International Journal of Advanced Human Computer Interaction
International Journal of Advanced Human Computer Interaction (IJAHCI) Overview: The International Journal of Advanced Human-Computer Interaction (IJAHCI) is a prestigious, blind, peer-reviewed, open-access journal dedicated to the dissemination of high-quality research in all areas of human-computer interaction. Established in 2020 and registered with the Ministry of Culture and Islamic Guidance (registration number 92621), IJAHCI is committed to advancing the field through the publication of original research articles and comprehensive review articles. Scope and Aim: IJAHCI aims to bridge the gap between theory and practice in human-computer interaction by fostering the exchange of innovative ideas and practical solutions. We welcome submissions that explore new methodologies, technologies, and theoretical advancements in the field. Our goal is to provide a platform for researchers, practitioners, and educators to share their findings and contribute to the growth of human-computer interaction as a discipline. Subjects Covered: Artificial Intelligence User Experience (UX) Design Human-Centered Computing Interaction Design Cognitive Ergonomics Virtual and Augmented Reality Assistive Technologies User Interface Design Usability Engineering Human-Robot Interaction Social Computing Mobile Computing Health Informatics Smart Environments Game Design and Gamification Collaborative Systems Affective Computing Human Factors in Security and Privacy Internet of Things (IoT) E-learning and Educational Technology Publication Ethics: IJAHCI maintains a rigorous peer-review process to ensure the integrity and quality of published articles. We employ state-of-the-art plagiarism detection tools to uphold the highest standards of academic honesty. Open Access Policy: IJAHCI is an open access journal, meaning all articles are freely available to readers worldwide. We apply the Creative Commons Attribution (CC BY) license to our publications, allowing for unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Article Processing Charge: To cover the costs of publishing, IJAHCI charges a minimal Article Processing Charge (APC). This fee supports the open access model and ensures that all articles are freely accessible. Indexing and Abstracting: IJAHCI is indexed and abstracted in major databases, ensuring wide visibility and accessibility of our published works. We continuously strive to enhance the discoverability of our articles through inclusion in reputable indexing services. About the Journal: Format: Print & Online Available from: Online Language: English Types of Journal: Scientific/Scholarly Journal Policy: Peer-reviewed Submission Format: IJAHCI format Contact Information: For any inquiries or submissions, please contact us at: admin@ijahci.com
Human–AI Co-Writing for Scientific Peer Review: An HCI Framework for Transparent, Calibrated Assistance
Large language models (LLMs) increasingly assist reviewers in drafting, organizing, and justifying peer-review reports. While such tools promise faster, clearer feedback, they risk over-confident claims, ungrounded citations, and homogenized critique that can erode reviewer accountability and author trust. We study AI-assisted peer review as a human–computer interaction (HCI) problem centered on co-writing under constraints of fairness, transparency, and time. Through a multi-method investigation—(i) contextual inquiry with 24 active reviewers across computer science, industrial engineering, and HCI venues; (ii) two controlled experiments (N=96 reviews) comparing prompt patterns, evidence-linking, and uncertainty displays; and (iii) a six-week field deployment of a review-composition interface integrated with a manuscript viewer—we derive a design framework that aligns AI generation with reviewer judgment and venue policy. Interface patterns that (1) require evidence anchoring (inline links to exact manuscript spans), (2) enforce claim typing with uncertainty ranges, and (3) provide counter-arguments on demand improved rubric coverage (+22%), rationale specificity (+31%), and self-reported confidence calibration (+28%) while preserving reviewer voice. However, naive auto-summaries elevated superficiality and increased reliance on model phrasing. We contribute actionable guidelines, auditing checklists, and failure taxonomies for safe, transparent AI co-writing in scientific peer review.
