International Journal of Human-Computer Studies最新文献

{"title":"The mediating effects of emotions on trust through risk perception and system performance in automated driving","authors":"Lilit Avetisyan ,&nbsp;Emmanuel Abolarin ,&nbsp;Vanik Zakarian ,&nbsp;X. Jessie Yang ,&nbsp;Feng Zhou","doi":"10.1016/j.ijhcs.2025.103642","DOIUrl":"10.1016/j.ijhcs.2025.103642","url":null,"abstract":"<div><div>Trust in automated vehicles (AVs) has traditionally been explored through a cognitive lens, but growing evidence highlights the significant role emotions play in shaping trust. This study moves beyond correlation to formally test the mechanisms through which emotions mediate the relationship between real-time AV performance and trust. We conducted an experimental study with 70 participants (42 male, 28 female) who viewed real-life AV recordings operating with or without errors, coupled with varying levels of risk information (high, low, or none). Participants reported their anticipated emotional responses using 19 discrete emotion items, while trust was assessed through dispositional, learned, and situational trust measures. Through factor analysis, 4 key emotional components were extracted, namely hostility, confidence, anxiety, and loneliness, that were influenced by risk perception and AV performance. Using mediation analysis, the extent to which four emotional factors explain the effect of AV performance on trust was quantified. The results show that real-time AV behavior is more influential on trust than pre-existing risk perceptions, indicating trust in AVs might be more experience-based than shaped by prior beliefs. The mediation analysis revealed major asymmetry in the power of emotional mediators: confidence emerged as the primary psychological pathway to trust, mediating 46.7% of the performance–trust effect. In contrast, negative emotions showed substantially weaker mediating effects. Hostility (11.3%) and anxiety (17.7%) were significant but substantially weaker negative mediators, while loneliness did not significantly mediate the relationship between AV performance and trust. Linear mixed modeling supported these patterns, confirming that unlike risk perception, AV performance and individual differences serve as the primary predictors of trust. These findings quantify trust’s emotional architecture, revealing that fostering positive emotional responses is more powerful than mitigating negative ones. AV development should therefore prioritize performance reliability and confidence building over safety communication or anxiety reduction.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103642"},"PeriodicalIF":5.1,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Safeguarding worker psychosocial well-being in the age of AI: The critical role of decision control","authors":"Mario Passalacqua ,&nbsp;Robert Pellerin ,&nbsp;Florian Magnani ,&nbsp;Laurent Joblot ,&nbsp;Frédéric Rosin ,&nbsp;Esma Yahia ,&nbsp;Pierre-Majorique Léger","doi":"10.1016/j.ijhcs.2025.103649","DOIUrl":"10.1016/j.ijhcs.2025.103649","url":null,"abstract":"<div><div>Advancements in artificial intelligence (AI) have ushered in the era of the fourth industrial revolution, transforming workplace dynamics with AI's enhanced decision-making capabilities. While AI has been shown to reduce worker mental workload, improve performance, and enhance physical safety, it also has the potential to negatively impact psychosocial factors, such as work meaningfulness, worker autonomy, and motivation, among others. These factors are crucial as they impact employee retention, well-being, and organizational performance. Yet, the impact of automating decision-making aspects of work on the psychosocial dimension of human-AI interaction remains largely unknown due to the lack of empirical evidence. To address this gap, our study conducted an experiment with 102 participants in a laboratory designed to replicate a manufacturing line. We manipulated the level of AI decision support—characterized by the AI's decision-making control—to observe its effects on worker psychosocial factors through a blend of perceptual, physiological, and observational measures. Our aim was to discern the differential impacts of fully versus partially automated AI decision support on workers' perceptions of job meaningfulness, autonomy, competence, motivation, engagement, and performance on an error-detection task. The results of this study suggest the presence of a critical boundary in automation for psychosocial factors, demonstrating that while some automation of decision selection can nurture work meaningfulness, worker autonomy, competence, self-determined motivation, and engagement, there is a pivotal point beyond which these benefits can decline. Thus, balancing AI assistance with human control is vital to protect psychosocial well‑being. Practically, industry and operations managers should keep employees involved in decision making by adopting partial, confirm‑or‑override AI systems that sustain motivation and engagement, boosting retention and productivity.