Recent research has developed measures that conceptualise human factors in relation to AI in relatively broad terms, such as general attitudes and positive, negative or adaptive use. However, more nuanced instruments are still needed to capture specific psychological dimensions, such as apprehension, which is the focus of this paper. Moreover, many existing measures conceptualise AI as a single, undifferentiated object of evaluation, rather than distinguishing between different AI systems, contexts of use, or functional domains. The present study adapted, developed and validated four distinct but structurally parallel instruments measuring apprehensions toward General AI, Large Language Models, Personal AI and Institutional AI. The instruments share identical structure and item content adapted to each type of AI. Apprehension is operationalised across three dimensions (Implied Malice, Undesirability, and Unpredictability). Data from a British sample of 559 adults (age range 18–45, M = 30.64, SD = 6.75, 50.3% males). For each scale, confirmatory factor analyses supported the three-dimensional structure, while internal consistency was strong at both total and subscale levels, and model-based indicators demonstrated well-defined latent constructs. The scales demonstrated good discriminant and convergent validity. These findings establish four distinct and psychometrically robust instruments suitable for research requiring measurement of apprehension toward artificial intelligence both at a general level and across specific classes of AI systems.

Research shows that trust in AI is influenced by socio-ethical considerations, technical features of AI systems, and user characteristics. Yet, the mediating role of emotional response between perceived risk and trust remains underexplored, particularly across different AI contexts. This cross-sectional vignette experiment design aims to explore the relationship between users’ perceived potential risk, emotional response, and trust in AI, and examine how these relationships vary across different levels of automation and criticality. An online survey included a total of 639 participants including 316 from the UK and 323 from Arab Gulf Cooperation Council (GCC) countries. Participants rated their perceived risk, trust, and emotional response across four scenarios representing different combinations of automation (low/high) and criticality (low/high). Correlation results indicate a significant negative association between perceived risk and trust, as well as between emotional response and perceived risk. These associations were weakest in the low-automation, low-criticality scenario and strengthened with increasing criticality levels. Mediation analyses assessed the role of emotional response in the perceived risk–trust relationship. In the UK sample, emotional response fully mediated the risk–trust relationship in the low-automation, low-criticality scenario and partially mediated this relationship in the remaining scenarios. In the Arab sample, emotional response partially mediated the risk–trust relationship in all scenarios. Gender and age showed varying influences in both samples. These findings underscore the critical role of emotional response in shaping users’ trust across cultures and AI modalities, significantly influencing trust alongside cognition. Awareness of emotional sources enables users to build trust based on both rational and emotional aspects, highlighting the inadequacy of cognition alone in achieving effective trust calibration. Enhancing system performance, addressing perceived risks, and promoting positive affect can foster greater user trust.

Delegating socially significant roles to artificial intelligence (AI) is an emerging reality, yet little is known about how publics evaluate this transfer of responsibility across contexts and countries. This study applied a structural model to a large cross-national dataset (30,994 individuals in 35 countries) to test how cognitive appraisals, affective dispositions, and contextual factors jointly shape willingness to delegate socially important roles of companionship, mental health advisor, doctor and teacher to children to AI. The results revealed a robust hierarchy of delegation preferences, with companionship most frequently entrusted to AI, followed by mental-health advisor, teacher, and doctor. Cognitive appraisals emerged as the strongest predictors: trust in online information was consistently the most powerful driver across all roles, while optimism and life satisfaction made smaller but reliable contributions. Affective dispositions played narrower, domain-specific roles, with anxiety shaping delegation in teaching and mental health, and loneliness linked only weakly to companionship. Women were less willing than men to delegate across all roles, with the gender gap largest in medicine and education, and strikingly invariant across cognitive and affective predictors. Beyond these, national baselines diverged by nearly 30 percentage points even after adjusting for these predictors demonstrating the independent influence of country context. Our findings show that willingness to delegate socially important roles to AI follows a robust hierarchy and reflects the combined influence of cognitive appraisals, affective dispositions, and contextual factors. A key implication is that delegation roles to AI must be understood as both a personal and a societal orientation, requiring attention to the interplay between these layers.