Human–AI Collaboration for Clinical Decision Support: An HCI Design Framework
Artificial intelligence (AI) is increasingly embedded in clinical decision support (CDS) systems for triage, diagnosis, and care planning. Yet, improvements in model performance do not automatically translate into safer or better clinical decisions. In high-stakes environments such as emergency departments (EDs), the success of CDS hinges on human–computer interaction (HCI): how information is presented, when and how recommendations arrive, and how accountability, oversight, and feedback are managed within complex workflows. This paper advances a comprehensive HCI framework for human–AI collaboration in CDS across three layers: (1) information design (uncertainty-forward summaries, contrastive explanations, progressive disclosure), (2) coordination design (workflow-aligned timing, interruption management, handoff support), and (3) governance design (provenance, auditability, and clinician override as first-class operations). We report findings from a mixed-methods program: 36 hours of contextual inquiry in two EDs; two controlled studies with 72 clinicians comparing uncertainty encodings and explanation patterns; and an eight-week field deployment of a modular interface layer running in shadow mode over an existing risk-prediction model. The interface variants with uncertainty-aware summaries and counterfactual explanations produced a statistically significant reduction in over-treatment (–14%, p < .05), improved trust calibration (+27% reduction in calibration error, p < .01), and decreased handoff screen-switching (–22%). The deployment preserved decision time within operational thresholds (median change +6s, n.s.) and reduced post-hoc diagnostic revisions (–19%). We surface risks, including automation bias induced by persuasive explanations and alert fatigue from mistimed prompts, and we propose concrete mitigations. The paper contributes: a rigorously evaluated design framework; reusable UI patterns with parameterizations for uncertainty and contrastive reasoning; and a governance checklist to support safe adoption, audit, and continual improvement.
Comparative Study of Hybrid and Calibrated Entity Linking for Semantic Enrichment Across Domains
Bibliometric evidence [17] indicates rapid growth in semantic enrichment, yet comparative studies that integrate calibration and human-in-the-loop (HITL) operation remain scarce. We present a cross-domain comparison of four systems: (i) BM25+metadata, (ii) SBERT bi-encoder, (iii) domain-adapted bi-encoder+cross-encoder, and (iv) a hybrid that pairs (iii) with calibrated thresholds and lightweight HITL review. Across news, technical reports, and clinical-like narratives, the hybrid achieves the best end-to-end macro-F1 and reliability (lower ECE) with acceptable latency. We contribute four reproducible figures (PR curves, reliability, accuracy–throughput trade-off, error taxonomy) and tables for dataset statistics, metrics, and latency.
Clinical Semantic Enrichment with Calibration and FHIR Interoperability
Clinical notes and reports contain high-value signals for analytics and care pathways, but they are heterogeneous, noisy, and riddled with privacy-sensitive details. Building on the bibliometric evidence base in [20], we design an applied pipeline for clinical semantic enrichment that (i) performs PHI redaction, (ii) recognizes entities and links them to SNOMED CT and LOINC through UMLS, (iii) calibrates cross-encoder scores for risk-aware operation, and (iv) exports interoperable resources in HL7 FHIR. Evaluated on mixed corpora (discharge summaries, radiology reports, lab narratives), the approach improves candidate PR and macro-F1 while reducing latency. We provide reproducible figures (architecture, PR curves, reliability, latency), two tables (metrics and ontology coverage), and deployment guidance for hospital IT and research teams.
Human–AI Collaboration for Semantic Enrichment: Interaction Design, Accessibility, and Risk-Aware Review
Semantic enrichment tools are increasingly used by analysts, editors, and curators to attach entities and relations to text at scale. Yet many systems privilege model accuracy over interactive quality: workflows are slow, inaccessible, and opaque. Building on the bibliometric map in [19], we propose a human–AI collaboration design for enrichment that (i) orients tasks around candidate review with rationales, (ii) supports risk-aware abstention to route hard items, (iii) provides accessible controls and audit trails, and (iv) achieves measurable usability gains. Across three scenarios, we reduce time-on-task by 23–28%, raise SUS to 78.4, and drop operator-verified errors at higher confidence thresholds. We release reproducible figures (workflow, SUS histogram, time-on-task, threshold–error) and template-conformant tables.