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103649"},"PeriodicalIF":5.1,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Influence of anthropomorphism on trust in artificial intelligence: Take virtual agent as an example","authors":"Xiaohan Shi ,&nbsp;Gengfeng Niu ,&nbsp;Siyu Jin ,&nbsp;Wencheng Yang ,&nbsp;Xiaojun Sun","doi":"10.1016/j.ijhcs.2025.103644","DOIUrl":"10.1016/j.ijhcs.2025.103644","url":null,"abstract":"<div><div>Nowadays, artificial intelligence (AI) and its applications (represented by virtual agents) are gaining increasing popularity in people's daily lives; and trust in them plays a crucial role in influencing the acceptance and usage of AI. Anthropomorphism is a fundamental element in studying human-machine trust. However, the findings regarding the impact on trust in AI are inconsistent, and the underlying mechanism remains unclear. To address these gaps, three studies were conducted to examine the influence of behavioral anthropomorphism (manipulated by emoticons use) on the trust in virtual agents, as well as the potential moderating and mediating mechanisms. The results revealed that behavioral anthropomorphism positively affected trust in AI. More importantly, this study identified the boundary conditions (the moderating effect of attitudes towards AI) and internal mechanisms (the mediating roles of warmth and competence). It is worth noting that excessive anthropomorphism (behavioral anthropomorphism and appearance anthropomorphism exist at the same time) may cause the uncanny valley effect, thereby damaging trust in AI. Additionally, it was found that the positive effect of anthropomorphism on AI trust is more prominent among individuals who hold negative attitudes towards AI. Overall, the study offered theoretical foundations and practical guidelines for effective AI-human interaction and trust-building strategies.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103644"},"PeriodicalIF":5.1,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating the challenges of remotely supporting blind riders in ridesharing","authors":"Eshed Ohn-Bar ,&nbsp;Ruizhao Zhu ,&nbsp;Jimuyang Zhang ,&nbsp;Lu Zhang","doi":"10.1016/j.ijhcs.2025.103594","DOIUrl":"10.1016/j.ijhcs.2025.103594","url":null,"abstract":"<div><div>Ridesharing services often rely on co-located drivers to assist blind users. This process involves users disclosing their disability to drivers, who might otherwise remain in their vehicles before pick up or stop in inaccessible locations. There are, however, emerging scenarios where users may instead prefer to leverage <em>remote sighted support</em>, e.g., in the case of discrimination, driver unwillingness to assist, or the absence of a driver in autonomous rides. While remote support offers a promising solution, prior studies have primarily focused on simple navigation tasks rather than real-time guidance for intricate vehicle identification, entry, and exit tasks in complex urban environments. To address this gap, we conducted a smartphone-based study with 10 users and three remote orientation and mobility guides. Grounded in the realistic context of busy urban conditions, our in-situ analysis of support strategies provides nuanced insights into in-the-wild usability not possible from interviews and lab experiments alone. We further identify frequent issues in discrimination and ride cancellations, further highlighting the growing importance of remote-sighted support in vehicle-interactive tasks. Our findings inform the design of accessible ride services by addressing wait times, failure cases (e.g., incorrect ride identification), and refined support guidelines that consider practical system limitations and user needs during origin-to-vehicle-to-destination travel.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103594"},"PeriodicalIF":5.1,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"“She was useful, but a bit too optimistic”: Augmenting Design with Interactive Virtual Personas","authors":"Paluck Deep,&nbsp;Monica Bharadhidasan,&nbsp;A. Baki Kocaballi","doi":"10.1016/j.ijhcs.2025.103646","DOIUrl":"10.1016/j.ijhcs.2025.103646","url":null,"abstract":"<div><div>Personas have been widely used to understand and communicate user needs in human-centred design. Despite their utility, they may fail to meet the demands of iterative workflows due to their static nature, limited engagement, and inability to adapt to evolving design needs. Recent advances in large language models (LLMs) pave the way for more engaging and adaptive approaches to user representation. This paper introduces Interactive Virtual Personas (IVPs): multimodal, LLM-driven, conversational user simulations that designers can “interview,” brainstorm with, and gather feedback from in real time via voice interface. We conducted a qualitative study with eight professional UX designers, employing an IVP named \"Alice\" across three design activities: user research, ideation, and prototype evaluation. Our findings demonstrate the potential of IVPs to expedite information gathering, inspire design solutions, and provide rapid user-like feedback. However, designers raised concerns about biases, over-optimism, the challenge of ensuring authenticity without real stakeholder input, and the inability of the IVP to fully replicate the nuances of human interaction. Our participants emphasized that IVPs should be viewed as a complement to, not a replacement for, real user engagement. We discuss strategies for prompt engineering, human-in-the-loop integration, and ethical considerations for effective and responsible IVP use in design. Finally, our work contributes to the growing body of research on generative AI in design process by providing insights into UX designers’ experiences of LLM-powered interactive personas.