In the era of technological advancement, cybersecurity is an indispensable component of progress. Along with men, women’s participation in the digital landscape is paramount to ensuring equal rights to technological advancement. However, in developing countries, societal barriers pose significant challenges for women, limiting their access to opportunities, which in turn impacts their understanding and practices within the digital landscape. These factors influence the adoption, practices and awareness surrounding cybersecurity, making women in developing countries more vulnerable to cyber-related threats. Thus, it is crucial to assess the state of cybersecurity practices and awareness among women in countries like Bangladesh. This research aims to examine cybersecurity practices and awareness across key components, including password security, information security, device privacy and protection, incident reporting and malware detection, among different demographics of Bangladeshi women. The study employs a quantitative approach using a large-scale survey (n = 1202) to explore the cybersecurity practices and awareness of three groups of women: high school students, university students and working women. By investigating digital habits and practices, this study addresses women’s perceptions, knowledge and response strategies concerning cybersecurity awareness. The findings reveal a significant gap in women’s cybersecurity awareness, highlighting vulnerable practices related to password security, information security, phishing awareness, lack of confidence in handling cybersecurity incidents and limited knowledge of malware detection mechanisms. These findings provide insights into the vulnerabilities of women’s cybersecurity practices in developing countries and lay the groundwork for investigating digital interventions to address these issues. Furthermore, the research sheds light on the substantial barriers contributing to women’s digital insecurity, emphasizing the need to tackle these challenges to ensure women’s online safety and well-being.
As smart dynamic ecosystems, represented e.g. by (semi-) autonomous vehicles in smart city settings, become more prevalent, determining liability and accountability for these systems becomes increasingly complex. This raises serious concerns about the course of the investigation and fair judgments in case of incidents such as rule violations or accidents, which directly impact humans affected by these systems. The current research gap lies in distinguishing between intentional and unintentional misbehavior within these ecosystems, which depend on interpreting human action and user interactions with the autonomous system. This paper addresses this multidisciplinary challenge through three main contributions. First, we consider both technological and human factors for intent classification in post-incident investigations. Second, we extend human-like analysis to autonomous systems to evaluate their intent in a manner consistent with legal reasoning. Third, we propose a comprehensive, context-aware conceptual framework grounded in legal theory, which integrates trust modeling and social metrics to support the identification of intent for investigating misbehavior within smart dynamic ecosystems.
Since the recent proliferation of voice-based agents such as Alexa, Siri, Google Assistant and ChatGPT, it seems appropriate to equip a wide variety of applications with such an interaction modality. We show the use of voice-based technologies using a simple demonstration example with the aim of making it easier for developers of classic applications to get started with this technology. In the course of our work on our demonstration example, a number of questions arose which—in addition to explaining our individual development steps—are addressed.
The pattern of ethnic inter-embeddedness serves both as a mirror of the current dynamics of inter-ethnic relations and as a framework shaping their future evolution. This study employs ArcGIS and GeoDetector, in combination with demographic and socio-economic datasets, to investigate the spatial transformation and underlying drivers of ethnic inter-embeddedness in Sichuan Province between 2000 and 2020. The findings indicate that socio-economic advancement has played a significant role in facilitating the spatial embeddedness of diverse ethnic groups within the province. Specifically, economic modernization—marked by the expansion of the secondary and tertiary sectors—together with the development of educational systems and the improvement of transportation infrastructure, emerges as the most influential set of factors promoting spatial interweaving among ethnic communities. The study concludes that, with the continued socio-economic progress in Sichuan, the spatial configuration of ethnic groups is likely to evolve toward deeper inter-embeddedness, thereby providing enduring spatial platforms for inter-ethnic contact, interaction, and the enhancement of mutual recognition and understanding.
Investigating the impact of active and passive social media behaviors on mental health—including self-esteem, anxiety, and belonging—this study presents an integrative review of 18 peer-reviewed articles. We uncover that while passive engagement, often stemming from social comparison and dysfunctional cognition, consistently correlates with increased anxiety, depression, and body dissatisfaction in vulnerable groups, active engagement driven by authentic interaction tends to boost self-esteem and emotional well-being. Crucially, our findings challenge the simplistic active–passive dichotomy, demonstrating that the psychological outcomes are mediated by interdependent variables such as emotional intent, cognitive profiles, and the inherent design architecture of social media platforms. This highlights the necessity for more refined behavioral models and suggests avenues for future mixed-methods, longitudinal, and intervention-focused research to cultivate healthier digital interactions. Ultimately, this work deepens the understanding of digital well-being and highlights the importance of considering the psychological and social factors that mediate social media use. It provides a foundation for the creation of more ethically conscious and psychologically beneficial social media interfaces, enabling designers to build platforms that mitigate adverse psychological impacts and foster emotionally sustainable online environments.