Investigating AI-Driven Adaptive Interfaces to Enhance User Engagement
This paper presents a large-scale experimental investigation into how AI-driven adaptive user interfaces can improve user engagement in data-intensive tasks. Over 60 participants engaged with both static and dynamically adaptive dashboards; the latter reordered widgets in real time using a LightGBM model trained on preliminary interaction logs. We collected objective metrics (task completion time, error rates) and subjective measures (NASA-TLX, User Engagement Scale). The adaptive condition exhibited a 14 % reduction in completion time and a 22 % increase in reported engagement (p ¡ 0.01), alongside a 17 % decrease in perceived workload. Qualitative feedback highlighted enhanced perceived intuitiveness and satisfaction. We situate our contributions within emerging AI-HCI research, extend theory on personalization reflexives, and propose guidelines for deploying real-time adaptation responsibly. Our findings confirm that machine-learning-powered adaptation is both feasible and beneficial for enriching user experience in professional analytics tools. We conclude by discussing implications for explainability, privacy, and longitudinal viability.
Advancing Task Guidance Systems: The ARGUS Framework for AI and ML-Assisted Augmented Reality
This paper presents the ARGUS (Augmented Reality Guidance Using Smart-algorithms) framework, a pioneering system that integrates artificial intelligence (AI), machine learning (ML), and augmented reality (AR) to deliver adaptive, context-aware task guidance. Traditional guidance systems often lack flexibility and personalization; ARGUS addresses these limitations by incorporating multimodal perception, real-time decision-making, and reinforcement learning to personalize support and improve task performance. We detail its architecture—including the perception module, intelligence engine, visualization system, and user interface—and evaluate its effectiveness across industrial, healthcare, emergency response, and educational domains. Empirical results show up to 81% error reduction, 42% efficiency gains, and significant improvements in user satisfaction. This work underscores the transformative potential of intelligent AR guidance systems and highlights technical, ethical, and practical considerations for future development.
Optimizing Data Input Interfaces Through User Error Analysis: A Statistical and HCI-Driven Approach
Accurate and efficient data input interfaces are essential for user interaction in a wide range of digital systems, from online forms to enterprise software. However, poorly designed input forms often lead to high error rates, increased cognitive load, and inefficiencies in task completion. This paper presents a comprehensive approach to optimizing data input interfaces by integrating Human-Computer Interaction (HCI) principles with statistical analysis techniques. The study compares three types of input form designs—simple, complex, and optimized layouts—focusing on their impact on user performance, including task completion time, error rates, and interaction counts (such as clicks and screen changes). By utilizing synthetic and real-world user data, we employ statistical methods to analyze how interface design choices influence user behavior, revealing patterns in error frequency and task inefficiency. Through iterative testing and user-centered redesign, the research aims to demonstrate how optimizing layout structure, minimizing unnecessary interactions, and improving input validation can reduce cognitive load and enhance user accuracy. The findings underscore the value of a data-driven approach to interface design, with results showing significant reductions in error rates and task times when optimized interfaces are implemented. This research offers an innovative framework for combining HCI insights and statistical analysis, providing a replicable methodology for improving the usability of data input systems across various industries. The proposed approach not only identifies specific design flaws but also delivers practical, evidence-based solutions for creating more intuitive and user-friendly interfaces, ultimately enhancing user experience and system efficiency.
Enhancing User Experience for Real-Time Panic Attack Detection with Wearable Technology: A Human-Computer Interaction Approach with Machine Learning Integration
Panic attacks are sudden and debilitating episodes of intense fear, often accompanied by physiological symptoms such as increased heart rate, irregular heart rhythms, and shortness of breath. Early detection and timely intervention can significantly improve the management of panic disorder. This paper presents a human-computer interaction (HCI) focused approach to real-time panic attack detection using wearable devices that monitor physiological signals. The system uses data such as heart rate (HR), heart rate variability (HRV), blood oxygen saturation (SpO2), and electrocardiogram (ECG) to predict panic attacks. A machine learning model, incorporating algorithms such as Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), is employed to analyze these signals and detect abnormal patterns indicative of a panic episode. The study prioritizes the user interface design and interaction experience, ensuring the system is user-friendly, discreet, and provides timely alerts and coping strategies. A six-month clinical trial with participants diagnosed with panic disorder demonstrated the feasibility of real-time monitoring and detection. Results show promising accuracy in detecting panic attacks, while user feedback indicated high satisfaction with the device's usability and effectiveness in real-world scenarios. This research contributes to the growing field of wearable technology in mental health by integrating machine learning with a focus on improving user experience.
