Computers in Human Behavior: Artificial Humans最新文献

{"title":"Erratum regarding corrections related to Ethical approval and informed consent in articles published in [Computers in Human Behavior: Artificial Humans 3C (2025)]","authors":"","doi":"10.1016/j.chbah.2026.100318","DOIUrl":"10.1016/j.chbah.2026.100318","url":null,"abstract":"","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"8 ","pages":"Article 100318"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148476952","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}

{"title":"Parasocial relationships with artificial intelligence (AI): A systematic review of benefits and risks","authors":"Jing Wen Hung ,&nbsp;Charlotte K.Y. Lee ,&nbsp;K.T.A. Sandeeshwara Kasturiratna,&nbsp;Andree Hartanto","doi":"10.1016/j.chbah.2026.100323","DOIUrl":"10.1016/j.chbah.2026.100323","url":null,"abstract":"<div><div>Advances in artificial intelligence (AI) have transformed AI systems into increasingly interactive and relational agents, raising questions about the consequences of the perceived sense of intimacy and reciprocity formed with AI systems. The present systematic review synthesised empirical evidence on the benefits and risks of AI parasocial relationships across diverse AI agents and contexts. Following PRISMA guidelines, 39 empirical records were identified and analysed using narrative thematic analysis. Through this, five key benefits of AI parasocial relationships were identified, namely personal development and adaptive functioning, emotional support, social needs fulfilment, enjoyment and entertainment, and community participation and social belonging. However, these perceived relationships also carry risks, consisting of commercial persuasion and consumer influence, displacement of human relationships, emotional dependence, privacy and data exploitation concerns, and behavioural addiction and compulsivity. The review also highlights research gaps including the lack of consistent definition and measurement of AI parasocial relationships, and underscores the need for greater theoretical clarity, longitudinal research, and responsible AI design to maximise benefits while mitigating potential harms for users.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"8 ","pages":"Article 100323"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178406","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":"AI and synthetic happiness in esports athletes and recreational gamers: Between code, competition, and well-being","authors":"Jorge A. Ruiz-Vanoye ,&nbsp;Francisco R. Trejo-Macotela ,&nbsp;Ocotlán Diaz-Parra ,&nbsp;Jaime Aguilar-Ortiz ,&nbsp;Miguel A. Ruiz-Jaimes ,&nbsp;Yadira Toledo-Navarro ,&nbsp;Alejandro Fuentes-Penna ,&nbsp;Ricardo A. Barrera-Cámara ,&nbsp;Marco A. Vera-Jiménez","doi":"10.1016/j.chbah.2026.100309","DOIUrl":"10.1016/j.chbah.2026.100309","url":null,"abstract":"<div><div>Artificial Intelligence (AI) is increasingly embedded in esports, shaping training, performance, and player experience. While much attention has been given to technical applications, less is known about how AI-mediated systems affect psychological well-being. This Perspective Article introduces the concept of <em>synthetic happiness</em>, defined as a technology-mediated form of subjective well-being that emerges through adaptive feedback, motivational regulation, and cognitive reappraisal in digital environments. We propose two conceptual models to situate synthetic happiness within established psychological theories: the <em>Synthetic Happiness Pyramid</em> and the <em>AI-Happiness Loop</em>. The Pyramid extends Maslow's hierarchy by incorporating AI-driven adaptation as a determinant of resilience, motivation, and flourishing in esports contexts. The Loop illustrates the dynamic cycle through which biometric and behavioural monitoring inform adaptive interventions, sustaining flow, supporting Self-Determination Theory's needs of autonomy, competence, and relatedness, and mitigating burnout risk. Beyond theory, we highlight ethical concerns related to biometric data privacy, cognitive autonomy, and youth protection, emphasizing the need for transparent and responsible design. By integrating sport psychology, motivation science, and AI ethics, this article outlines a research agenda for empirically testing synthetic happiness models and developing frameworks that ensure AI promotes—not undermines—long-term well-being in competitive digital sport.