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103646"},"PeriodicalIF":5.1,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Watch dogs: A mixed-methods investigation of dog owners’ views on dog monitoring technologies","authors":"Luís Fernando Costa Garrido ,&nbsp;Ruan R. Daros ,&nbsp;Bianca Vandresen ,&nbsp;Courtney Graham ,&nbsp;Beth A. Ventura","doi":"10.1016/j.ijhcs.2025.103645","DOIUrl":"10.1016/j.ijhcs.2025.103645","url":null,"abstract":"<div><div>Dog monitoring technologies are increasingly integrated into dog owners’ routines, yet the perceived impacts of these technologies on dogs and their owners remain underexplored. This study aimed to investigate the perceptions of dog owners (<em>n</em> = 86) regarding use and impacts of these technologies through a mixed-methods questionnaire. Qualitative content analysis was conducted to identify owners’ reasons for adopting dog monitoring technologies and their perceived impacts. Quantitative data were summarized descriptively and associations of perceived impacts with owner demographics, dog characteristics, technology characteristics and scores on the Monash Dog Owner Relationship Scale were assessed using mixed-effect logistic regression models. Five main themes were identified as reasons for adopting and using technologies: 1) monitoring dog safety, 2) monitoring dog behavior, 3) monitoring dog health or disease, 4) owner-related factors, and 5) miscellaneous reasons. Perceived positive impacts on dogs included improved care and increased off-leash opportunities, while negative impacts involved owners’ low trust in the technology’s reliability and concerns about dog behavioral issues and discomfort. Most owners identified a positive impact on themselves, including reduced anxiety and enhanced self-assessed pet parenting. Stress and financial concerns were mentioned as negative effects. No associations were found between quantitative variables and perceived positive or negative impacts. The findings underscore the importance of considering both the benefits and potential drawbacks of these technologies to ensure their ethical and effective integration into the lives of dogs and the humans who care for them.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103645"},"PeriodicalIF":5.1,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reducing dental anxiety in children using robotic companions: A comparative study of behavior management techniques","authors":"Mine Yasemin ,&nbsp;Elif Bahar Tuna Ince ,&nbsp;Gökhan Ince","doi":"10.1016/j.ijhcs.2025.103633","DOIUrl":"10.1016/j.ijhcs.2025.103633","url":null,"abstract":"<div><div>Anxiety related to dental treatment is a common case among children and can cause serious problems. This study introduces a new distraction technique that utilizes a robotic companion to improve the clinical experience of children and reduce dental anxiety during procedures. The goal is to create an enjoyable and calming environment for pediatric patients by employing robots, encouraging positive behaviors, and cooperation. This approach aims to avoid the expensive and risky alternatives of sedation and general anesthesia. The study focuses on children aged 6 to 10 years and presents an experimental setup involving a humanoid robot, enabling a Wizard of Oz experiment. We compare the effectiveness of two robotic companions and two conventional behavior management methods in reducing dental anxiety. Four groups of patients are treated in different behavior management scenarios: (1) a dentist treating a child without assistance, (2) a dentist assisted by a tablet, (3) a dentist assisted by a humanoid robot, and (4) a dentist assisted by a humanoid robot equipped with a screen on its chest. The performances of the robotic systems are evaluated through patient and dentist questionnaires, as well as by measuring the patient’s pulse rate. The results of the experiments carried out with 120 children demonstrate the effectiveness of the proposed approach using socially assistive robots in dental treatment.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103633"},"PeriodicalIF":5.1,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"User-centric evaluation of explainability of AI with and for humans: A comprehensive empirical study","authors":"Szymon Bobek ,&nbsp;Paloma Korycińska ,&nbsp;Monika Krakowska ,&nbsp;Maciej Mozolewski ,&nbsp;Dorota Rak ,&nbsp;Magdalena Zych ,&nbsp;Magdalena Wójcik ,&nbsp;Grzegorz J. Nalepa","doi":"10.1016/j.ijhcs.2025.103625","DOIUrl":"10.1016/j.ijhcs.2025.103625","url":null,"abstract":"<div><div>This study is located in the Human-Centered Artificial Intelligence (HCAI) and focuses on the results of a user-centered assessment of commonly used eXplainable Artificial Intelligence (XAI) algorithms, specifically investigating how humans understand and interact with the explanations provided by these algorithms. To achieve this, we employed a multi-disciplinary approach that included state-of-the-art research methods from social sciences to measure the comprehensibility of explanations generated by a state-of-the-art machine learning model, specifically the Gradient Boosting Classifier (XGBClassifier). We conducted an extensive empirical user study involving interviews with 39 participants from three different groups, each with varying expertise in data science, data visualisation, and domain-specific knowledge related to the dataset used for training the machine learning model. Participants were asked a series of questions to assess their understanding of the model’s explanations. To ensure replicability, we built the model using a publicly available dataset from the University of California Irvine Machine Learning Repository, focusing on edible and non-edible mushrooms. Our findings reveal limitations in existing XAI methods and confirm the need for new design principles and evaluation techniques that address the specific information needs and user perspectives of different classes of artificial intelligence (AI) stakeholders. We believe that the results of our research and the cross-disciplinary methodology we developed can be successfully adapted to various data types and user profiles, thus promoting dialogue and address opportunities in HCAI research. To support this, we are making the data resulting from our study publicly available.