This research explores the advancement of Artificial Intelligence (AI) in Occupational Health and Safety (OHS) across high-risk industries, highlighting its pivotal role in mitigating the global incidence of occupational incidents and diseases, which result in approximately 2.3 million fatalities annually. Traditional OHS practices often fall short in completely preventing workplace incidents, primarily due to limitations in human-operated risk assessments and management. The integration of AI technologies has been instrumental in automating hazardous tasks, enhancing real-time monitoring, and improving decision-making through comprehensive data analysis. Specific AI applications discussed include drones and robots for risky operations, computer vision for environmental monitoring, and predictive analytics to pre-empt potential hazards. Additionally, AI-driven simulations are enhancing training protocols, significantly improving both the safety and efficiency of workers. Various studies supporting the effectiveness of these AI applications indicate marked improvements in risk management and incident prevention. By transitioning from reactive to proactive safety measures, the implementation of AI in OHS represents a transformative approach, aiming to substantially reduce the global burden of occupational injuries and fatalities in high-risk sectors.

The achievement of sustainable development in Kenya has been hindered by the prevalence of HIV. The effects of HIV on sustainable development have been given less academic attention. HIV prevalence prevents people from achieving good health and well-being, which then makes them unable to conduct activities that lead to sustainable economic growth. The paper found that the prevalence of HIV causes economic hardship, destroys human capital development and human resources by reducing life expectancy and increasing mortality rates. It was equally found that the prevalence of HIV undermines social stability and mobility, reduces economic investments, influences food insecurity and makes people vulnerable. The paper found that the prevalence of HIV reduces labor supply and productivity, increases the cost of health services, promote inequality and poverty. The paper found that the prevalence of HIV was caused by the failure to integrate religion, culture and science infrastructure to achieve a holistic treatment acceptance and adherence that would overcome all misconceptions people have towards the disease. The paper found that while science provides effective HIV treatments, religious and cultural perspectives often shape community attitudes toward the disease. It was found that engaging religious and cultural as well as health workers or health advocates can help reduce stigma and promote ART adherence by aligning treatment messages with faith-based principles. The paper found that the integration that incorporates religion, culture, and science into HIV interventions would promote a more inclusive healthcare system that respects diverse beliefs while ensuring evidence-based treatment is accessible and widely accepted. The study was conducted through a qualitative methodology. Data was collected from secondary sources that included published articles, books and occasional papers as well as reports. Collected data was interpreted and analyzed through document analysis techniques.

The integration of chatbots in the financial sector has significantly improved customer service processes, providing efficient solutions for query management and problem resolution. These automated systems have proven to be valuable tools in enhancing operational efficiency and customer satisfaction in financial institutions. This study aims to conduct a systematic literature review on the impact of chatbots in customer service within the financial sector. A review of 61 relevant publications from 2018 to 2024 was conducted. Articles were selected from databases such as Scopus, IEEE Xplore, ARDI, Web of Science, and ProQuest. The findings highlight that efficiency and customer satisfaction are central to the perception of service quality, aligning with the automation of the user experience. The bibliometric analysis reveals a predominance of publications from countries such as India, Germany, and Australia, underscoring the academic and practical relevance of the topic. Additionally, essential thematic terms such as “artificial intelligence” and “advanced automation” were identified, reflecting technological evolution in this field. This study provides significant insights for future theoretical, practical, and managerial developments, offering a framework to optimize chatbot implementation in highly regulated environments.

The purpose of this study was to assess rural students’ computational thinking abilities. The following proofs were observed: (1) Students’ abstraction affected algorithmic thinking skills; (2) Students’ decomposition influenced algorithmic thinking skills; (3) Students’ abstraction impacted evaluation skills; (4) Students’ algorithmic thinking affected evaluation skills; (5) Students’ abstraction impacted generalization skills; (6) Students’ decomposition impacted generalization skills; (7) Students’ evaluation affected generalization skills. Gender differences were observed in the relationship among the computational thinking factors of junior high school students. This included the abstraction-generalization skills; evaluation-generalization skills; and decomposition-generalization skills relationships, which were moderated by the gender of the students. 258 valid surveys were collected, and they were utilized in the study. Conducting the descriptive, reliability, and validity analyses used SPSS software, and the structural equation modeling (SEM) was also conducted through Smart PLS software to assess the hypothetical relationships. There were gender disparities in the correlation among computational thinking components of the junior high school students’ studying in rural areas. Research has shown that male and female students may have different abstractions, evaluations, and generalizations related to computational thinking, with females being more strongly associated than males in non-programming learning contexts. These results are expected to provide relevant information in subsequent analyses and implement a computational thinking curriculum to overcome the still-existing gender gaps and promote computational thinking skills.

This study investigates the impact of human resource management (HRM) practices on employee retention and job satisfaction within Malaysia’s IT industry. The research centered on middle-management executives from the top 10 IT companies in the Greater Klang Valley and Penang. Using a self-administered questionnaire, the study gathered data on demographic characteristics, HRM practices, and employee retention, with the questionnaire design drawing from established literature and validated measuring scales. The study employed the PLS 4.0 method for analyzing structural relationships and tested various hypotheses regarding HRM practices and employee retention. Key findings revealed that work-life balance did not significantly impact employee retention. Conversely, job security positively influenced employee retention. Notably, rewards, recognition, and training and development were found to be insignificant in predicting employee retention. Additionally, the study explored the mediating role of job satisfaction but found it did not mediate the relationship between work-life balance and employee retention nor between job security and employee retention. The research highlighted that HRM practices have diverse effects on employee retention in Malaysia’s IT sector. Acknowledging limitations like sample size and research design, the study suggests the need for further research to deepen understanding in this area.