Designing Adaptive Interfaces in the Metaverse: Reducing Cognitive Load for Enhanced User Experience
The increasing complexity of tasks in the metaverse often imposes high cognitive demands on users, hindering their performance and satisfaction. This study explores the development of adaptive interfaces designed to dynamically adjust to users' needs, reducing cognitive load in immersive environments. Using the NASA-TLX framework, we conducted controlled experiments to evaluate task completion times, error rates, and user-reported workload across adaptive and static interface designs. Results indicate that adaptive interfaces significantly improve user efficiency and satisfaction by minimizing mental and physical demands. These findings highlight the potential of adaptive human-computer interfaces in creating more intuitive, inclusive, and productive metaverse experiences.
The Influence of Color Schemes and Aesthetics on User Satisfaction in Web Design: An Empirical Study
The visual design of a website, particularly the choice of color schemes and overall aesthetics, plays a crucial role in shaping user satisfaction and influencing user behavior. This study explores the impact of color schemes and aesthetic elements on user satisfaction by conducting an empirical analysis across different types of websites, including e-commerce, educational, and informational platforms. By utilizing a mixed-method approach that combines quantitative data from user satisfaction surveys and qualitative feedback from usability testing, the research investigates how color schemes influence user perceptions of usability, trustworthiness, and emotional engagement. The study reveals that certain color schemes, especially those that align with cultural preferences and psychological associations, can enhance users’ emotional connection with a website, improving their perception of usability and encouraging longer engagement. Conversely, poorly chosen color palettes can lead to negative emotional responses, reducing satisfaction and trust. Additionally, the overall aesthetic harmony of a website, including the balance of colors, imagery, and layout, significantly contributes to the user's overall experience and satisfaction. The findings underscore the importance of visual coherence and suggest that web designers must not only consider the psychological effects of individual colors but also how these elements work together to create a seamless and satisfying user experience. This study offers valuable insights for web designers, UX professionals, and marketers aiming to optimize visual design for enhanced user satisfaction, and it suggests further exploration of other design components like typography and layout in relation to color schemes.
Measuring User Engagement in Educational Platforms: A Study on Interactive vs. Passive Learning
User engagement is critical to the success of educational platforms, as it directly impacts learning outcomes, retention, and overall user satisfaction. This study investigates the differences in user engagement between interactive and passive learning environments on digital educational platforms, using a mixed-methods approach. Quantitative metrics such as time spent on tasks, completion rates, and interaction frequencies were collected through platform analytics, while qualitative data on user satisfaction and perceived learning effectiveness were gathered through surveys and interviews. A total of 40 participants were divided into two groups: one engaged in an interactive learning experience featuring quizzes, videos, and real-time feedback, while the other experienced a passive learning format consisting primarily of lecture videos and reading materials. The results reveal significantly higher engagement in the interactive learning environment, with participants spending more time on tasks, completing more content, and reporting greater satisfaction. The interactive group also demonstrated higher interaction frequency and better quiz performance, indicating that active participation promotes deeper cognitive involvement and improved learning outcomes. These findings provide valuable insights for educational platform designers, suggesting that incorporating interactive elements can enhance user engagement and lead to more effective learning experiences.
Investigating Interaction Fatigue in Gesture-Based Interfaces: A Comprehensive Study on User Performance, Physical Strain, and Task Efficiency
Gesture-based interfaces offer intuitive and immersive interaction methods, but prolonged use can lead to interaction fatigue, which can negatively impact user performance and satisfaction. This study explores the effects of extended use of gesture-based interfaces on task efficiency, error rates, and physical strain. Forty participants engaged in tasks of varying complexity, with physical strain measured using wearable fitness trackers to monitor heart rate and activity levels. Additionally, subjective fatigue levels were recorded using the Borg Rating of Perceived Exertion (RPE) scale. Task performance data, including task completion times and error rates, were analyzed to assess the impact of fatigue on efficiency. Results indicate a significant correlation between increased physical strain and a decline in performance, particularly in tasks requiring complex gestures. Time-series analysis revealed critical thresholds where fatigue begins to impair performance, while correlation plots highlighted the relationship between physical exertion and error rates. The findings provide insights into designing more ergonomic and adaptive gesture-based systems that can mitigate fatigue and improve long-term user experience, offering practical guidelines for enhancing interface usability during extended interactions.