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103625"},"PeriodicalIF":5.1,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards the future of pedestrian–AV interaction: Human perception vs. LLM insights on Smart Pole Interaction Unit in shared spaces","authors":"Vishal Chauhan ,&nbsp;Anubhav ,&nbsp;Chia-Ming Chang ,&nbsp;Xiang Su ,&nbsp;Jin Nakazato ,&nbsp;Ehsan Javanmardi ,&nbsp;Alex Orsholits ,&nbsp;Takeo Igarashi ,&nbsp;Kantaro Fujiwara ,&nbsp;Manabu Tsukada","doi":"10.1016/j.ijhcs.2025.103628","DOIUrl":"10.1016/j.ijhcs.2025.103628","url":null,"abstract":"<div><div>As autonomous vehicles (AVs) reshape urban mobility, establishing effective communication between pedestrians and self-driving vehicles has become a critical safety imperative. This work investigates the integration of Smart Pole Interaction Units (SPIUs) as external human–machine interfaces (eHMIs) in shared spaces and introduces an innovative approach to enhance pedestrian–AV interactions. To provide subjective evidence on SPIU usability, we conduct a group design study (“Humans”) involving 25 participants (aged 18–40). We evaluate user preferences and interaction patterns using group discussion materials, revealing that 90% of the participants strongly prefer real-time multi-AV interactions facilitated by SPIU over conventional eHMI systems, where a pedestrian must look at multiple AVs individually. Furthermore, they emphasize inclusive design through multi-sensory communication channels—visual, auditory, and tactile signals—specifically addressing the needs of vulnerable road users (VRUs), including those with impairments. To complement these non-expert, real-world insights, we employ three leading Large Language Models (LLMs) (ChatGPT-4, Gemini-Pro, and Claude 3.5 Sonnet) as “experts” due to their extensive training data. Using the advantages of the multimodal vision-language processing capabilities of these LLMs, identical questions (text and images) used in human discussions are posed to generate text responses for pedestrian–AV interaction scenarios. Responses generated from LLMs and recorded conversations from human group discussions are used to extract the most frequent words. A keyword frequency analysis from both humans and LLMs is performed with three categories, Context, Safety, and Important. Our findings indicate that LLMs employ safety-related keywords 30% more frequently than human participants, suggesting a more structured, safety-centric approach. Among LLMs, ChatGPT-4 demonstrates superior response latency, Claude shows a closer alignment with human responses, and Gemini-Pro provides structured and contextually relevant insights. Our results from “Humans” and “LLMs” establish SPIU as a promising system for facilitating trust-building and safety-ensuring interactions among pedestrians, AVs, and delivery robots. Integrating diverse stakeholder feedback, we propose a prototype SPIU design to advance pedestrian–AV interactions in shared urban spaces, positioning SPIU as crucial infrastructure hubs for safe and trustworthy navigation.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103628"},"PeriodicalIF":5.1,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing for human-centered AI—Lessons learned from a case study in the clinical domain","authors":"Tove Helldin,&nbsp;Christian Norrie","doi":"10.1016/j.ijhcs.2025.103623","DOIUrl":"10.1016/j.ijhcs.2025.103623","url":null,"abstract":"<div><div>AI tools for supporting, or even fully automating, human decision-making have been proposed in a variety of domains, promising faster and better quality of decisions. However, for high-stakes and critical decisions, humans are still required in the decision-making process. Despite the need for human involvement, the research core centers mainly around the technical issues of AI, i.e. how to develop better performing machine learning (ML) models, setting aside the issue of designing, developing, and evaluating AI tools that are to be used in a human-AI context. This focus has led to a lack of experience and guidance of designing and developing AI tools that support their users in a decision-making context, keeping the human in the loop.</div><div>In this paper, we outline our work on designing, developing, and evaluating a transparent AI-based tool to be used by non-AI experts, namely healthcare professionals. The work carried out had two parallel tracks. One focused on testing and implementing a suitable ML technique for sepsis diagnostics based on real patient data and applying explainable AI (XAI) techniques on the results to better enable healthcare professionals to understand and trust the analysis results. The other track included an iterative design process for developing a user-centered, transparent, and trustworthy sepsis diagnostic tool, evaluating whether the generated XAI explanations were fit for purpose. We present the process applied for intertwining these tracks during a common multidisciplinary development process, providing guidance how to conduct a human-centered AI (HCAI) project. We discuss lessons learned, and outline future work for the development of HCAI tools to be used by non-AI experts.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103623"},"PeriodicalIF":5.1,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}