As artificial intelligence (AI) systems increasingly assume roles with social, educational, and emotional significance, understanding the psychological drivers behind individuals' readiness to delegate such roles to AI is crucial. Drawing on Self-Determination Theory (SDT), this study examines how the satisfaction of basic psychological needs (autonomy, competence, and relatedness) predicts individuals' readiness to delegate socially significant roles to AI across four domains (education, healthcare, mental health, and companionship) and 35 nations. Using data from over 35,000 participants in the 2023 Global Digital Wellbeing Survey, we applied Bayesian multilevel multivariate modelling to assess both global and culture-specific motivational associations. Results revealed that competence emerged as the most consistent predictor of AI delegation across roles, particularly in high-stakes, expertise-driven contexts (healthcare and education). Autonomy showed reliable positive associations in socially and emotionally salient domains (mental health support and companionship). Relatedness showed the weakest and most inconsistent effects but exhibited culturally contingent activation in regions such as the Confucian-Asian and Arab-Islamic clusters. Notably, motivational effects were not uniform across cultures: for example, the influence of autonomy was amplified in South Asian regions, while Nordic countries exhibited low delegation readiness despite culturally autonomy-supportive values. These findings highlight the potential importance of psychological need satisfaction and its cultural modulation in shaping global AI adoption. They also suggest that AI systems should be designed not only for functionality but also for motivational alignment. We discuss implications for ethical design, cross-cultural deployment, and the future of human-AI interaction in socially meaningful domains.

PurposeVarious studies have established a strong connection between excessive smartphone use, personality traits (PTs), and various health issues. Excessive smartphone use has been associated with physical and mental health issues and sleep disturbances. Some PTs play a protective role against excessive smartphone usage, while others are more prone to smartphone use to further understand these relationships. Exploratory graph analysis (EGA) has been employed to explore the relationship between PTs and category-wise smartphone app usage in relation to inferred sleep patterns.MethodThis study analyzed data from 269 participants to explore the relationships among PTs, category-wise app usage, and inferred sleep patterns. App usage categories were extracted from smartphone usage data collected through a dedicated application. Sleep variables were inferred from periods of nonusage during the human sleep-wake cycle. Additionally, EGA was utilized to examine and visualize the associations between PTs, category-wise smartphone usage, and inferred sleep patterns.ResultsAverage smartphone use emerges as a central node, strongly linked to app categories and sleep variables, with higher usage correlating positively with social media, communication, and video streaming apps while negatively impacting sleep duration. PTs influence app usage patterns, with neuroticism associated with social media and communication apps, and conscientiousness negatively linked to gaming and video streaming.ConclusionThis study reveals key relationships between PTs, category-wise smartphone app usage, and inferred sleep patterns. Conscientiousness emerged as a protective factor, correlating with lower total mobile usage, less engagement in communication, video streaming, and gaming apps, and fewer sleep disturbances. In contrast, neuroticism was linked to higher smartphone use, increased use of social media and communication apps, and poorer sleep quality. App usage patterns revealed that social media, communication, and video streaming apps negatively affect sleep by delaying bedtime, while overall mobile usage primarily disrupts nighttime routines rather than morning wake-up times. These findings emphasize the need for targeted interventions that consider PTs and app categories to promote healthier digital habits and improve sleep patterns.

This study examines the impact of two key AI modalities - freedom of choice (FoC) and social proof (SP) - on public attitudes toward AI, focusing on cultural differences between UK and Arab participants. FoC refers to the option of selecting a non-AI, possibly human, alternative, while SP means knowing that others have used AI without issues. Four scenarios were designed, combining the presence or absence of these modalities. The context was a customer service chatbot for a telecommunications company, familiar to all participants. A total of 639 participants (316 British and 323 Arab) were introduced to the modalities and then the scenarios in randomised order, then asked about their reactions. Factor analysis grouped their responses into two categories: personal and social good, and risks and ethical concerns. Results indicate that both modalities positively influence perceptions of personal and social benefits of AI while reducing perceived risks and ethical concerns. When one modality was present, FoC had a stronger effect on improving positive perceptions and reducing concerns than SP. Cultural differences were minor but present, suggesting both groups generally respond similarly. Findings highlight the importance of providing a human alternative and avoiding reliance solely on SP or similar strategies to build trust in AI.

