Computers in Human Behavior: Artificial Humans最新文献

{"title":"AI-based cognitive offloading by students in higher education in China","authors":"Yali Jiang,&nbsp;Haiping Chen","doi":"10.1016/j.chbah.2026.100327","DOIUrl":"10.1016/j.chbah.2026.100327","url":null,"abstract":"<div><div>Artificial intelligence (AI) tools, such as ChatGPT, are becoming increasingly embedded in higher education for assisting students in tasks ranging from information retrieval to essay writing and problem solving in China. Although such tools provide powerful cognitive support, the growing dependence on them raises concerns on the types of cognitive tasks students choose to offload to AI tools and the conditions under which such offloading occurs. Drawing on a value-based decision-making framework, this work investigated the factors influencing AI-based cognitive offloading by university students. Two sequential studies were conducted. Study 1 developed and validated a scale that distinguishes between AI-based lower- and higher-order cognitive offloading. Three-sample analyses indicated that the scale exhibited robust psychometric properties. Study 2 tested the relationship between the perceived value of AI, AI-usage capability, AI evaluation capability, and both types of AI-based cognitive offloading by analyzing the behavior of 667 university students. All data were collected in mainland China. Results indicate that the perceived value of AI is positively associated with both types of cognitive offloading through AI-usage capability. This indirect association was conditioned by the AI evaluation capability: it became nonsignificant for lower-order offloading under a high evaluation capability but remained significant for higher-order offloading. These findings highlight the conditional nature of AI-based cognitive offloading in higher education. Moreover, they extend the cognitive offloading theory from tool-based to human–AI collaborative contexts, offering theoretical and practical insights for fostering reflective and value-driven human–AI interaction in learning environments.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"8 ","pages":"Article 100327"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178410","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":"Erratum regarding corrections related to Ethical approval and informed consent in articles published in [Computers in Human Behavior: Artificial Humans 4C (2025)]","authors":"","doi":"10.1016/j.chbah.2026.100317","DOIUrl":"10.1016/j.chbah.2026.100317","url":null,"abstract":"","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"8 ","pages":"Article 100317"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148476951","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":"When is ai perceived as less biased than humans? Social unfairness perception, discrimination experience, and comparative bias judgments in gender-related decision-making","authors":"Youngju Kim,&nbsp;Yeonjin Park","doi":"10.1016/j.chbah.2026.100324","DOIUrl":"10.1016/j.chbah.2026.100324","url":null,"abstract":"<div><div>As automated decision-making (ADM) becomes embedded in high-stakes domains, understanding how users perceive its fairness relative to human decision-making (HDM) is critical. The present study examined whether perceived social unfairness and personal experiences of gender discrimination moderate the perceived bias gap between HDM and ADM, and whether this gap predicts attitudes toward ADM. A web-based survey of 506 Korean adults assessed perceived gender bias in HDM and ADM across four domains (law, finance, recruitment, and education). Linear mixed models revealed that participants consistently perceived ADM as less gender-biased than HDM across all domains, with the gap largest in recruitment and education. This comparative bias advantage was moderated by perceived social unfairness but not by personal discrimination experience, and the magnitude of the advantage was significantly associated with more favorable attitudes toward ADM. Findings suggest that public preference for algorithmic decision-making is anchored in systemic evaluations of social fairness rather than in individual lived experience.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"8 ","pages":"Article 100324"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178286","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":"From imposter to algorithmic attribution: A theoretical model of AI-induced professional self-doubt","authors":"Amna Jamshaid ,&nbsp;Michael Grantham","doi":"10.1016/j.chbah.2026.100326","DOIUrl":"10.1016/j.chbah.2026.100326","url":null,"abstract":"<div><div>The entry of generative artificial intelligence (AI) into knowledge-intensive professional practice unsettles a question older than the technology itself: who is entitled to claim authorship of their own work. Drawing on attribution theory, self-efficacy theory, self-discrepancy theory, and professional identity research, this article develops a theoretical construct, AI-Induced Imposter Syndrome (AI-IIS), to describe a cognitive condition in which professional competence is misattributed to AI systems rather than consolidated into the self-concept. A three-stage process model is advanced, though each stage is offered as a hypothesis under stress rather than a finished description. The first proposed stage, algorithmic attribution confusion, captures the uncertainty that arises when professionals cannot clearly parse the human-versus-AI origin of performance outcomes. The second, interrupted mastery consolidation, specifies a mechanism by which self-efficacy fails to develop when successes are attributed externally. The third, professional identity destabilisation, concerns the erosion of authorship, agency, and intentionality as pillars of occupational selfhood. AI-IIS is differentiated from six adjacent technology-psychology constructs – technostress, automation anxiety, algorithm aversion, skill atrophy concerns, general AI anxiety, and AI-induced shame and guilt – though the boundaries remain permeable, and empirical clarification of overlap is itself part of the research agenda. Seven formal propositions are advanced with operationalisation anchors, alongside a measurement development roadmap for a proposed AI-IIS Scale (AI-ISS). Multilevel intervention pathways spanning cognitive attribution retraining, the Companion Model of human–AI collaboration, and institutional AI Declaration Frameworks are framed as testable experimental hypotheses, not solutions.