International Journal of Human-Computer Studies最新文献

{"title":"Evaluating empathic responses to bimodal realism in emotionally expressive virtual humans: An eye-tracking and facial electromyography study","authors":"Darragh Higgins ,&nbsp;Benjamin R. Cowan ,&nbsp;Rachel McDonnell","doi":"10.1016/j.ijhcs.2025.103630","DOIUrl":"10.1016/j.ijhcs.2025.103630","url":null,"abstract":"<div><div>With the expanding range of uses for advancements in animation and voice synthesis, more opportunities arise for interactions with animated virtual humans. Such interactions may be influenced by improved portrayals of character features such as emotion and realism. The present study aimed to examine how variations in animated facial detail and vocal prosody shape user perception of emotion in virtual characters. This impact was assessed via facial electromyography and eye-tracking measures, as well as self-reports of state empathy and character appeal. Results indicate that participants were influenced by emotional valence in terms of zygomaticus major and corrugator supercilii muscle activation. Survey data appear to show greater empathy for conditions of increased facial detail and more human-like vocal prosody. Moreover, eye tracking results suggest a preference for eye contact regardless of detail or prosody, with participants fixating more on facial areas of interest overall for the positively valenced conditions. Finally, there is evidence that trait empathy and mismatches between higher facial detail and lower vocal human-likeness may influence zygomaticus major activity in response to positively valenced stimuli. These results are discussed in the context of virtual character design, contemporary understandings of empathy and the phenomenon of the Uncanny Valley.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103630"},"PeriodicalIF":5.1,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107311","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":"Do men and women navigate differently in virtual environments? A comparative study","authors":"Pinyan Tang,&nbsp;Yuye Liao,&nbsp;Kun Zheng,&nbsp;Yifeng Sheng,&nbsp;Wenjie Ren,&nbsp;Chuan Liu,&nbsp;Yuqi Li","doi":"10.1016/j.ijhcs.2025.103621","DOIUrl":"10.1016/j.ijhcs.2025.103621","url":null,"abstract":"<div><div>This study investigates the effects of gender and training interventions on spatial navigation in VR. Thirty-eight participants, divided into male and female intervention and control groups, performed a dual-task involving coin collection and destination location in a large-scale urban VR environment. Performance metrics included the number of coins collected, time taken to reach the destination, and eye-tracking data, normalised for task difficulty. While pre-test performance revealed no significant gender differences, eye movement data highlighted baseline gender differences in gaze patterns, with females exhibiting more exploratory behaviour. Training interventions led to performance improvements, particularly for females, whose gains remained statistically significant after Bonferroni correction. These improvements were accompanied by successful transitions between egocentric and allocentric strategies, as evidenced by gaze data and post-hoc interviews. For males, the intervention led to mixed results, with improvements in performance but a trade-off in efficiency. These findings deepen our understanding of how gender and training influence navigation strategies in VR and inform the design of future VR training systems, emphasising the importance of balancing cognitive load and strategy selection.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103621"},"PeriodicalIF":5.1,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050578","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":"Mental health management as a social endeavour: Challenges and opportunities for conversational agent design","authors":"Robert Bowman ,&nbsp;Anja Thieme ,&nbsp;Benjamin Cowan ,&nbsp;Gavin Doherty","doi":"10.1016/j.ijhcs.2025.103618","DOIUrl":"10.1016/j.ijhcs.2025.103618","url":null,"abstract":"<div><div>Conversational agents (CAs) are a tempting type of computer interface for assisting people’s mental health due to their ability to simulate human-like interactions, however their integration within the broader social context of mental health management remains largely under-explored. Recognising that managing one’s mental health is often a <em>social</em> rather than individual activity involving close persons such as partners, family, and friends, our research takes a social-orientation to mental health management. Utilising design cards that depict fictional, yet plausible CA concepts, we present the analysis of an interview study with 24 young adults to understand their views on CAs for both their own use and for a close person. Participants viewed CAs as potentially valuable complements to human support, but expressed concerns about over-reliance and replacement. Our analysis reveal key tensions, design considerations, and opportunities for integrating CAs into mental health ecosystems in ways that respect and enhance existing social support structures.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103618"},"PeriodicalIF":5.1,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107312","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":"Detection of cognitive and attention dimensions in block programming interface for learning sensor data analytics in construction education","authors":"Mohammad Khalid ,&nbsp;Abiola Akanmu ,&nbsp;Ibukun Awolusi ,&nbsp;Homero Murzi","doi":"10.1016/j.ijhcs.2025.103626","DOIUrl":"10.1016/j.ijhcs.2025.103626","url":null,"abstract":"<div><div>The increasing adoption of sensing technologies in the construction industry generates vast amounts of raw data, requiring analytics skills for effective extraction, analysis, and communication of actionable insights. To address this, ActionSens, a block-based programming interface, was developed to equip undergraduate construction engineering students with domain-specific sensor data analytics skills. However, efficient user interaction with such tools requires integrating intelligent systems capable of detecting users’ attention and cognitive states to provide context-specific and tailored support. This study leveraged eye-tracking data from construction students