Accurate classification of logos is a challenging task in image recognition due to variations in logo size, orientation, and background complexity. Deep learning models, such as VGG16, have demonstrated promising results in handling such tasks. However, their performance is highly dependent on optimal hyperparameter settings, whose fine-tuning is both labor-intensive and time-consuming. Swarm intelligence algorithms have been widely adopted to solve many highly nonlinear, multimodal problems and have succeeded significantly. The Hunger Games Search (HGS) is a recent swarm intelligence algorithm that has shown good performance across various applications. However, the standard HGS still faces limitations, such as restricted population diversity and a tendency to get trapped in local optima, which can hinder its effectiveness. In this paper, we propose an optimized deep learning architecture called EHGS-VGG16 designed based on the VGG16 model and boosted by an enhanced Hunger Games Search (EHGS) algorithm for hyperparameter tuning. The proposed enhancement to HGS involves modified search strategies, incorporating the concepts of ”local best” and a ”local escaping mechanism” to improve its exploration capability. To validate our approach, the evaluation is conducted in three folds. First, the EHGS algorithm is evaluated through 30 real-valued benchmark functions from the IEEE CEC2014 suite. Second, a custom-developed VGG16 model is tested on the Flickr-27 logo classification dataset and compared against state-of-the-art deep learning models such as ResNet50V2, InceptionV3, DenseNet121, EfficientNetB0, and MobileNetV2. Finally, EHGS is integrated into the VGG16 model to optimize its hyperparameters. The experimental results show that VGG16 outperformed the other counterparts with an accuracy of 0.956966, a precision of 0.957137, and a recall of 0.956966. Moreover, the integration of EHGS further improved classification quality by 3%. These findings highlight the potential of combining evolutionary optimization techniques with deep learning for enhanced accuracy in log classification tasks.

In many studies addressing smartphone usage, reliance on self-reported data, typically collected through questionnaires, has been commonplace. However, these investigations often offered a broad overview of overall smartphone usage without delving into specific app categories. This study, in contrast, employed a dataset derived from a smartphone application that objectively recorded user activities, encompassing details such as accessed apps and the initiation and termination times of each app session. Our analysis focused on discerning patterns of social media engagement within the subset of SPACE app utilized. The inferential analysis utilized the Mann–Whitney U test. Notably, the findings unveiled that youngsters exhibit a higher smartphone usage duration compared to grownups. Additionally, a gender-based disparity was observed, with females spending more time on social media than their male counterparts. Furthermore, females demonstrated a higher likelihood of initiating social media apps in comparison to males. This research, grounded in objective data, provides a nuanced understanding of social media engagement.

This paper discusses the shift from paper-based academic services to e-services, which has become prevalent in College of Technological Innovation (CTI), UAE. However, the usability of these e-services is a challenge due to design issues, and This paper introduces a usability assessment study focusing on the e-services available through the CTI’s academic portal. The study employs eye tracking to examine the viewing, searching, and navigation behavior of college students, along with the factors that impact their searching behavior. The study focuses on the students’ visual patterns in choosing an e-service. Eye-tracking experiments were conducted, and data were collected from a group of CTI students. According to the findings of the heatmap and eye gaze analysis, students had trouble locating the right link for the desired service and displayed intense, disoriented scattered, and erratic visual behaviors when performing assignments. We discovered several user interface design issues that hinder student productivity. Based on the results of the usability assessment study, the research recommends changes to the CTI portal’s design to better suit students’ needs, preferences, and expectations.

This paper describes how the gazing pattern differ between the responses of Normal Developing (ND) and Autistic (AP) children to sad emotion. We employed an eye tracking technology to collect and track the participants’ eye movements by showing a dynamic stimulus (video) that showed a gradual transition from pale emotions to melancholy facial expressions in both female and male faces. The location of the child's gaze in the stimulus was the focus of our data analysis. We deduced that there was a distinction between the two groups based on this. ND children predominantly concentrated on the eyes and mouth region of both male and female sad faces, but AP children showed no interest in these areas by glancing away from the stimuli faces. Based on the findings, an ideal eye tracking model for early ASD diagnosis can be constructed. This will aid in the early treatment of Autism children as well as the development of socio-cognitive skills.