Enhancing Human-Computer Interaction in Healthcare: Optimizing UI/UX Design for Electronic Health Records (EHR) Systems
Electronic Health Records (EHR) systems are critical components of modern healthcare, providing digital platforms for managing patient information, streamlining clinical workflows, and supporting medical decision-making. However, despite their widespread adoption, EHR systems often suffer from poor user interface (UI) and user experience (UX) design, leading to significant challenges such as increased cognitive load, workflow disruption, user dissatisfaction, and even the risk of medical errors. This paper addresses the need to optimize the UI/UX of EHR systems by examining the core human-computer interaction (HCI) issues that healthcare professionals face when interacting with these systems. Through a comprehensive analysis of existing EHR design practices, common usability flaws, and user feedback, we identify key pain points, including complex navigation, inefficient task flows, and lack of customization. We propose design improvements based on HCI principles, such as task-oriented interfaces, minimalistic design to reduce cognitive load, and better alignment with clinical workflows to enhance usability. The study emphasizes the importance of a user-centered design approach that actively involves healthcare professionals in the design process, ensuring that EHR systems support the efficiency, accuracy, and safety needed in healthcare environments. The findings of this research suggest that by focusing on intuitive and efficient UI/UX design, EHR systems can better serve the needs of clinicians, reduce burnout, and contribute to improved patient outcomes.
The Role of Gesture-Based Interaction in Improving User Satisfaction for Touchless Interfaces
Gesture-based interaction has emerged as a promising technology for enhancing user satisfaction in touchless interfaces, offering more natural, intuitive, and efficient ways of interacting with digital systems. This study explores the impact of gesture-based interfaces on user satisfaction by examining various applications across industries such as healthcare, gaming, smart homes, and public displays. As the demand for hygienic, contact-free technologies increases, particularly in the wake of global health concerns, gesture-based systems provide an innovative solution that reduces reliance on physical contact while maintaining high usability. The research investigates how different types of gestures—such as swipe, tap, and point—affect user experience in terms of accuracy, ease of use, and emotional engagement. Through a mixed-method approach combining quantitative surveys and usability testing, the study evaluates user satisfaction levels, analyzing factors such as learning curve, task completion time, and the sense of control over the interface. Results indicate that gesture-based interaction significantly enhances user satisfaction by making interfaces more engaging and accessible, particularly for users with mobility impairments or those interacting with public systems where physical contact is minimized. The findings suggest that, while there are challenges related to gesture recognition accuracy and user fatigue, the potential benefits in terms of efficiency, hygiene, and accessibility make gesture-based interaction a valuable component of future interface design. This research offers insights into optimizing gesture-based systems to improve user satisfaction, recommending best practices for interface designers and developers working to implement effective touchless solutions.
User-Centered Design in HCl: Enhancing Usability and Interaction in Complex Systems
User-centered design (UCD) has emerged as a pivotal approach in the field of Human-Computer Interaction (HCI), emphasizing the importance of creating systems that align with user needs, preferences, and behaviors. As systems grow increasingly complex, spanning domains such as healthcare, education, and industrial applications, ensuring their usability and enhancing interaction quality have become central challenges. This study delves into the application of UCD principles in addressing these challenges, focusing on how iterative design processes, combined with usability testing and user feedback analysis, can significantly improve system efficiency, reduce cognitive load, and increase user satisfaction. Drawing from case studies across different industries, this research highlights how early and continuous user involvement in the design process leads to more intuitive, accessible, and high-functioning systems. In particular, the study explores the role of user personas, task analysis, and prototyping in refining system interfaces and workflows. By identifying user pain points and iteratively adjusting system functionalities, designers can create solutions that not only meet technical requirements but also provide seamless, enjoyable user experiences. The findings indicate that UCD is especially effective in enhancing the usability of complex systems, where user interaction is often multifaceted and cognitively demanding. These insights offer practical guidance for designers, developers, and HCI professionals, illustrating the value of integrating UCD methodologies to create systems that are more responsive to user needs. Ultimately, this research underscores the transformative potential of UCD in fostering systems that improve interaction quality, elevate performance, and contribute to overall user well-being.
