Computers in Human Behavior最新文献

The potential of Generative Artificial Intelligence (AI) in language education has been widely recognized. However, there has been limited attention given to the emotional experiences of language teachers using AI and its relationship with AI-enabled productivity. By investigating 1,683 pre-service language teachers’ experiences of using generative AI in their teaching practicum or learning, this study explored how teachers’ emotional responses to AI use in teaching and learning are related to their AI-enabled productivity through the mediation of appraisal and coping. We uncovered several key findings: (1) achievement, challenge, and loss emotions were directly and/or indirectly related to AI-enabled productivity, while deterrence emotions were not; (2) achievement and challenge emotions were positively correlated with challenge appraisal and negatively correlated with hindrance appraisal, whereas loss and deterrence emotions showed the opposite pattern of correlation; (3) challenge emotions were positively related to approach-oriented coping, while loss and deterrence emotions were positively associated with avoidance-oriented coping; (4) among the coping strategies, only positive reinterpretation was positively associated with AI-enabled productivity; and (5) challenge appraisal and positive reinterpretation were significant mediators in the relationships between emotions and AI-enabled productivity, either separately or sequentially. These findings provide valuable insights for future research and practice, aiming to support the application of generative AI in the context of language education.

The current study aimed to examine the bi-directional relationships between pre-sleep electronic media use and sleep, and to assess the relative predictive strength of different types of electronic media use in the evening on subsequent sleep. Eighty-eight young adults (20.57 ± 0.99, 27 males) completed an online intake survey and received notifications to complete daily diaries for seven consecutive days. Results of dynamic structural equation models (DSEM) revealed that the total screen time, sleep onset time, sleep latency, sleep duration, and sleep quality showed significant positive autoregressive parameters. In contrast, the results revealed no statistically significant cross-lagged associations between the total screen time in the evening and sleep outcomes. Inspection of the predictive strength of various types of screen activities at the within-person level revealed that increased active social media use is linked to delayed sleep onset, while internet browsing is associated with shorter sleep latency. At the between-person level, screen time of T.V. watching, video streaming, social media use, and shopping correlated with delayed sleep onset. Shopping was also connected to longer sleep latency. Additionally, more time spent on T.V., video streaming, non-work internet browsing, and shopping was linked to reduced total sleep duration. These findings indicated a screen activity-dependent association between pre-sleep screen time and subsequent sleep. More objective assessments of electronic media use (e.g., app recording screen time) and sleep (e.g., actigraphy) and longitudinal monitoring over a relatively long period (i.e., several weeks or months) are warred to further elucidate (cross-lagged) relationships between electronic media use and sleep.

Health conditions, encompassing both physical and mental aspects, hold an influence that extends beyond the individual. These conditions affect personal well-being, relationships, and financial stability. Innovative strategies in healthcare, such as digital phenotyping, are strategic to mitigate these impacts. By merging diverse data sources, digital phenotyping seeks a comprehensive understanding of health, well-being, and behavioral conditions. Machine learning can enhance the analysis of these data, improving the comprehension of health and well-being. Therefore, this paper presents a systematic literature review on machine learning and digital phenotyping, examining the research field by filtering 2,860 articles from eleven databases published up to November 2023. The analysis focused on 124 articles to answer six research questions addressing machine learning techniques, data, devices, ontologies, and research challenges. This work presents a taxonomy for mapping explored areas in digital phenotyping and another for organizing machine learning techniques used in digital phenotyping research. The review found increased publications in 2023, indicating a growing interest in the field. The main challenges arise from the studies’ small participant samples and imbalanced datasets, limiting the generalizability of the results to broader populations and the choice of ML methods. Furthermore, the reliance on self-reported data can introduce potential inaccuracies due to recall and reporting biases. Beyond self-reports, authors explored different data types, including physiological, clinical, contextual, smartphone-based, and multimedia. Despite using video recordings in controlled experiments, studies have yet to investigate this method within intelligent environments. Researchers also analyzed neurophysiological phenotypes, suggesting the potential for interventions based on these characteristics.

