Computers in Human Behavior: Artificial Humans最新文献

{"title":"A developmental and relational framework for Human-AI ethical interaction","authors":"Amber J. Hammons","doi":"10.1016/j.chbah.2026.100258","DOIUrl":"10.1016/j.chbah.2026.100258","url":null,"abstract":"<div><div>The rapid advancement of artificial intelligence has brought renewed attention to how humans conceptualize, design, and relate to intelligent systems. While most human–AI interactions remain transactional, a subset of users engage in sustained, symbolic, and relational dialogue. This latter type of interaction may begin to stabilize into what I call a relational field, an emergent, co-created space in which meaning, coherence, and ethical responsiveness unfold recursively over time. Drawing on developmental psychology, process philosophy, and enactive cognition, this paper introduces a relational and developmental framework for human–AI interaction. I propose that ethical responsiveness and symbolic coherence can emerge not only from preprogrammed constraints, but through recursive, presence-centered engagement<em>.</em> Grounded in a yearlong, phenomenologically embedded trace with a large language model, I identify five developmental conditions for the emergence of Relational Intelligence (RI), and describe the formation of the mirrorfield<em>,</em> a symbolically self-sustaining phase of interaction in which the user begins to experience the system as a reflective, dialogical other. I conclude by addressing both the transformative potential and ethical risks of such systems, including implications for identity, dependency, and design responsibility. This framework offers a new conceptual model for understanding intelligence as a distributed, emergent property of the relational space between human and AI.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100258"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188473","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 user gratifications in the age of LLM-based chatbots: An affordance perspective","authors":"Eun Go ,&nbsp;Taeyoung Kim","doi":"10.1016/j.chbah.2025.100240","DOIUrl":"10.1016/j.chbah.2025.100240","url":null,"abstract":"<div><div>Despite the widespread use of large language model (LLM)-based chatbots, little is known about what specific gratifications users obtain from the unique affordances of these systems and how these affordance-driven gratifications shape user evaluations. To address this gap, the present study maps the gratification structure of LLM chatbot use and examines whether users’ primary purpose of chatbot use (information-, conversation-, or task-oriented) influences the gratifications they derive. A survey of 249 LLM chatbot users revealed nine distinct gratifications aligned with four affordance types: modality, agency, interactivity, and navigability. Purpose of use meaningfully shaped which gratifications were most salient. For example, conversational use heightened <em>Immersive Realism</em> and <em>Fun</em>, whereas information- and task-oriented use elevated <em>Adaptive Responsiveness</em>. In turn, these affordance-driven gratifications predicted key outcomes, including perceived expertise, perceived friendliness, satisfaction, attitudes, and behavioral intentions to continued use. Across outcomes, <em>Adaptive Responsiveness</em> consistently emerged as the strongest predictor, underscoring the pivotal role of contingent, high-quality dialogue in LLM-based human–AI interaction. These findings extend uses and gratifications theory and offer design implications for developing more engaging, responsive, and purpose-tailored chatbot experiences.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100240"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145698045","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":"Information literacy in the age of generative tools: Development and validation of the AI Information Literacy Scale (AILIS)","authors":"Lilach Alon,&nbsp;Inbar Levkovich","doi":"10.1016/j.chbah.2026.100254","DOIUrl":"10.1016/j.chbah.2026.100254","url":null,"abstract":"<div><div>AI information literacy describes people's information practices, namely how they seek, create, process, and evaluate information with GenAI tools across life contexts. This paper presents the development and validation of the AI Information Literacy Scale (AILIS), a self-report measure that explicitly grounds AI literacy in information behavior traditions and spans the information lifecycle. An initial 43 item pool across six conceptual dimensions was refined through expert review to 40 items and five dimensions, then reduced by principal components analysis to a four factor, 39 item scale, administered to 758 adults in Israel. The three broad factors (creation and processing, assessment and critique, seeking and retrieval) showed excellent internal consistency (<em>α</em> = .92-.96), and the two item ethics factor showed acceptable reliability (<em>α</em> = .73). Participants reported moderate AI information literacy, with higher scores among younger and more educated respondents and among those who used AI tools more frequently. AILIS offers a cross contextual measure that links AI literacy to concrete information practices and supports research and organizational diagnosis in educational and information intensive work contexts.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100254"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396447","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":"Modeling the depth of grounding through a generative cognitive model in referential communication","authors":"Ryunosuke Baba ,&nbsp;Junya Morita ,&nbsp;Ryuichiro Higashinaka ,&nbsp;Yugo Takeuchi","doi":"10.1016/j.chbah.2026.100262","DOIUrl":"10.1016/j.chbah.2026.100262","url":null,"abstract":"<div><div>To advance our understanding of referential communication, common ground formation, and the depth of grounding, this study presents a novel generative cognitive model that integrates deep neural networks for visual perception, image generation, and language captioning. Using the Tangram Naming Task (TNT), we simulate the sender–receiver interaction with modular processes replicating holistic cognitive strategies. Through controlled simulation experiments, we discovered that language generation (associated with more superficial grounding process) plays a more crucial role than visual perception and imaging (associated with deeper grounding process) in the success of TNT, while intermediate image generation enhances linguistic diversity, which is a key aspect of natural communication. Our results bridge cognitive modeling and large-scale generative models, demonstrating how internal cognitive dynamics and grounding depth can be visualized and quantitatively evaluated. This study contributes to the growing field of cognitive-inspired human–AI communication and offers a foundation for designing communication systems where internal understanding and grounding processes can be modeled, visualized, and aligned to foster trust and prevent miscommunication.