Statistical Evaluation of Multimodal Interfaces: Exploring User Preferences for Combined Input Methods
Multimodal interfaces, which integrate input methods such as voice, touch, and gesture, are increasingly used to enhance user interaction. However, understanding user preferences and optimizing the combination of input modalities remains a challenge. This paper presents a comprehensive statistical analysis of user preferences for different input method combinations, focusing on how these combinations affect task performance, efficiency, and overall satisfaction. A study was conducted with participants performing tasks that required various levels of complexity. Multivariate analysis of variance (MANOVA) was applied to investigate the effects of combined input modalities on task completion time, error rates, and user satisfaction. Regression analysis was used to identify factors influencing user preference for specific input combinations, including demographic and behavioral variables. The results demonstrate clear preferences for certain combinations of input methods depending on task type and complexity, offering key insights into the design of more adaptive and efficient multimodal systems. These findings provide a statistical foundation for improving the integration of multimodal inputs, leading to enhanced user experiences and more effective system designs.
Beyond Speed and Distance: Expanding Metrics for Detecting User Frustration in Human-Computer Interaction
The detection of emotional states, particularly frustration, during human-computer interaction (HCI) is critical for optimizing user experience and system performance. While prior research has primarily focused on mouse cursor speed and distance as indicators of negative emotion, this study seeks to broaden the scope by introducing and evaluating novel behavioral metrics. We explore additional indicators such as hesitation patterns, interaction pauses, click dynamics, and trajectory irregularities to capture more nuanced emotional responses. By integrating these metrics with traditional tracking data, we aim to enhance the accuracy of frustration detection in real-time system interactions. Through a series of experiments conducted in both controlled environments and live user interfaces, our results demonstrate a significant improvement in detecting emotional variability and user frustration. This approach paves the way for the design of adaptive, emotion-aware systems that can dynamically adjust to users' emotional states, ultimately leading to more intuitive and satisfying user experiences. Our findings provide a foundation for the development of next-generation HCI designs that prioritize user emotion as a key component of interaction design.
Emotion Detection×User Frustration×Human-Computer Interaction (HCI)×Behavioral Metrics×Mouse Tracking×Hesitation Patterns×Interaction Pauses×Real-Time Emotion Analysis×Adaptive System
Evaluating the Impact of VR Interfaces on User Productivity in Manufacturing Environments
As industries increasingly adopt advanced technologies to enhance productivity, Virtual Reality (VR) has emerged as a promising tool for improving user interaction and efficiency in manufacturing environments. This study evaluates the impact of VR interfaces on user productivity by comparing traditional and VR-based systems in various manufacturing tasks. Through a series of controlled experiments, participants engaged in assembly, maintenance, and quality control tasks using both conventional and VR interfaces. The results indicate that VR interfaces significantly enhance user productivity by providing immersive and intuitive environments that streamline complex tasks, reduce errors, and improve overall task performance. However, the study also highlights challenges such as initial learning curves and potential discomfort associated with prolonged VR use. These findings offer valuable insights for industries looking to integrate VR technologies to optimize manufacturing processes and improve worker efficiency.
Copyright (c) 2024 International Journal of Advanced Human Computer Interaction
Adaptive Virtual Assistant Interaction through Real-Time Speech Emotion Analysis Using Hybrid Deep Learning Models and Contextual Awareness
The integration of real-time speech emotion analysis with contextual awareness in virtual assistants has the potential to significantly enhance user interactions. This study presents a novel approach to adaptive virtual assistant interaction by employing hybrid deep learning models, specifically 1D Convolutional Neural Networks (CNNs) combined with attention mechanisms, to accurately detect and interpret user emotions. Additionally, the system incorporates contextual awareness, leveraging conversation history, user preferences, and environmental factors to adapt responses dynamically. The hybrid model is trained on a comprehensive speech emotion dataset, and its performance is evaluated against baseline methods using various metrics, including accuracy, precision, recall, and F1-score. Comparative analyses and ablation studies highlight the impact of the attention mechanisms and contextual modules. Real-time performance tests demonstrate the system's responsiveness and efficiency in a simulated virtual assistant environment. The integration of the emotion recognition system into a virtual assistant framework is detailed, with examples of adaptive interaction scenarios. A user experience study assesses the impact on user satisfaction and interaction quality. The findings indicate significant improvements in the virtual assistant's ability to respond appropriately to user emotions and contexts, paving the way for more personalized and engaging user experiences. Future research directions include exploring multimodal emotion recognition and further enhancing system robustness.