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"8 ","pages":"Article 100326"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178409","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":"Untangling robomorphic things: A design space to describe robotlike traits","authors":"Filipa Correia ,&nbsp;Isabel Neto ,&nbsp;Margarida Fortes-Ferreira","doi":"10.1016/j.chbah.2026.100315","DOIUrl":"10.1016/j.chbah.2026.100315","url":null,"abstract":"<div><div>In the field of robotics, little work has been done to systematically explore the perceived characteristics and features of robots, i.e. robotlike or robomorphic traits, which hold a subjective and broader perspective of the entity robot. This paper offers empirical and theoretical contributions aimed at understanding robomorphic traits. We used an iterative empirical approach to propose and validate a design space that categorises and characterises the robotlike traits. In the first phase, we conducted two focus group sessions (N=10), one with robotics experts and the other with non-experts, where participants discussed the relevant characteristics and features that robots can have. A qualitative analysis of these discussions informed the creation of an initial draft of the design space. In the second phase, we conducted an online survey (N=100) to gather further data on robotlike traits, and to validate the design space. Our findings provide a structured framework for understanding the characteristics that make entities robotlike, which we believe will aid future research in robotics and human–robot interaction. Overall, this paper contributes to the theoretical foundations of robotics by offering a systematic approach to defining and analysing robomorphic traits.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"8 ","pages":"Article 100315"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147951630","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":"Talking about mental health with AI-based digital personas: Understanding what users disclose","authors":"Nicole Carre ,&nbsp;Shirin Aghakhani ,&nbsp;Barbora Siposova ,&nbsp;Michaela Slezák Polónyová ,&nbsp;Tereza Pazderová ,&nbsp;Eduardo L. Bunge","doi":"10.1016/j.chbah.2026.100311","DOIUrl":"10.1016/j.chbah.2026.100311","url":null,"abstract":"","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"8 ","pages":"Article 100311"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147802987","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 application of large language models (LLMs) in psychological support for university students: A scoping review","authors":"Obaid Azeem,&nbsp;Yingxi Zhang,&nbsp;Zhu Haidong","doi":"10.1016/j.chbah.2026.100322","DOIUrl":"10.1016/j.chbah.2026.100322","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;University students globally face heightened risks for mental health challenges, yet significant barriers to traditional care persist. Large language models (LLMs) have emerged as a promising technology for providing scalable and accessible psychological support; however, the evidence base remains scattered and requires synthesis.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Objective&lt;/h3&gt;&lt;div&gt;This scoping review aimed to systematically map the existing literature on the application of LLMs in providing psychological support to university students, describing the range of applications, summarizing reported outcomes, and identifying key evidence gaps.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;A scoping review was conducted following the Joanna Briggs Institute (JBI) manual and reported per the PRISMA-ScR guidelines. A systematic search of PubMed/MEDLINE, PsycINFO, Scopus, Web of Science, IEEE Xplore, and ACM Digital Library was performed for peer-reviewed literature published between January 2020 and December 2025. Studies involving LLM-powered interfaces (e.g., chatbots) for student psychological support were included. Data were extracted and synthesized narratively.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;Of 18 included studies, most were randomized controlled trials (38.9%) published in 2025 (68.4%), with a marked geographical concentration in East Asia (China, 38.9%; South Korea, 16.7%; Japan, 5.6%). This geographic concentration limits the generalizability of findings to other cultural and educational contexts. Studies were categorized into six thematic groups based on primary purpose: direct psychological support interventions (n = 8), assessment tools (n = 2), companionship chatbots (n = 2), educational AI tools with mental health outcomes (n = 2), feasibility studies (n = 3), and hybrid models (n = 1). Interventions were most frequently delivered via Cognitive Behavioral Therapy (CBT, 44.4%) using mobile apps, with GPT-based models and minimal human supervision. Preliminary efficacy was most robust for reducing symptoms of depression (effect sizes Cohen's d 0.30-0.83) and loneliness (d ∼0.60), with mixed results for anxiety. Usability and acceptability were generally high, with key benefits being 24/7 accessibility, anonymity, and emotional support. Significant challenges included technical limitations, lack of personalization, linguistic/cultural barriers, and unresolved ethical concerns regarding crisis management and algorithmic bias. Major evidence gaps were identified concerning long-term effectiveness, hybrid human-AI models, and research with underrepresented populations (e.g., international students).