during the usability evaluation of ActionSens to explore machine learning models for classifying areas of interest and interaction difficulties. For visual detection, key interface elements were defined as areas of interest, serving as ground truth, while interaction difficulty was labeled based on participant feedback for reported challenges. The Ensemble model demonstrated the highest performance, achieving 88.3% accuracy in classifying areas of interest with raw data, and 82.9% for classifying interaction difficulties using oversampling techniques. Results show that gaze position and pupil diameter were the most reliable predictors for classifying areas of interest and detecting interaction difficulties. This study pioneers the integration of machine learning and eye-tracking with block-based programming interfaces in construction education. It also reinforces the Aptitude-Treatment Interaction theory by demonstrating how personalized support can be adapted based on individual cognitive aptitudes to enhance learning outcomes. These findings further contribute to the development of adaptive learning environments that can detect specific user aptitudes and provide context-specific guidance, enabling students to acquire technical skills more effectively.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103626"},"PeriodicalIF":5.1,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107309","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":"Exploring the Impact of Modality and Speech Rate Manipulation in Voice Permission Requests—Limits of Applicability and Potential for Influencing Decision-Making","authors":"Anna Leschanowsky ,&nbsp;Anastasia Sergeeva ,&nbsp;Judith Bauer ,&nbsp;Sheetal Vijapurapu ,&nbsp;Mateusz Dubiel","doi":"10.1016/j.ijhcs.2025.103590","DOIUrl":"10.1016/j.ijhcs.2025.103590","url":null,"abstract":"<div><div>As voice-enabled technologies are becoming increasingly more prevalent, voice-enabled permission requests become a crucial topic of investigation. It is yet unclear how to appropriately inform users in voice user interfaces (VUIs) about data processing practices. To understand how modality (text vs. voice) and the speech rate of the voice can influence users’ perceptions and decisions to grant permission, we conducted two preregistered studies (N = 343 and N = 594) and one pre-study, including two listening tasks to design potentially deceptive voice patterns. We found that users can distinguish between different levels of intrusiveness in the voice modality. However, they are less likely to accept voice-based permissions, pointing to cognitive problems associated with them. Moreover, we found that speech rate manipulations of action verbs “Accept” and “Decline” shifted users’ decisions towards acceptance, making the effect less controllable than predicted. This work highlights implications and design considerations for future voice-enabled permission requests.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103590"},"PeriodicalIF":5.1,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027837","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":"Do you need help? Identifying and responding to pilots’ troubleshooting through eye-tracking and Large Language Model","authors":"Mengtao Lyu,&nbsp;Fan Li","doi":"10.1016/j.ijhcs.2025.103617","DOIUrl":"10.1016/j.ijhcs.2025.103617","url":null,"abstract":"<div><div>In-time automation support is crucial for enhancing pilots’ performance and flight safety. While extensive research has been conducted on providing automation support to mitigate risks associated with the Out-of-the-Loop (OOTL) phenomenon, limited attention has been given to supporting pilots who are actively engaged, known as In-the-Loop (ITL) status. Despite their active engagement, ITL pilots face challenges in managing multiple tasks simultaneously without additional support. For instance, providing critical information through in-time automation support can significantly improve efficiency and flight safety when pilots need to visually troubleshoot unexpected incidents while monitoring the aircraft’s flying status. This study addresses the gap in ITL support by introducing a method that utilizes eye-tracking data tokenized into Visual Attention Matrices (VAMs), integrated with a Large Language Model (LLM) to identify and respond to troubleshooting activities of ITL pilots. We address two primary challenges: capturing the complex troubleshooting status of pilots, which blends with normal monitoring behaviors, and effectively processing non-semantic eye-tracking data using LLM. The proposed VAM approach provides a structured representation of visual attention that supports LLM reasoning, while empirical VAMs enhance the model’s ability to efficiently identify critical features. A case study involving 19 licensed pilots validates the efficacy of the proposed approach in identifying and responding to pilots’ troubleshooting activities. This research contributes significantly to adaptive Human–Computer Interaction (HCI) in aviation by improving support for ITL pilots, thereby laying a foundation for future advancements in human–AI collaboration within automated aviation systems.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103617"},"PeriodicalIF":5.1,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107302","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":"Shaping the fairness journey: The roles of AI literacy, explanation, and interpersonal interaction in AI interviews","authors":"Yi Xu,&nbsp;Zhiyun Chen,&nbsp;Mengyuan Dong","doi":"10.1016/j.ijhcs.2025.103629","DOIUrl":"10.1016/j.ijhcs.2025.103629","url":null,"abstract":"<div><div>Grounded in organizational justice theory, this two-study investigation provides a comprehensive examination of fairness perceptions across the entire AI interviews process. Through a three-stage experimental design (Study 1, <em>N</em> = 113; Study 2, <em>N</em> = 206), we explored how design and outcome factors influence procedural and distributive justice. We manipulated the AI’s explanation (With vs. Without) and level of interpersonal interaction (High vs. low) during the interview process, and the interview result (Pass vs. Fail) and decision agent (100% AI vs. 50% AI + 50% Human) in the post-decision stage. Results indicate that while candidate AI literacy, human-in-the-loop decision-making, and positive outcomes consistently improved fairness perceptions, the effects of system design were more complex. Design features intended to enhance the user experience, such as high AI interactivity and detailed explanations, improved aspects of procedural justice during the interview. Yet, these sometimes backfired by diminishing the perceived distributive justice of the final decision. This reveals a critical tension between a positive process experience and a fair outcome evaluation. These complex effects underscore the practical need for a holistic design approach that manages the entire candidate journey. Fair AI systems require not only improving candidate literacy but also carefully designing system explanations to manage applicant expectations effectively.