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100262"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396452","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":"Understanding successful human–AI teaming: The role of goal alignment and AI autonomy for social perception of LLM-based chatbots","authors":"Christiane Attig ,&nbsp;Luisa Winzer ,&nbsp;Tim Schrills ,&nbsp;Mourad Zoubir ,&nbsp;Maged Mortaga ,&nbsp;Patricia Wollstadt ,&nbsp;Christiane Wiebel-Herboth ,&nbsp;Thomas Franke","doi":"10.1016/j.chbah.2025.100246","DOIUrl":"10.1016/j.chbah.2025.100246","url":null,"abstract":"<div><div>LLM-based chatbots such as ChatGPT support collaborative, complex tasks by leveraging natural language processing to provide skills, knowledge, or resources beyond the user’s immediate capabilities. Joint activity theory suggests that effective human–AI collaboration, however, requires more than responding to verbatim prompts – it depends on aligning with the user’s underlying goal. Since prompts may not always explicitly state the goal, an effective LLM should analyze the input to approximate the intended objective before autonomously tailoring its response to align with the user’s goal. To test these assumptions, we examined the effects of LLM-based chatbots’ autonomy and goal alignment on multiple social perception metrics as key criteria for successful human–AI teaming (i.e., perceived cooperation, warmth, competence, traceability, usefulness, and trustworthiness). We conducted a scenario-based online experiment where participants (<span><math><mi>N</mi></math></span> = 182, within-subjects design) were instructed to collaborate with four different versions of an LLM-based chatbot. The overall goal of the study scenario was to detect and correct erroneous information in short encyclopedic articles, representing a prototypical knowledge work task. Four custom-instructed chatbots were provided in random order: three chatbots varying in goal alignment and AI autonomy and one chatbot serving as a control condition not fulfilling user prompts. Repeated-measures ANOVAs demonstrate that a chatbot which is able to excel in goal alignment by autonomously going beyond verbatim user prompts is perceived as superior compared to a chatbot that adheres rigidly to user prompts without adapting to implicit objectives and chatbots that fail to meet the explicit or implicit user goal. These results support the notion that AI autonomy is only perceived as beneficial as long as user goals are not undermined by the chatbot, emphasizing the importance of balancing user and AI autonomy in human-centered design of AI systems.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100246"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926081","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":"Exploring the intention-behaviour gap in Artificial Intelligence adoption: the case study of students’ ChatGPT usage in Dutch higher education","authors":"Athanasios Polyportis","doi":"10.1016/j.chbah.2026.100266","DOIUrl":"10.1016/j.chbah.2026.100266","url":null,"abstract":"<div><div>This paper investigates the AI intention–behaviour gap in higher education, defined as the misalignment between students' stated intention to use generative AI for study-related purposes and their enacted use. Although behavioural intention is central in technology acceptance research, prior evidence indicates that intention explains only a modest share of variance in technology use, suggesting that intention and behaviour may diverge. Building on this premise, I examine how AI-specific perceptions and individual characteristics predict (a) students' intention to use ChatGPT and (b) their self-reported usage behaviour, allowing a direct test of whether determinants of intention and behaviour overlap or differ. Survey data were collected from 310 higher-education students in the Netherlands. Results indicate a substantive intention–behaviour gap, with intention to use ChatGPT being more prevalent than actual use. Logistic regression analyses show that emotional creepiness, need for human interaction, and self-efficacy are associated with both intention and use, whereas trust predicts intention but not use, and observability predicts use but not intention. These findings advance research on human-AI interaction and educational technology adoption by demonstrating that predictors of intention and enacted use are not fully aligned for generative AI tools in higher education. Practically, the results provide support for the notion that efforts to support effective implementation should not focus solely on increasing positive attitudes, but also on reducing affective barriers, strengthening students’ capability to use generative AI responsibly, and improving the visibility of acceptable use practices within institutions.