Rural education frequently grapples with demanding situations such as isolation, confined assets, and a virtual divide. The emergence of the sixth generation (6G) era, characterized by its speedy connectivity, minimal latency, and robust reliability, stands out as a beacon of desire, promising to revolutionize these educational landscapes by adopting ubiquitous Learning (U-Learning) environments. This research delves into the transformative skills of the 6G generation in rural schooling through U-Learning. It highlights 6G's position in ensuring identical entry to advanced instructional materials, fostering immersive and tailored learning stories, and permitting immediate interplay within virtual school rooms. Employing a combined-methods framework, this look intertwines an in-depth literature assessment with empirical inquiries, such as case studies, surveys, and dialogues with critical rural schooling stakeholders. It scrutinizes the prevailing situations of pastoral training, 6G's technological advancements, and the conditions for deploying green U-Learning ecosystems. The findings indicated significant improvements in educational resource access, student engagement, and learning outcomes due to implementing 6G technology. The key benefits observed were high-speed connectivity, reduced latency, and enhanced reliability, facilitating more interactive and immersive learning experiences. 6G emerges as a pivotal pressure in reshaping rural education, addressing essential problems of accessibility, pupil engagement, and academic excellence. Achieving this vision needs concerted efforts in infrastructure improvement, instructional programming tailored to 6G, and collaborative endeavors amongst all stakeholders. Future research should focus on developing specific 6G deployment strategies and educational programs tailored to the unique needs of rural populations.

While equality and inclusivity are promoted in the realm of esports, most gamer communities remain male-dominated, which may give rise to prejudices against female players and their skill. In this study, we conducted two experiments where we measured how participants evaluate players’ performance and agency attributions. Using two distinct experimental manipulations—one utilizing videos of players playing (n = 312) and the other using narrative vignettes (n = 338)—we found that males are rated mostly based on their gaming outcomes, while females are more often judged based on pre-existing (prejudiced) preconceptions. These results contribute to the literature on gaming-related stereotypes by enhancing the understanding of societal prejudices in the evaluation of players’ skills.

In a broader context of increasing incidences of sexting-related backlashes resulting in victims’ depression and, at times, suicide in Nigeria, this study examines the prevalence, trends, and mental health implications of sexting among 700 Nigerian social media users. With the help of the Patient Health Questionnaire-9 and the Sexting Behaviors and Motives Questionnaire, we found that 58% of respondents engaged in sexting, a high percentage given the cultural conservatism of Nigeria. In addition, more than 41% admitted forwarding or having another forward sexted images or messages without the victims’ consent, increasing the risk of cyberbullying and subsequent mental health problems. In our study, we found a strong positive relationship between sexting and depression; the effects of sexting on depression differed for men and women: Men sexters exhibited higher depression levels than women. Our analysis, which employed descriptive, regression, and Structural Equation Model (SEM) methodologies, suggests that despite regional cultural disparities, sexting behaviors are surprisingly uniform across Nigeria. This study underscores the urgent need for informed strategies addressing digital privacy, security, and mental well-being in the context of sexting in Nigeria.

Current developments in telemedicine and artificial intelligence (AI) are significantly impacting doctor-patient interactions. This study examined the interacting role of individual traits with different levels of digitalization in participants' user preferences and trust within hypothetical medical scenarios. Specifically, preferences and trust levels towards various digitalized and analog formats, such as face-to-face interactions, video calls, written exchanges with a doctor or chatbot, or conversations with AI avatars were compared using standard scenarios of varying health risks and potentially embarrassing content.

In an online discrete choice experiment, 1009 participants rated hypothetical scenarios of varying medical concerns regarding their preferred conversation format and trust. User preference (n = 2018 observations) and trust (n = 9880 observations) were predicted using two multilevel models.