The distribution style for university academic services has undergone a significant paradigm shift toward electronic services over the past ten years. Services offered by Zayed University (ZU) are not distinctive. The students might access a sizable variety of services and information online with just a few mouse clicks or finger touches. However, several obstacles still prevent students from taking full advantage of these services because of serious usability problems with the way portals were created. This paper presents a usability assessment study of e-services provided to students through the academic portal of Zayed University. It explores the viewing, searching, and navigation behavior of young Arab female students as they engage with the university academic portal and factors that affect their searching behavior using an eye tracking methodology and analysis. The study focuses on the visual and viewing behaviors of the students as they make their initial decision to use an e-service (click). To gather data on students' navigational patterns, an eye tracking experiment was created and carried out utilizing the current designs of several e-services accessible to students. It has been recorded, gathered, and analyzed how the participants navigated the list of e-services and interacted with it. The study's results suggest that, on average, students spent more time checking the e-services homepage for the appropriate link to the desired service. Surprisingly, a large percentage of students had never used the university's online services which affect the usability rate of ZU web portal. From the viewpoints and expectations of the students, a set of recommendations were made to enhance the usability aspect and design of the ZU main portal.

This study examined the effects of gender, age, objective smartphone usage data, and Emotional Intelligence (EI) on Problematic Internet Use (PIU) and its components (obsession, neglect, and control disorder). The study relied on objective data of smartphone usage as a representative of technology use collected by a monitoring application of smartphone usage. PIU and EI were measured through the Problematic Internet Usage Questionnaire short form (PIUQ-SF-6) and Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF), respectively. The current cross-sectional study was carried out with 268 participants (Female: 61.6%, ages from 15 to 64) from ten different countries. The analysis was performed using multiple linear regression. The results of the multiple regression models showed that gender and age did not reveal a significant influence on PIU or its components. Smartphone usage had a positive and significant effect on PIU, while EI inversely and significantly affected PIU and accounted for 24.6% of PIU total variance. Similarly, smartphone usage and EI significantly affected the PIU components, accounting for 15.9% of obsession variance, 12.9% of neglect variance, and 16.4% of control disorder variance. Our findings contribute to the literature by objectively evaluating the influence of time spent using the internet on PIU. It is one of the first studies to rely on objectively measured smartphone usage data and compare findings to previous studies that relied on self-reported data. When used to regulate usage, the monitoring applications of smartphone usage should be better contextualized to reflect users’ psychometrics.

Purpose:The growing awareness and concern about the excessive use of social media have led to an increasing number of studies investigating the underlying factors contributing to this behavior. In the literature, it is discussed that problematic social media use (PSMU) can impact individuals' mental health and well-being. Drawing on the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, this study aimed to examine the association between the need for affect (affect approach and affect avoidance) and PSMU (operationalized via the social media disorder scale), as well as the mediating role of fear of missing out (FoMO) in that relation.Participants and Methods:Data were collected via an online survey from 513 participants in European and Arabic countries. Regression and mediation analyses were conducted to explore the relationships between affect approach, affect avoidance, FoMO, and PSMU.Results:Regression analysis results indicated that both affect approach and affect avoidance as part of the need for affect construct significantly predicted PSMU in both cultural contexts. Mediation analysis showed that FoMO partially mediated the relationship between affect approach and PSMU in the Arab sample but not in the European sample. Beyond this, FoMO partially mediated the relationship between affect avoidance and PSMU in both cultural samples.Conclusion:The present study indicates that managing emotions could be an effective strategy to combat PSMU. In line with this and against the background of the data business model behind social media companies, we deem it to be of importance to minimize triggers related to FoMO in the design of social media platforms (for example, push notifications). This might be particularly relevant for individuals with a high inclination towards affect approach and affect avoidance.

ObjectiveThis study aims to explore the user archetypes of health apps based on average usage and psychometrics. MethodsThe study utilized a dataset collected through a dedicated smartphone application and contained usage data, i.e. the timestamps of each app session from October 2020 to April 2021. The dataset had 129 participants for mental health apps usage and 224 participants for physical health apps usage. Average daily launches, extraversion, neuroticism, and satisfaction with life were the determinants of the mental health apps clusters, whereas average daily launches, conscientiousness, neuroticism, and satisfaction with life were for physical health apps. ResultsTwo clusters of mental health apps users were identified using k-prototypes clustering: help-seeking and maintenance users and three clusters of physical health apps users were identified: happy conscious occasional, happy neurotic occasional, and unhappy neurotic frequent users. ConclusionThe findings from this study helped to understand the users of health apps based on the frequency of usage, personality, and satisfaction with life. Further, with these findings, apps can be tailored to optimize user experience and satisfaction which may help to increase user retention. Policymakers may also benefit from these findings since understanding the populations' needs may help to better invest in effective health technology.