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103629"},"PeriodicalIF":5.1,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027836","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":"Uncovering the dynamics of human-AI hybrid performance: A qualitative meta-analysis of empirical studies","authors":"Dóra Göndöcs ,&nbsp;Szabolcs Horváth ,&nbsp;Viktor Dörfler","doi":"10.1016/j.ijhcs.2025.103622","DOIUrl":"10.1016/j.ijhcs.2025.103622","url":null,"abstract":"<div><div>Human-AI collaboration is an increasingly important area of research as AI systems are integrated into everyday workflows and moving beyond mere automation and augmentation to more collaborative roles. However, existing research often overlooks the dynamics and performance aspects of this interaction. Our study addresses this gap through a review of empirical AI studies from 2018–2024, focusing on the key factors influencing human-AI collaboration outcomes within the spectrum of Human-Centered Artificial Intelligence (HCAI).</div><div>We identify 24 critical performance factors that influence hybrid performance, grouped into four categories using thematic analysis. Then, we uncover and analyze the complex, non-linear interdependencies between these factors. We present these relationships in a factor dependency graph, highlighting the most influential nodes.</div><div>The graph and specific factor interactions supported by the papers reveal a quite complex web, an interconnectedness of factors. As opposed to being an easy-to-predict combination of inputs, human-AI collaboration in a given context likely leads to a dynamic, evolving system with often non-linear effects on its hybrid performance. Our findings and the previous research on automation technologies suggest that the application of AI tools in collaborative scenarios would benefit from a comprehensive performance framework. Our study intends to contribute to this future line of research with this initial framework.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103622"},"PeriodicalIF":5.1,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222122","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":"Examining dual-task interference effects of visual and auditory perceptual load in virtual reality","authors":"Mohamad El Iskandarani,&nbsp;Matthew Bolton,&nbsp;Sara Lu Riggs","doi":"10.1016/j.ijhcs.2025.103619","DOIUrl":"10.1016/j.ijhcs.2025.103619","url":null,"abstract":"<div><div>Immersive environments often require users to perform tasks that vary in sensory modality and processing demands. Dual-task interference arises when such tasks are performed concurrently, often leading to performance declines and safety risks in applied settings. Yet, it remains unclear how perceptual load and task type jointly shape such interference in virtual reality (VR). To address this gap, we examined intramodal and crossmodal effects of visual and auditory load on dual-task interference in a VR dual-task paradigm. Participants performed a continuous visual tracking task while concurrently completing auditory detection tasks of two types: spatial and object-based. Results showed that perceptual load and task type differentially influenced intramodal interference, with stronger effects in the auditory detection task. Contrary to predictions, no crossmodal interference was observed, suggesting a degree of independence between vision and audition. These findings provide valuable insights for VR interface designers on how to present multimodal content in VR environments, which has implications on user safety and efficacy.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103619"},"PeriodicalIF":5.1,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050630","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":"Conversational agents and charitable behavioral intentions: The roles of modality, communication style, and perceived anthropomorphism","authors":"Junqi Shao ,&nbsp;Leona Yi-Fan Su ,&nbsp;Ziyang Gong ,&nbsp;Minrui Chen","doi":"10.1016/j.ijhcs.2025.103616","DOIUrl":"10.1016/j.ijhcs.2025.103616","url":null,"abstract":"<div><div>Conversational agents (CAs) are increasingly utilized by organizations for fundraising and volunteer recruitment. Yet, little is understood about how voice-based CAs could serve these purposes optimally. This experimental study therefore compares voice-based CAs against text-based ones in terms of their ability to foster users’ intentions to make charitable contributions, and investigates the potential mediation of such effects by two dimensions of user-perceived anthropomorphism. Additionally, it examines how a CA’s communication style moderates these effects. It found that, when a voice-based CA employed a formal communication style, mindless anthropomorphism was a significant mediator of its positive association with charitable behavioral intentions. Conversely, when employing an informal communication style, a text-based CA elicited significantly higher levels of mindful anthropomorphism, and also was positively linked to charitable behavioral intentions. These findings expand our theoretical understanding of how CA modalities influence people’s moral responses toward computers; how this effect could be impaired, or strengthened, by different communication styles; and the underlying mechanisms of two dimensions of anthropomorphism. Practical implications are also discussed.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103616"},"PeriodicalIF":5.1,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050577","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}