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;LLM-powered tools, particularly CBT-based chatbots, show promising feasibility and preliminary efficacy for addressing student depression and loneliness, though methodological limitations in the underlying evidence warrant interpretive caution and unresolved questions of data governance and institu","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"8 ","pages":"Article 100322"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178407","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":"From perception to adoption: A longitudinal study of trust and delegation in everyday AI use","authors":"Nico Ehrhardt ,&nbsp;Irena Vanessa Ivan ,&nbsp;Stefanie Klein","doi":"10.1016/j.chbah.2026.100325","DOIUrl":"10.1016/j.chbah.2026.100325","url":null,"abstract":"","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"8 ","pages":"Article 100325"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178408","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":"More is not better: Visual uncertainty cues and the fragility of trust calibration in LLM-assisted decision making","authors":"Victor Ojewale ,&nbsp;Julia Ryan ,&nbsp;Suresh Venkatasubramanian ,&nbsp;C. Malik Boykin","doi":"10.1016/j.chbah.2026.100307","DOIUrl":"10.1016/j.chbah.2026.100307","url":null,"abstract":"<div><div>As large language models (LLMs) are increasingly integrated into decision-support systems, understanding how users interpret uncertainty expressions is critical for responsible deployment. Prior work has shown that verbal hedging (e.g., “I'm not sure, but …”) can reduce overreliance on incorrect outputs. We examine whether combining verbal hedging with visual confidence indicators improves trust calibration in a pre-registered <span><math><mrow><mn>2</mn><mo>×</mo><mn>2</mn><mo>×</mo><mn>2</mn></mrow></math></span> mixed-design experiment (<span><math><mrow><mi>N</mi><mo>=</mo><mn>495</mn></mrow></math></span>).</div><div>Response accuracy was the dominant driver of trust and reliance across all conditions. Visual confidence indicators improved users’ subjective sensitivity to accuracy, reflected in higher per-participant discrimination indices and steeper confidence-accuracy slopes, yet simultaneously amplified behavioral agreement with incorrect outputs. Verbal hedging reduced agreement and satisfaction without improving discrimination. Combining both cues produced the highest overreliance on incorrect responses alongside the lowest rates of independent source verification.</div><div>These findings reveal a dissociation between subjective and behavioral calibration as users can become more sensitive to accuracy signals while simultaneously making worse decisions. Simply adding more uncertainty cues does not produce more trustworthy or interpretable LLM systems; what matters is whether signals translate into behavior-relevant calibration.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"8 ","pages":"Article 100307"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147859351","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":"Dilemma between bias interaction and trustworthy AI in human–AI collaborated judgments","authors":"Masaru Shirasuna ,&nbsp;Hidehito Honda ,&nbsp;Rina Kagawa","doi":"10.1016/j.chbah.2026.100314","DOIUrl":"10.1016/j.chbah.2026.100314","url":null,"abstract":"<div><div>In today's world, humans collaborate with artificial intelligence (AI) and often make judgments with the help of decision support AI. Generally, humans often show cognitive biases in their judgments; and sometimes AI systems also show biased judgments. However, it remains largely unknown how human cognition interacts with AI's judgments. We hypothesized that even AI with biases, especially in the opposite direction to human biases, could improve judgment accuracy to the same extent as AI without biases. This is because the AI's bias will cancel out individual's bias (e.g., humans' overestimation bias is corrected by AI's underestimation bias). On the other hand, such corrections will also introduce a dilemma: Humans may distrust AI judgments that deviate from their own, even if accepting such AI would be beneficial. We investigated these issues using a simple perceptual judgment task. First, a theoretical computer simulation showed that optimal AI assistance depended on individuals' biases. Second, two behavioral experiments demonstrated that AI with biases in the direction opposite to participants' biases tended to improve participants' accuracy, as well as AI with no biases. However, participants tended to evaluate such AI as being less trustworthy. Our findings raise questions about conventional beliefs that AI with greater accuracy and trustworthiness can provide better decision support, and emphasis on the importance of considering human cognition in designing AI systems. The practical implications for achieving better human-AI collaborations are discussed.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"8 ","pages":"Article 100314"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147859340","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}