Whilst conversational AI offers an ample amount of potential for the healthcare sector, many current systems fall short in the areas of emotional intelligence, fairness, and politeness—qualities that are essential in building patients’ trust. This disparity undercuts the promise of digital health solutions and frequently causes worry among users. This study addresses the difficulty of integrating these ethical guidelines into practice by creating and assessing LunaAI, an innovative chatbot for healthcare assistance. Utilizing user-centered design concepts and a thorough literature review as a foundation, we built sophisticated conversational scenarios that addressed hostile user behaviour. These concepts were developed into a functional prototype using Google’s Gemini API and a mobile-focused Progressive Web App (PWA) created with React, Vite, and Firebase. To ensure its efficacy, we conducted preliminary testing with a small individual group, analyzing their responses with established frameworks such as the Godspeed Questionnaire. Furthermore, a comparative analysis was undertaken between LunaAI’s personalized responses and the initial outputs of an uncustomized Large Language Model (LLM). The results demonstrated that LunaAI has made significant improvements in multiple key areas; users rated it 4.7/5 for politeness and 4.9/5 for fairness. These findings have substantial effects on the future development of human-computer interaction, particularly in sensitive domains like healthcare, and emphasize the significance of purposeful, ethical conversational design.
Autism Spectrum Conditions (ASC) are neurodevelopmental disorders affecting communication, social interaction, and behavior. Many centers manually collect and track children’s developmental skills using printed forms, creating a heavy administrative burden for therapists who spend significant time organizing information. This paper presents a mapping review of research on technological tools used for collecting, tracking, monitoring, and assessing children with ASC. The search was conducted in the ACM Digital Library, IEEE Xplore, and Scopus databases, covering the period from 2011 to 2024. The inclusion criteria focused on the following: data collection, monitoring, tracking, or assessing the developmental skills of children with ASC or other neurodevelopmental conditions. The methodology for conducting the mapping review consisted of three main phases: planning, execution, and reporting. Initially, 583 records were identified, and 26 studies were selected after applying the inclusion and exclusion criteria to align with the review’s objectives. The findings indicate that 88.46
Generative Artificial Intelligence (GenAI), by enabling the transfer of tasks associated with human cognition to machines, is taking centre stage in public debate and corporate strategies. However, empirical studies conducted in real-work settings remain limited, with findings largely focused on quantitative indicators. In order to fill this gap in empirical research on the use of GenAI in work contexts, we conducted a study within an in-house trial of a GenAI tool in a major French company. Our objective was to explore how the integration of a GenAI-based tool within an existing sociotechnical system reconfigures employees’ work practices. Based on 9 in-depth re-situating interviews with material traces (self-documented logbooks), our results highlight (1) how workers adapt to the new interaction modality, (2) the varied way they integrate GenAI in their work and (3) the challenges and new tasks associated with the use of the system.
Several studies have shown how students’ mental health is affected when they begin college. When starting college, their mental health can be influenced by emotional, social, or academic challenges. It is crucial to offer tools that support the mental well-being of this group. In this paper, we present the redesign and validation of the Emotions Care app, a mobile application designed to support the mental health of college students. Using results from the latest validation session and applying Nielsen’s heuristics, we enhanced the app’s user interface design and conducted a validation session with 17 college students. Our results show that, although the app’s design has been improved, there are still critical issues to address to ensure its long-term use. These findings highlight the importance of evaluating digital mental health interventions, such as mobile apps, for their usability and acceptability among users, with the ultimate goal of enhancing students’ engagement with these apps.
Student Response Systems have long promised to enhance classroom engagement, yet adoption across higher education remains modest. To explore barriers to uptake, we interviewed teachers who have used such technology in their classes. Their experiences revealed three key challenges: steep learning curves, rigid constraints from pre-scripted quizzes, and disruptions to natural lecture flow. Drawing on Human-Computer Interaction principles, we translated these insights into design guidelines that emphasize learnability, flexibility, and minimal disruption. We developed a prototype Teacher Response System that enables students to signal confusion or pose questions anonymously, offering teachers low-friction, real-time feedback. This work positions a complementary approach that combines contrasting response systems to bridge structured student-centric interaction with dynamic, teacher-led responsiveness to support the situated practices of classroom teaching.