Most research on Problematic Internet Usage (PIU) relied on self-report data when measuring the time spent on the internet. Self-reporting of use, typically done through a survey, showed discrepancies from the actual amount of use. Studies exploring the association between trait emotional intelligence (EI) components and the subjective feeling on technology usage and PIU are also limited. The current cross-sectional study aims to examine whether the objectively recorded technology usage, taking smartphone usage as a representative, components of trait EI (sociability, emotionality, well-being, self-control), and happiness with phone use can predict PIU and its components (obsession, neglect, and control disorder). A total of 268 participants (Female: 61.6%) reported their demographic and completed a questionnaire that included Problematic Internet Usage Questionnaire short form (PIUQ-SF-6), Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF), level of happiness with the amount and frequency of smartphone use, and living conditions (whether alone or with others). Their smartphone usage was objectively recorded through a dedicated app. A series of one-way ANOVA revealed no significant difference in PIU for different living conditions and a significant difference in the subjective level of happiness with phone usage (F (3, 264) = 7.55, p < .001), as well as of the frequency of usage where the unhappy group had higher PIU (F (3, 264) = 6.85, p < .001). Multiple linear regression analysis showed that happiness with phone usage (β = -.17), the actual usage of communication (β = .17), social media (β = .19) and gaming apps (β = .13), and trait EI component of self-control (β = -.28) were all significant predictors of PIU. Moreover, gender, age, and happiness with the frequency of phone usage were not significant predictors of PIU. The whole model accounted for the total variance of PIU by 32.5% (Adjusted R2 = .287). Our study contributes to the literature by being among the few to rely on objectively recorded smartphone usage data and utilizing components of trait EI as predictors.

This paper aims to objectively compare the use of mental health apps between the pre-COVID-19 and during COVID-19 periods and to study differences amongst the users of these apps based on age and gender. The study utilizes a dataset collected through a smartphone app that objectively records the users’ sessions. The dataset was analyzed to identify users of mental health apps (38 users of mental health apps pre-COVID-19 and 81 users during COVID-19) and to calculate the following usage metrics; the daily average use time, the average session time, the average number of launches, and the number of usage days. The mental health apps were classified into two categories: guidance-based and tracking-based apps. The results include the increased number of users of mental health apps during the COVID-19 period as compared to pre-COVID-19. Adults (aged 24 and above), compared to emerging adults (aged 15–24 years), were found to have a higher usage of overall mental health apps and guidance-based mental health apps. Furthermore, during the COVID-19 pandemic, males were found to be more likely to launch overall mental health apps and guidance-based mental health apps compared to females. The findings from this paper suggest that despite the increased usage of mental health apps amongst males and adults, user engagement with mental health apps remained minimal. This suggests the need for these apps to work towards improved user engagement and retention.

In response to the COVID-19 pandemic, many governments have attempted to reduce virus transmission by implementing lockdown procedures, leading to increased social isolation and a new reliance on technology and the internet for work and social communication. We examined people's experiences working from home in the UK to identify risk factors of problematic internet use during the first lockdown period, specifically looking at life satisfaction, loneliness, and gender. A total of 299 adults completed the Problematic Internet Use Questionnaire-Short-Form-6, UCLA-3 Item Loneliness Scale, and Satisfaction with Life Scale online. Through structural equation modelling, we found that loneliness positively predicted problematic internet use while gender had no effect. Life satisfaction and age positively predicted loneliness but had no direct effect on problematic internet use, suggesting loneliness fully mediated their relationship with problematic internet use. Our study serves as a benchmark study of problematic internet use among those working from home during lockdown conditions, which may be utilized by future researchers exploring longitudinal patterns post-pandemic.

Through the use of eye tracking equipment, this study compares the behavior and gaze patterns of persons who have autism (AP) and participants who are typically developing (ND). Participants in the experiment are given access to a video with a happy face expression. The participants' gaze patterns are recorded and tracked using eye tracking technology. We discovered a substantial difference in both participant groups' visual behavior through heat map analysis, primarily in the stimulus's area of interest. The eyes and mouth area of a happy face expression received minimal attention from AP, who were primarily interested in non-facial regions. On the other hand, the happy face stimuli's mouth and eyes catch the attention of ND individuals. These findings can be used to create new techniques for early ASD detection and for enhancing the skills and abilities of autistic children.