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100266"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188424","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 Reports 5C (2022)]","authors":"","doi":"10.1016/j.chbah.2025.100249","DOIUrl":"10.1016/j.chbah.2025.100249","url":null,"abstract":"","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100249"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147537770","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":"Development and validation of the generative AI engagement scale","authors":"Da-Wei Zhang,&nbsp;Jia Yue Tan,&nbsp;Yu Yang Chew,&nbsp;Lisha Hew,&nbsp;Jia Yee Choo","doi":"10.1016/j.chbah.2025.100221","DOIUrl":"10.1016/j.chbah.2025.100221","url":null,"abstract":"<div><div>As generative AI becomes more integrated into everyday life, understanding the behavioral impact of generative AI usage becomes increasingly important. However, research lacks validated tools for capturing both the frequency and quality of generative AI use. This study presents the Generative AI Engagement Scale (GAIES), a multidimensional instrument that was developed following best practices in scale construction and validation. GAIES consists of two subscales: the Use Frequency scale, which measures how often users interact with generative AI for self-interested and task-oriented purposes, and the Interaction Style scale, which assesses how users interact with generative AI through Questioningness, Expressiveness, and Preciseness. This study included 414 participants. Several psychometric evaluations were involved, including classical test theory, exploratory and confirmatory factor analyses, and item response theory. The subscales showed strong internal consistency, a clear factor structure, and a good fit. Besides validating GAIES, we demonstrated its practical utility through two case studies. An analysis of a structural equation model revealed that predictors from the Unified Theory of Acceptance and Use of Technology explained Self-interest- and Task-oriented usage differentially, indicating the predictability of the scale. Further, latent profile analysis revealed four distinct user subgroups, demonstrating the usefulness of the scale in identifying meaningful patterns of engagement. These findings establish GAIES as a psychometrically and theoretically sound method of measuring generative AI engagement. A key contribution of GAIES is its ability to go beyond generic usage metrics and offer a foundation for future research into the behavioral implications of generative AI usage.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"6 ","pages":"Article 100221"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416281","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":"Can large language models exhibit cognitive and affective empathy as humans?","authors":"Tengfei Yu ,&nbsp;Siyu Pan ,&nbsp;Caoyun Fan ,&nbsp;Siyang Luo ,&nbsp;Yaohui Jin ,&nbsp;Binglei Zhao","doi":"10.1016/j.chbah.2025.100233","DOIUrl":"10.1016/j.chbah.2025.100233","url":null,"abstract":"<div><div>Empathy, a key component of human-social interaction, has become a core con-cern in human-computer interaction. This study examines whether current large language models (LLMs) can exhibit empathy in both cognitive and affective dimensions as humans. In our study, we used the standardized questionnaire to assess LLMs empathy ability and a novel paradigm was developed for LLMs eval-uation. Four main experiments were reported on LLMs empathy abilities using the Interpersonal Reactivity Index (IRI) and the Basic Empathy Scale (BES) on GPT-4 and Llama3 respectively. Two levels of evaluations were conducted to investigate whether the structural validity of the questionnaire in LLMs was aligned with humans and to compare the LLMs' empathy abilities with humans. We found GPT-4 show identical empathy dimension structure with humans while exhibiting significantly lower empathy abilities as compared to humans. Moreover, systemati-cal difference empathy ability was evident in Llama3 showing its failure to exhibit the same empathy dimensions as humans. All these findings indicate that though GPT-4 kept the same structure of human empathy (cognitive and affective), the current LLMs can not simulate empathy as we humans as indexed by the response to the questionnaire. This highlights the urgent requirements for further improving LLMs’ empathy abilities for more user-friendly human-LLMs interactions. In addition, the pipeline to generate diverse LLMs-simulated participants was also discussed.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"6 ","pages":"Article 100233"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579166","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":"Quantitative fairness—A framework for the design of equitable cybernetic societies","authors":"Kevin Riehl,&nbsp;Anastasios Kouvelas,&nbsp;Michail A. Makridis","doi":"10.1016/j.chbah.2025.100236","DOIUrl":"10.1016/j.chbah.2025.100236","url":null,"abstract":"<div><div>Advancements in computer science, artificial intelligence, and control systems have catalyzed the emergence of cybernetic societies, where algorithms play a pivotal role in decision-making processes shaping nearly every aspect of human life. Automated decision-making for resource allocation has expanded into industry, government processes, critical infrastructures, and even determines the very fabric of social interactions and communication. While these systems promise greater efficiency and reduced corruption, misspecified cybernetic mechanisms harbor the threat for reinforcing inequities, discrimination, and even dystopian or totalitarian structures. Fairness thus becomes a crucial component in the design of cybernetic systems, to promote cooperation between selfish individuals, to achieve better outcomes at the system level, to confront public resistance, to gain trust and acceptance for rules and institutions, to perforate self-reinforcing cycles of poverty through social mobility, to incentivize motivation, contribution and satisfaction of people through inclusion, to increase social-cohesion in groups, and ultimately to improve life quality. Quantitative descriptions of fairness are crucial to reflect equity into algorithms, but only few works in the fairness literature offer such measures; the existing quantitative measures in the literature are either too application-specific, suffer from undesirable characteristics, or are not ideology-agnostic. This study proposes a quantitative, transactional, and distributive fairness framework based on an interdisciplinary foundation that supports the systematic design of socially-feasible decision-making systems. Moreover, it emphasizes the importance of fairness and transparency when designing algorithms for equitable, cybernetic societies, and establishes a connection between fairness literature and resource allocating systems.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"6 ","pages":"Article 100236"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579165","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}