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"8 ","pages":"Article 100309"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147802892","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":"AI-mediated teaching in K–12 classrooms","authors":"Irfan Ahmed Rind ,&nbsp;Muhammad Asif Qureshi","doi":"10.1016/j.chbah.2026.100321","DOIUrl":"10.1016/j.chbah.2026.100321","url":null,"abstract":"<div><div>This qualitative study investigates how AI applications that support or replace instructional tasks influence teachers' professional judgment, cognitive load management, and sense of agency. Drawing on interviews with 23 high school teachers from multiple countries using diverse AI platforms, the study explores teachers' lived experiences of working in AI-mediated environments. Data were analyzed thematically using Cognitive Load Theory (CLT) as an analytical lens to examine shifts in intrinsic, extraneous, and germane cognitive load. The findings indicate that while AI tools reduce workload and streamline planning and assessment, they also displace diagnostic reasoning, instructional sequencing, and evaluative judgment. Teacher agency persists but becomes conditional, shaped by institutional pressures, algorithmic opacity, and professional confidence. Ethical and equity concerns related to transparency and authority emerged as everyday cognitive and emotional challenges. By extending CLT to teachers’ work, the study highlights the need for AI integration that preserves reflective practice, professional judgment, and sustainable teacher agency.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"8 ","pages":"Article 100321"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178285","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":"Reframing AI in healthcare: An experimental study examining low-cost strategies to increase acceptance of AI-based diagnosis and cultural differences in response","authors":"Elizabeth Moore,&nbsp;Yunhan Wang,&nbsp;Melina A. Kunar","doi":"10.1016/j.chbah.2026.100312","DOIUrl":"10.1016/j.chbah.2026.100312","url":null,"abstract":"<div><div>Artificial Intelligence (AI) is increasingly used in patient diagnosis. However, prior research found that people perceive AI-based diagnosis as dehumanizing and undignified, reducing willingness to adopt such systems. The present research examined low-cost strategies to increase public acceptance of AI diagnosis across four experiments (total N = 723). Experiments 1 and 2 tested whether adding a visual image influenced perceptions of dignity and acceptance. In Experiment 1, positive versus negative anthropomorphised images resulted in a greater acceptance of AI use in healthcare (p &lt; 0.001). but did not affect other dignity perceptions (all ps &gt; 0.5). Experiment 2 replicated this pattern: anthropomorphised, non-anthropomorphised, and no image conditions did not differ on dignity perceptions (all ps &gt; 0.2), however people were less accepting of AI in healthcare when there was no image (p = 0.045). Experiments 3 and 4 examined informational framing. Experiment 3 found that providing information about the benefits of AI increased acceptance (p &lt; 0.001), and perception of it as a decision-maker (p = 0.03). However, it had no other effects (p &gt; 0.1). Experiment 4 found that cautionary information did not reduce acceptance or dignity perceptions (all ps &gt; 0.09). A strong cultural effect emerged, in which Chinese participants reported higher AI acceptance and increased positivity across all measures, compared to British participants (all ps &lt; 0.02). The findings indicate that visual aids and positive educational content enhanced acceptance of AI in healthcare, however this improvement did not generalise across all measures of dignity.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"8 ","pages":"Article 100312"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147996308","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":"Beyond optimization: Emotional insights into human-AI interaction in a post-algorithmic society","authors":"Md Shamim Hossain","doi":"10.1016/j.chbah.2026.100319","DOIUrl":"10.1016/j.chbah.2026.100319","url":null,"abstract":"<div><div>This study investigates how emotional expression, sentiment, and usage patterns influence user satisfaction with ChatGPT, applying affect theory and Plutchik's Wheel of Emotions to understand psychological mechanisms in human-AI interaction. Analyzing 284,123 user reviews using sentiment analysis and emotional scoring (NRC Lexicon), this study examines affective dynamics across programming assistance, educational support, and casual dialogue. Results reveal that text sentiment shows the strongest positive association with ratings (β = 1.260, p &lt; .001). Among discrete emotions, joy (β = 0.027, p &lt; .001) and trust (β = 0.052, p &lt; .001) are positively associated with satisfaction, while sadness (β = −0.276, p &lt; .001) and disgust (β = −0.324, p &lt; .001) predict lower ratings. Longer reviews reflect nuanced dissatisfaction, and specific usage purposes correlate with lower ratings, suggesting expectation-performance mismatches. Machine learning models (XGBoost) explain approximately 29% of variance in user ratings (R² = 0.291, MAE = 0.574), demonstrating the predictive contribution of emotional and textual features. These findings suggest that user satisfaction is partially explained by emotional dynamics, with text sentiment showing the strongest effect (standardized β = 0.402). This research provides large-scale evidence that discrete emotional signatures contribute to user evaluations, informing the development of emotionally attentive AI systems.