Head Movement Patterns as Predictors of Cybersickness in Virtual Reality Games
The increasing popularity of virtual reality (VR) gaming has brought significant attention to user experience factors such as scene complexity, workload, presence, and cybersickness. This study explores the relationships between these elements in a task-based VR game environment. By conducting a comprehensive analysis of user interactions and experiences, we aim to identify how varying levels of scene complexity and workload impact the sense of presence and the incidence of cybersickness. Furthermore, the study investigates the potential of head movement patterns as indicators of cybersickness. The findings suggest that higher scene complexity and workload correlate with increased cybersickness, while a strong sense of presence may mitigate some negative effects. These insights provide valuable guidelines for VR game developers to enhance user experience and minimize cybersickness.
Advancing Human-Agent Interaction: Bridging the Gap Between Vision and Reality
The field of Human-Agent Interaction (HAI) has made significant strides in recent years, yet the gap between the envisioned capabilities of conversational agents and their current state remains substantial. This paper explores the challenges and opportunities in advancing HAI to bridge this gap. By examining theoretical frameworks such as Distributed Cognition for Teamwork (DiCoT), Human-Agent Team (HAT) Game Analysis, and Flows of Power (FoP), we identify key barriers in privacy, trust, and technology. Privacy concerns related to extensive data collection and user consent are analyzed, highlighting the need for transparent and compliant data practices. Trust issues are addressed by focusing on the reliability and transparency of agent operations, essential for user confidence and adoption. Technological hurdles, particularly in natural language understanding and contextual awareness, are examined to outline the advancements needed for agents to engage in seamless, intelligent interactions. This paper provides a comprehensive roadmap for future research and development, emphasizing the importance of user-centric design and collaborative efforts to realize the full potential of conversational agents. By addressing these critical challenges, we can move closer to the vision of advanced, trustworthy, and capable conversational agents.
Privacy, Trust, and Technological Hurdles in Human-Agent Interaction: A Case Study of Apple's Knowledge Navigator
In 1987, Apple introduced the Knowledge Navigator, a visionary concept of a digital personal assistant capable of sophisticated human-agent interactions. This paper examines why the conversational capabilities depicted in Apple's Knowledge Navigator video have yet to be realized, analyzing the barriers through the lenses of privacy, trust, and technological challenges. Utilizing three theoretical frameworks—Distributed Cognition for Teamwork (DiCoT), Human-Agent Team (HAT) Game Analysis, and Flows of Power (FoP)—we systematically deconstruct the interactions between the user and the agent, "Phil," to identify the technological and social impediments to creating such an advanced agent. Key findings highlight the significant privacy concerns associated with extensive user data collection, the complexities of establishing and maintaining user trust, and the technological limitations in natural language processing and contextual understanding. This analysis offers a roadmap for designers and researchers to address these hurdles, paving the way for the development of more capable and trusted conversational agents. By understanding the intricate dynamics between humans and agents, we can better navigate the future of human-computer interaction.
Optimizing Human-Computer Interaction for Sustainable Organic Food Production Through Advanced Detection and Algorithmic Strategies
The integration of advanced Human-Computer Interaction (HCI) techniques in organic food production has the potential to significantly enhance both efficiency and sustainability. This paper explores a novel approach that combines HCI-driven object detection technologies with optimization algorithms to improve the identification and management of pests and diseases in organic farming. By leveraging HCI principles, the proposed system facilitates intuitive and effective interaction between farmers and technology, ensuring timely interventions and optimized decision-making processes. The study demonstrates how this synergy between HCI and agricultural practices can lead to improved crop yields, better quality, and a reduced environmental footprint.