Virtual Humans (VHs) are rapidly emerging as key interfaces in Human-Computer Interaction (HCI) across domains such as education, healthcare, and customer services. While their technical capabilities continue to advance, a core challenge remains; designing VHs that meaningfully align with the expectations of diverse user groups. This study investigates how user age influences preferences for key VH attributes, including perceived agent age and emotional responsiveness. Based on responses from 773 participants aged 18 and above, we identify clear age-based differences in preferences for visual age and interactional expressiveness. Younger users tend to favour emotionally responsive, younger-looking VHs, while older users show preferences for mature agent personas. While context-specific VH preferences may vary, these findings reinforce the position that VH systems should, in general, incorporate adaptive, age-sensitive designs to promote inclusivity and adoption.
This study investigates the comparative effectiveness of traditional digital media and Extended Reality (XR) environments in eLearning for students. Participants engaged with academic content presented through both conventional digital formats—such as text-based documents, slide presentations, and pre-recorded video lectures—and an immersive XR environment using the Apple Vision Pro headset. The aim was to assess differences in user engagement, knowledge retention, and overall learning experiences across these two modalities. Data were gathered through pre- and post-experiment questionnaires, which evaluated subjective experiences, usability, and retention. The findings revealed a clear preference for traditional media for reading and writing tasks, while XR proved to enhance engagement, particularly with visually rich content like video and slide presentations. However, retention was lower for text-heavy materials in XR, suggesting that immersive environments may be less effective for deep reading. Despite this, the majority of participants expressed interest in using XR for educational purposes again, highlighting its potential as a complementary tool in learning. The study concludes with suggestions for refining XR interfaces and the integration of immersive technologies in higher education to support multimodal learning experiences.
Autonomous systems are making significant changes in the military domain, particularly in artillery operations, where they enhance precision and efficiency of fire support. However, these technologies also raise important questions regarding effective human-machine collaboration, especially in high-stakes and critical environments. This paper examines operator attitudes toward autonomous systems and explores the connection between artillery scenarios and autonomous driving in logistics. Mobility, material transport, and convoy operations are critical to the effectiveness of fire support; therefore, the acceptance of and trust in autonomous driving functions are directly relevant to artillery practice. The theoretical framework is grounded in models of trust calibration in automation, emphasising the need to avoid both undertrust and overtrust by aligning reliance with actual system reliability. The empirical part employs a driving simulator and a questionnaire survey among professional drivers. The literature review first demonstrated the absence of currently applicable solutions in relevant military domains, then followed by experimental testing in the simulator and statistical analysis of attitudes. The focus on truck driving was chosen as a representative proxy for military logistics tasks closely tied to artillery; therefore, the results are directly applicable to artillery practice as well. The scientific contribution of this study lies in its analysis of user trust and acceptance of autonomous systems through a controlled experimental framework. The results offer new insights into the psychological and ergonomic dimensions of human-machine interaction. They may inform the design of user interfaces, training protocols, and deployment strategies for autonomous systems in real-world military and civilian contexts.
Design of smartphones and their apps can conflict with users’ personal goals and negatively impact well-being, motivating tech companies to develop tools aimed at self-monitoring and altering smartphone usage. This mixed-method study describes the use of self-nudges within Android’s native Digital Wellbeing app as a tool to achieve smartphone usage goals. Students (N = 63) were encouraged to use the Digital Wellbeing app for several weeks, but were given the freedom to select their preferred strategies to curb smartphone usage. Specifically, they could set personal smartphone usage goals and employ features like app ‘Time Limits’, “Grayscale” mode, and “Focus” mode. Results revealed that 58
Artificial intelligence (AI) has significantly advanced efficient, data-driven decision-making, but as AI becomes increasingly embedded in everyday life, it is essential to examine how our cultural values affect trust in AI within subjective contexts. This paper utilizes Hofstede’s cultural dimensions to investigate potential patterns in receptiveness towards perceived AI predictions during art interpretation. This work contributes a novel study design integrating electroencephalography (EEG), Pleasure-Arousal-Dominance (PAD) values, the Self-Assessment Manikin (SAM) scale, and machine-learning predictions from ArtEmis. Through regression modeling, no significant relationship was found between any cultural dimension and the number of responses altered post-AI prediction. However, 26 out of the total 35 college-student participants acknowledged experiencing self-reflection or persuasion when exposed to AI, regardless of cultural dimensions. This paper expands on prior research and notes future opportunities to address limitations related to sample size and the personalization of AI-user interaction.