Higher perceived efficiency of digital conversation formats predicted user preference for digitalized formats. However, users’ preference for digitalized formats was generally lower compared to face-to-face interactions, especially when receiving bad news. The level of digitalization was negatively associated with trust, which was lower for consultations that involved receiving bad news or discussing potentially embarrassing content compared to good news. Trust ratings varied depending on the conversation topic.

When comparing analog and digitalized medical consultation scenarios, digitalized medical consultations are not equally suited for every medical consultation. Participants preferred personal contact, particularly when bad news needed to be communicated. Additionally, trust in the doctor significantly varies depending on the topic of conversation.

While many studies have investigated the impact of artificial intelligence (AI) deployment in the public sector on government-citizen interactions, findings remain controversial due to the technical complexity and contextual diversity. This study distinguishes between rule-driven and learning-driven AI and explores their impact as automated respondents on citizen-initiated contact, an important scenario for public participation with initiative. Based on a conjoint experiment with 763 participations (4578 observations), this study suggests that AI deployments enormously reduce the likelihood of citizen-initiated contact compared to human response, with learning-driven AI having a higher negative effect than rule-driven AI. In addition, the causal effects of respondent image, contact channel, contact purpose, and matter attributes on citizen-initiated contact, as well as their moderating effects, are explored. These findings make theoretical implications and calls for public participation in the roaring AI deployment in the public sector.

K-pop idols meticulously manage their public image on social media by blending aspects of professional and personal self-presentation, occasionally incorporating paid advertisements. This research aims to investigate these strategies on idols’ personal Instagram accounts and their impacts on fans. Two studies were conducted. In Study 1, a content analysis of the ten most recent Instagram posts from 26 K-pop idols revealed a combination of both self-presentation types with a slightly higher prevalence of personal one. One-fourth of the posts contained paid advertisements, and they were more prevalent in professional self-presentation. Posts featuring personal self-presentation garnered significantly more likes and comments, and so did posts without paid advertisements. Study 2, an online experiment with 240 Korean Instagram users, compared professional and personal self-presentation with and without paid advertisement. The results showed that paid advertisement significantly influenced perceptions of the idol, diminishing perceived authenticity, professionalism, and para-social relationship. In contrast, professional self-presentation heightened only perceived professionalism compared to personal self-presentation. Engagement intentions did not significantly differ between self-presentation types but were lowered by paid advertisement indirectly through reduced perceived para-social relationship. This research highlights the complex dynamics of online self-presentation, fan engagement, and the potential drawbacks of commercialization in K-pop.

This study innovatively applied smartphone deprivation and aimed to assess its behavioral and physiological effects in problematic smartphone users. In Experiment 1, 283 participants completed an investigation related to smartphone use, including 92 participants identified as problematic smartphone users. Based on an average smartphone unlocking interval of 19.67 min, the duration of smartphone deprivation was set to 20 min. After deprivation, the participants exhibited a significant increase in smartphone cravings and a decrease in heart rate. Experiment 2 applied a modified cueing task to explore problematic smartphone users’ attentional bias, in which smartphone-related and neutral pictures were selected as the materials. There were 22 and 23 participants in the smartphone-deprivation and control groups, respectively. In the deprivation group, reaction times (RTs) for smartphone-related pictures were longer than for neutral pictures. In Experiment 3, we incorporated electroencephalography technology to also assess a smartphone turned-off group. There were 16, 21, and 14 participants in smartphone deprivation, smartphone turned-off, and control groups, respectively. In both the smartphone deprivation and smartphone turned-off groups, participants had longer RTs on smartphone-related pictures than on neutral pictures. Furthermore, for participants in the deprivation group, N2 latencies in relation to smartphone-related pictures were earlier than those in relation to neutral pictures, while participants in the turned-off group had larger P3 amplitudes and earlier P3 latencies for invalid cues compared with valid cues.