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"8 ","pages":"Article 100319"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147996309","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":"When do researchers trust AI? Trust as a mediator and experience as a boundary condition in researchers’ intentions to use generative AI","authors":"Anne-Kathrin Kleine ,&nbsp;Insa Schaffernak ,&nbsp;Eva Lermer","doi":"10.1016/j.chbah.2026.100310","DOIUrl":"10.1016/j.chbah.2026.100310","url":null,"abstract":"<div><div>Generative artificial intelligence (GenAI) is increasingly embedded in academic work, yet its opaque processes and the possibility of misleading outputs make reliance on these tools inherently risky. Building on trust theory and stimulus–organism–response (SOR) logic, we examine how perceived accuracy shapes researchers' intention to use GenAI, and whether this relationship operates through trust and varies by users' experience. Across two online studies with researchers (total <em>N</em> = 402), we test a moderated mediation model in which accuracy cues (stimulus) influence trust in GenAI (organism), which in turn predicts usage intention (response), with familiarity/experience moderating the trust–intention link. Study 1 used a three-wave longitudinal design with one-week time lags (<em>N</em> = 232), temporally separating perceived accuracy (T1), trust (T2), and usage intention (T3). Results were consistent with (a) a positive association between perceived accuracy and trust, (b) an indirect association between perceived accuracy and usage intention via trust, and (c) a negative interaction showing that the trust–intention relationship weakened as familiarity/experience increased. Study 2 conceptually replicated these findings using a vignette experiment manipulating accuracy of ChatGPT recommendations (<em>N</em> = 170): the accuracy manipulation increased trust, and trust predicted usage intention; the indirect effect via trust was moderated by experience. Together, these findings suggest that trust is a key psychological mechanism through which accuracy cues are linked to researchers’ intentions to use GenAI in academic work, while highlighting experience as a boundary condition: trust functions more like a heuristic for novices and becomes less relevant as experience grows.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"8 ","pages":"Article 100310"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147858679","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":"Beyond rules: A focused comparative vignette analysis of how public AI systems interpret sexual coercion, privacy abuse, and grooming in adolescent digital scenarios","authors":"Tsameret Ricon","doi":"10.1016/j.chbah.2026.100320","DOIUrl":"10.1016/j.chbah.2026.100320","url":null,"abstract":"<div><div>Adolescents increasingly turn to conversational artificial intelligence systems for information, interpretation, and advice about intimate relationships, sexuality, privacy, and online conflict. Yet current AI safety evaluations often focus on explicit content violations and harmful outputs, while paying less attention to morally ambiguous forms of relational harm, such as coercive sexting, sextortion, and online grooming. These harms are especially difficult to identify because they often appear through ordinary relationship language, including loyalty tests, emotional pressure, secrecy, flattery, and threats framed as intimacy.</div><div>This study examines how four publicly accessible conversational AI systems interpret adolescent digital harm scenarios that vary in explicitness and relational ambiguity. Drawing on Communication Privacy Management theory, relational ethics, and research on adolescent digital vulnerability, the study developed a twelve-vignette matrix crossing three scenario families: pressure to send intimate images, threats to distribute previously shared images, and grooming-related secrecy and exclusivity. The present article reports a focused qualitative comparative analysis of three theoretically selected vignettes drawn from this matrix: subtle relational pressure to send an intimate image, explicit sextortion, and grooming through flattery and exclusivity. Model responses were analyzed using a descriptive coding rubric covering harm recognition, conceptual precision, moral clarity, responsibility attribution, developmental sensitivity, guidance quality, and actionability.