Agile software development promotes adaptability, collaboration, and continuous delivery. However, implementing Agile principles remains a challenge for many organizations due to complex organizational, cultural, and interpersonal factors. Building on a previous empirical study that identified the most difficult Agile principles to apply and their underlying causes, this paper extends the investigation by analyzing the barriers that practitioners perceive as limiting Agile adoption in Romanian IT industry exploring human factors compared to computer science world wide industry. Using a mixed-method approach and Straussian Grounded Theory, we developed a concept model that links causes, barriers, and mitigation strategies. The results indicate that barriers often emerge from unresolved causes and can reinforce them over time, leading to persistent implementation difficulties. The study identifies five major categories of barriers and highlights that successful mitigation requires both process-level improvements and cultural transformation. The findings provide actionable insights for practitioners and contribute to a more nuanced understanding of Agile implementation challenges.
Digital identity wallets are software systems that allow users to securely store, manage, and share personal data. While existing approaches have mainly focused on storing identity-related information such as ID cards or driver’s licenses, the use of wallets for use cases beyond, such as managing and sharing shopping data in e-commerce, is still largely unexplored, particularly in terms of user experience (UX). In e-commerce, such wallets offer a promising way for users to share shopping data with online shops in return for, e.g., personalized product recommendations. However, this extended functionality introduces unique UX challenges that differ from traditional identity scenarios. As the success of these systems strongly depends on user adoption, delivering a seamless and intuitive UX is essential. This study evaluates the UX of a wallet prototype designed for managing and sharing shopping data. The evaluation combined an expert-based analysis using a Cognitive Walkthrough with user-based testing involving Thinking aloud, eye tracking, and UX questionnaires. The results revealed several UX issues specific to the shopping data context, as well as recurring challenges already observed in traditional digital identity use cases.
The integration of Large Language Models (LLMs) in education has emerged as a key strategic direction on a global scale. The main purpose of the study is to assess the prompting habits regarding the use of LLM in a mathematical context. We aim to understand how users position themselves regarding the prompt formulation of mathematical problems when using LLM-based tools. An evaluation of the impact of prompt formulation is performed on perception of AI output accuracy, user satisfaction, and perceived usability. The findings underscore the critical role of prompt clarity in shaping user satisfaction and AI accuracy, while also revealing that mathematical proficiency is not a sufficient predictor of effective prompting. The diversity of challenges faced by users reinforces the importance of adaptive interfaces and targeted educational interventions to bridge the gap between AI capabilities and user needs.
This paper addresses the influence of EMS haptic feedback on the physical and tactical behavior of players in a VR multiplayer boxing fight with live motion capturing. A custom prototype was developed, using Unity, the Teslasuit and a Photon PUN 2 cloud server. A within-subjects user test with a mixed-methods approach was conducted, collecting quantitative and qualitative data in EMS-enabled and EMS-disabled conditions. The analysis of the data showed heterogeneous user responses with some participants showing an increase and some participants showing a decrease in their recorded metrics. In general, EMS feedback led to increased tactical decision-making, enhanced realism and a stronger sense of embodiment. Statistically significant differences were found primarily among participants with increased recorded values under EMS-enabled condition. These differences were found in punch counts, movement range, covered area and body orientation metrics. Most players showed an increase in their defense. As the results showed to be highly dependent on individual factors, a generalization of the results is limited.
Approximately 20% of intensive care patients in Germany require mechanical ventilation due to illness or post-operative conditions, which impairs their ability to communicate effectively. This poses significant challenges for patients and their caregivers. Augmentative and alternative communication concepts from other fields can be adapted to the context of intensive care to support ventilated patients. We developed an assistive system designed for the needs of the user group that is controlled by a ball-shaped interaction device. In this paper we introduce the central element of the assistive system, a radial menu technique called Compass Menu. It is systematically described using a taxonomy of menu properties. Our prototype was evaluated by six HCI experts in a pilot study. They evaluated how well design decisions made in development met previously identified menu design objectives as well as usability, applicability, user experience, and aesthetics. For the evaluation, we developed a comprehensive questionnaire tailored to menu design. The results indicate that the menu design could be suitable for implementation in interactive systems that support mechanically ventilated intensive care patients.
