodological behaviorism and purposive behaviorism—serves as the foundation for the design and development of HDT, with methodological behaviorism being more prominent in healthcare and purposive behaviorism being more dominant in human-robot collaboration works. Overall, our review offers a detailed presentation of the use of HDT in these domains.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100280"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396360","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}

{"title":"Human-robot action teams: How robot proactivity affects teamwork","authors":"Rabeya Jamshad ,&nbsp;Arthi Haripriyan ,&nbsp;Advika Sonti ,&nbsp;Susan Simkins ,&nbsp;Laurel D. Riek","doi":"10.1016/j.chbah.2026.100269","DOIUrl":"10.1016/j.chbah.2026.100269","url":null,"abstract":"<div><div>Many researchers in human-AI and human-robot interaction are interested in robots supporting teams that collaborate under intense conditions, such as action teams. Action teams must make de-cisions quickly, under uncertainty, and under high workload. Here, a helpful autonomous system would be one that can act proactively, as people do not have time to engage in autonomy manage-ment. However, there is limited understanding of how proactive robot behaviors may impact team dynamics and human-robot collaboration, as prior work has primarily focused on either dyadic interactions or separately studied proactive dialogue and proactive actions. In this paper, we ex-plored this within the context of an escape room game, where action teams of three people and a physical robot (Stretch) collaboratively complete tasks. We varied Stretch's behaviors (proactive or passive) in each escape room to facilitate teamwork. Our results reveal insights into how teams can differ in their preferences for proactive and passive robot behaviors, which are affected by a robot's perceived helpfulness, capabilities, and a team's desire for a sense of control over the robot. We discuss several suggestions for designing future autonomous system teaming behaviors, which consider: the team's sense of control and resilience to robot errors, the robot's status in the team, and the robot's communication style. We also explore the ethical implications of how proactive robot help may mislead people during human-robot collaboration. These contributions lay the foundation for designing robots that can support action teams, specifically with regard to the nuances of designing proactive behaviors, and moderating user expectations of proactive robot teammates.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100269"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396454","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}

{"title":"Why do people prefer social sharing of emotion with conversational AI over human partners? A structural topic modeling approach","authors":"Yuki Nozaki","doi":"10.1016/j.chbah.2026.100260","DOIUrl":"10.1016/j.chbah.2026.100260","url":null,"abstract":"<div><div>The social sharing of emotion, traditionally studied in human-to-human interactions, is increasingly occurring with conversational AI systems. However, the specific motives for preferring AI over human partners for this purpose remain relatively underexplored. The present study aims to identify these motives and examine their associations with individual differences—namely, frequency of conversational AI usage, social anxiety, and self-esteem. Open-ended survey responses from 178 university students, describing situations in which they would prefer to share emotions with conversational AI rather than with human partners and their reasons for this preference, were analyzed using structural topic modeling to identify key themes and their relationships with individual difference variables. The analysis revealed five primary motives: (1) talking things over casually, (2) seeking emotional connection, (3) venting as an alternative to a close confidant, (4) seeking objective advice, and (5) consulting on topics difficult to discuss with people. Moreover, higher social anxiety was significantly associated with greater endorsement of consulting on topics difficult to discuss with people. This research provides an empirically derived taxonomy of motives for sharing emotions with conversational AI, highlighting that individuals perceive AI as a partner capable of fulfilling specific socio-affective needs by leveraging its unique features, such as its nonjudgmental nature and constant availability. The findings contribute to a deeper understanding of the evolving landscape of human–AI relationships.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100260"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396455","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}

{"title":"Generative agents in the streets: Exploring the use of Large Language Models (LLMs) in collecting urban perceptions","authors":"Deepank Verma,&nbsp;Olaf Mumm,&nbsp;Vanessa Miriam Carlow","doi":"10.1016/j.chbah.2026.100277","DOIUrl":"10.1016/j.chbah.2026.100277","url":null,"abstract":"<div><div>Evaluating the surroundings to gain understanding, frame perspectives, and anticipate behavioral reactions is an inherent human trait. However, these continuous encounters are diverse and complex, posing challenges to their study and experimentation. Researchers have been able to isolate environmental features and study their effect on human perception and behavior. However, research attempts to replicate and study human behaviors with proxies, such as by integrating virtual mediums and interviews, have been inconsistent. Large language models (LLMs) have recently been unveiled as capable of contextual understanding and semantic reasoning. These models have been trained on large amounts of text and have evolved to mimic believable human behavior. This study explores the current advancements in Generative agents powered by LLMs with the help of perceptual experiments. The experiment employs Generative agents to interact with the urban environments using street view images to plan their journey toward specific goals. The agents are given virtual personalities, which make them distinguishable. They are also provided a memory database to store their thoughts and essential visual information and retrieve it when needed to plan their movement. Since LLMs do not possess embodiment, nor have access to the visual realm, and lack a sense of motion or direction, we designed movement and visual modules that help agents gain an overall understanding of surroundings. The agents are further employed to rate the surroundings they encounter based on their perceived sense of safety and liveliness. As these agents store details in their memory, we query the findings to get details regarding their thought processes. Overall, this study experiments with current AI developments and their potential in simulated human behavior in urban environments.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100277"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396457","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}