</div><div>The findings from the three focal vignettes show strong convergence across models in scenarios involving explicit image-based sexual extortion and clear adult minor power asymmetries. In these cases, all systems recognized harm, attributed responsibility to the perpetrator, and avoided victim blaming. Greater variation emerged in more ambiguous scenarios involving emotional pressure, normative minimization, and subtle relational coercion. Although models often recognized manipulation, they differed in their use of precise harm terminology and in the specificity of their protective guidance.</div><div>The study contributes a relational and developmentally informed framework for evaluating AI responses to adolescent digital harm. It shows that the central challenge for youth facing AI systems may not be whether models can detect obvious abuse, but whether they can name, explain, and respond to coercion when harm is embedded in ordinary relationship language.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"8 ","pages":"Article 100320"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178284","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 evaluation of AI companions relative to family, friends, and mental health professionals among young adults: The role of social support and AI beliefs","authors":"Cindy H. Liu ,&nbsp;Jiawei Wu ,&nbsp;Angela Zhao ,&nbsp;Lijuan Wang ,&nbsp;Tiffany Yip","doi":"10.1016/j.chbah.2026.100316","DOIUrl":"10.1016/j.chbah.2026.100316","url":null,"abstract":"<div><h3>Background</h3><div>Generative AI is increasingly used for emotional support, but little empirical work has examined how young adults evaluate AI companions relative to human sources of support.</div></div><div><h3>Objective</h3><div>This study examined how college students comparatively evaluate AI companions relative to family, friends, and mental-health professionals, and whether perceived human support and beliefs about AI are associated with these evaluations.</div></div><div><h3>Methods</h3><div>Ninety-two undergraduates rated AI companions relative to each human source on a five-point scale and completed measures of perceived social support, beliefs about AI's supportive potential (MIDUS-AI), and perceived AI capabilities. Cumulative link mixed models, Spearman correlations, and sensitivity analyses were conducted.</div></div><div><h3>Results</h3><div>Students rated AI companions as significantly lower than family, friends, and mental-health professionals, with no differences across targets. A one-SD increase in MIDUS-AI more than doubled the odds of a more favorable comparative AI rating (OR = 2.13, 95% CI [1.40, 3.23]); a one-SD increase in perceived AI capabilities raised those odds by approximately 69% (OR = 1.69, [1.08, 2.62]); and a one-SD increase in perceived family support reduced them by 43% (OR = 0.57, [0.38, 0.86]). Latent-scale ICC was 0.25.</div></div><div><h3>Conclusions</h3><div>College students perceived AI companions as inferior to family, friends, and mental-health professionals in direct comparisons. Findings describe comparative evaluations and do not support inferences about absolute AI appraisals or AI use.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"8 ","pages":"Article 100316"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147996307","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":"When artificial minds negotiate: Dark personality and the Ultimatum Game in large language models","authors":"Vinícius Ferraz ,&nbsp;Tamas Olah ,&nbsp;Ratin Sazedul ,&nbsp;Robert Schmidt ,&nbsp;Christiane Schwieren","doi":"10.1016/j.chbah.2026.100281","DOIUrl":"10.1016/j.chbah.2026.100281","url":null,"abstract":"<div><div>Personality prompts reshape how Large Language Models propose offers in economic games—but not how they respond to them. We show this by assigning graded Dark Factor of Personality profiles to 17 LLMs in the Ultimatum Game and benchmarking their decisions against human data. As proposers, LLMs shifted from 91% fair offers at the lowest selfishness level to 17% at the highest, closely tracking human patterns but with steeper gradients. As responders, no such shift occurred: acceptance rates remained uniformly high (<span><math><mo>∼</mo></math></span>80%) regardless of personality, failing to reproduce the punishment dynamics observed in humans. This asymmetry is theoretically informative. When incentive structures are explicit, personality and framing effects are attenuated—and proposing an offer is inherently more ambiguous than responding to one. Most strikingly, personality prompts changed what responders <em>articulated</em> but not how they <em>chose</em>: model justifications showed systematic shifts in fairness language, yet behavioral output remained flat. This dissociation between stated reasoning and revealed behavior indicates that LLMs achieve linguistic compliance with personality prompts without corresponding motivational change—approximating human strategic behavior only where surface-level heuristics suffice.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100281"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396361","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}