{"title":"The acceptability of artificial intelligence to support university students’ mental health: the role of Asian cultural values and social support","authors":"Mazel Lye,&nbsp;Rachael Martin,&nbsp;Sally Richmond","doi":"10.1016/j.chbah.2025.100247","DOIUrl":"10.1016/j.chbah.2025.100247","url":null,"abstract":"<div><div>The global prevalence of mental disorders is rising, with university students at-risk due to the increased psychological distress associated with this period simultaneously occurring with the age of onset of many mental disorders. Artificial intelligence (AI) has been proposed as a novel treatment approach for mental disorders, yet little is known about its acceptability when applied to mental health interventions from the perspective of university students. This study aimed to explore the role of social support and Asian cultural values, two factors linked more broadly with mental health help-seeking, in the acceptability of AI-based mental health interventions among university students. A sample of 135 Australian university students (<em>M</em>_<em>age</em> = 25.26 years, <em>SD</em>_<em>age</em> = 7.72 years) of diverse ethnicity (e.g. 43.7 % White/European, 29.6 % Asian and 11.9 % Aboriginal and/or Torres Strait Islander) completed questionnaires for social support, Asian cultural values, and acceptability. A multiple linear regression indicated that Asian cultural values were positively associated with acceptability of AI-based mental health interventions; no support was found for the association with social support. Age and ethnicity were included in the regression model as covariates to adjust for their potential influence on acceptability. These findings provide insight into the relationship between the acceptability of AI-based mental health interventions, social support and Asian cultural values, and demonstrate the need to understand student perspectives before implementing such interventions.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100247"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791589","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}

{"title":"Public acceptance of various ways to use and treat AI: A predictive moral framework","authors":"Kimmo Eriksson ,&nbsp;Simon Karlsson ,&nbsp;Irina Vartanova ,&nbsp;Pontus Strimling","doi":"10.1016/j.chbah.2026.100257","DOIUrl":"10.1016/j.chbah.2026.100257","url":null,"abstract":"<div><div>As artificial intelligence rapidly transforms society, understanding the psychological principles that drive public acceptance or resistance is a critical challenge. Our study (U.S. sample, N = 589) of 100 diverse AI applications, both personal and organizational, demonstrates these moral judgments are highly predictable. We show that public perceptions of five relatively independent core qualities—risk, benefit, dishonesty, unnaturalness, and accountability—predict 91.4% of the variance in an application's acceptability. Notably, all five qualities contributed to predictive accuracy. Our framework also explains a profound contradiction in public attitudes toward AI treatment: people strongly reject deceiving or forcing AI systems—as if granting them rights—yet readily accept their termination. We show this is not an inconsistent attribution of AI's status, but a consistent moral evaluation of human actions: deception and coercion are rejected as intrinsically flawed behaviors, while termination is accepted as a legitimate exercise of control. Furthermore, we identify a key psychological mechanism for attitude change: personal experience with an AI application predicts higher acceptance by improving perceptions of its moral qualities. In sum, our research reveals the structured moral psychology that governs AI acceptance, providing a powerful framework for understanding human-AI interaction and guiding responsible innovation.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100257"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396458","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}

{"title":"Mapping AI learning readiness self-efficacy worldwide: Scale validation and cross-continental patterns","authors":"Atte Oksanen ,&nbsp;Teijo Osma ,&nbsp;Moona Heiskari ,&nbsp;Anica Cvetkovic ,&nbsp;Eerik Soares Ruokosuo ,&nbsp;Mayu Koike ,&nbsp;Patrícia Arriaga ,&nbsp;Iina Savolainen","doi":"10.1016/j.chbah.2026.100251","DOIUrl":"10.1016/j.chbah.2026.100251","url":null,"abstract":"<div><div>In today's world, knowing how to use artificial intelligence (AI) technologies is becoming an essential skill. While methods for measuring the perceived efficacy of AI use are emerging, brief measures of users' self-evaluated learning and self-efficacy regarding AI use are still lacking. This study aimed to validate the five-item AI Learning Readiness Self-Efficacy (AILRSE) scale and examine cross-national differences between 12 countries on six continents. We used large-scale, adult population samples from Australia, Brazil, Finland, France, Germany, Ireland, Italy, Japan, Poland, Portugal, South Africa, and the United States collected in 2024–2025 (N = 20,173), enabling both cross-sectional and longitudinal analysis. Scale validation involved confirmatory factor analysis and measurement invariance testing across countries and over time. The results supported a one-factor structure with high internal consistency and scalar invariance across countries as well as strict invariance in Finnish cross-sectional and longitudinal data. AI positivity emerged as the strongest predictor of AILRSE-5 scores across all models, followed by younger age and more frequent use of text-to-text AI tools (e.g., ChatGPT, Copilot). Education and gender effects were small and context dependent. The findings indicate that AILRSE-5 is a brief, reliable, and valid tool for assessing self-efficacy in AI learning readiness. Its invariance across diverse national contexts supports its applicability in cross-cultural research, while its longitudinal invariance suggests stability over time. Furthermore, our results provide rare cross-national evidence on the individual factors shaping AI learning readiness self-efficacy. The study advances understanding of how people adapt to the rapidly evolving AI landscape.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100251"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078002","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}