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This study examines the role of Artificial Intelligence (AI) in enhancing student collaborative learning, with a particular emphasis on AI-driven feedback mechanisms and patterns of student interaction in developing effective collaborative skills. Unlike prior studies, this research highlights the mediating effect of AI-driven feedback on teamwork efficiency and overall learning outcomes in collaborative environments. An explanatory quantitative approach was applied using Partial Least Squares Structural Equation Modeling (PLS-SEM) to ensure robust data analysis. Data were collected from 112 university students who were actively engaged in AI-assisted collaborative learning activities, using a structured online survey instrument. The data were subsequently analyzed using SmartPLS software. The results reveal that AI significantly enhances student interaction (β = 0.534, p < 0.000) and improves problem-solving feedback (β = 0.620, p < 0.000), both of which contribute to significantly strengthening collaborative skills (β = 0.716, p < 0.000). However, the findings also indicate that AI alone does not directly improve collaboration without the support of structured pedagogical design and guidance. Therefore, universities should strategically integrate AI-driven feedback into Learning Management Systems (LMS) and strengthen digital literacy initiatives to optimize the effectiveness and sustainability of AI in collaborative learning contexts.

The digital era has fundamentally revolutionized human interaction and information processing, particularly for Generation Z as digital natives. This research aims to identify the characteristics of digital information consumed by Generation Z and evaluate their long-term memory (LTM) capacity in the digital age. Using a Systematic Literature Review (SLR) method with a qualitative approach, 15 relevant studies published between 2021 and 2026 were analyzed from databases such as Scopus, Google Scholar, and ScienceDirect. The findings indicate that digital information characteristics—which are often "bite-sized," visual, and rapidly consumed—tend to encourage shallow encoding and digital amnesia. However, the study also found a dual impact: while high cognitive load and constant multitasking can hinder memory consolidation, structured digital use and interactive learning modules can significantly enhance memory retention. These results imply that optimal cognitive function in the digital era highly depends on digital literacy and effective information management strategies to balance technological use with natural cognitive sharpness.

Academic productivity is a vital indicator of student success in higher education, but it is often hampered by the complexity of tasks, transitions in the learning environment, and digital distractions that trigger procrastination. This phenomenon demands a high degree of adaptability so that students do not become trapped in physical and mental exhaustion due to unmanaged workloads. Therefore, this study aims to analyze in depth the causal relationship between time management skills and academic productivity, as well as investigate their role in mitigating academic stress levels in students. The research method applied is a literature review with a qualitative-descriptive approach. The research data was sourced from secondary data in the form of 21 reputable scientific articles (national and international journals) published between 2020 and 2025. The data analysis process was carried out through the stages of data reduction, synthesis of findings, and narrative conclusion drawing to systematically map the relationship between variables. The results and discussion of the study show that time management has a significant positive correlation with improved learning achievement. Specific indicators such as daily schedule planning, priority setting, and self-regulation have been empirically proven to increase task completion efficiency and Grade Point Average (GPA) achievement. Conversely, poor time management was identified as a major predictor of cognitive overload and exhaustion, especially among students with dense curricula such as engineering majors. This study concludes that mastery of time management is not merely a scheduling tool, but a fundamental cognitive strategy that functions as a coping mechanism to maintain mental health and achieve an optimal study-life balance.

Primary Healthcare Facilities (Fasilitas Kesehatan Tingkat Pertama, FKTP) represent the first level of contact in the healthcare system and play a central role in infection prevention and control. Despite mandatory Infection Prevention and Control (IPC) training in Indonesia, evidence regarding its effectiveness in improving cognitive abilities among primary healthcare workers remains limited. This study aimed to evaluate the effectiveness of IPC training in enhancing the cognitive abilities of healthcare workers in FKTP. A quasi-experimental study with a one-group pretest–posttest design was conducted involving 91 healthcare workers who participated in IPC training across three cohorts in 2024. The training was delivered online through a Learning Management System and consisted of structured learning modules accompanied by a pre-test and a final quiz. Cognitive improvement was assessed using paired samples t-tests, while the magnitude of training impact was evaluated using Cohen’s dz effect size. The results showed statistically significant improvements in cognitive scores across all cohorts (p < 0.001), with mean score increases ranging from 16.10 to 23.35 points. Effect size analysis revealed large to very large effects, with an overall Cohen’s dz of 1.19, indicating substantial and practically meaningful cognitive gains. In conclusion, IPC training was effective in improving cognitive competence among FKTP healthcare workers. These results reinforce the value of well-structured training programs as an essential component of efforts to strengthen infection prevention capacity in primary healthcare settings.

Electronic Medical Records (EMR) are crucial for the quality of healthcare services, but compliance remains a challenge. This study analyzed factors influencing compliance among healthcare workers at a type B private hospital in North Jakarta using a quantitative cross-sectional design with 58 respondents through total sampling. Data were obtained through questionnaires related to individual factors (age, length of service, knowledge), psychological factors (attitude, motivation), and organizational factors (leadership, work design, rewards). The results showed that 72.4% of respondents were compliant. The chi-square test revealed a significant relationship between compliance and age (p=0.042), length of service (p=0.000), knowledge (p=0.001), attitude (p=0.017), motivation (p=0.002), leadership (p=0.046), and rewards (p=0.010), while work design was not significant (p>0.05). Multivariate analysis found age, length of service, knowledge, and leadership as the dominant factors. Healthcare workers with younger age, shorter tenure, good knowledge, positive attitudes, high motivation, good leadership, supportive work designs, and adequate reward systems tend to have higher compliance rates. Improving compliance in completing EMRs depends not only on individual factors but also requires organizational support through effective leadership and management systems. Recommended interventions include improving digital literacy, regular training, strengthening a work culture that emphasizes the importance of medical documentation, and implementing peer learning strategies among healthcare workers to accelerate adaptation and share best practices in completing EMRs.

This study examines the application of systems thinking in modern healthcare organizations, with a particular focus on Peter Senge’s Fifth Discipline. Using a systematic literature review of 20 selected articles, the study investigates the potential and challenges of applying systems thinking across key areas such as patient safety, service integration, resource management, and innovation. Findings indicate that systems thinking provides a holistic framework to better understand and address the inherent complexity of healthcare systems. By fostering interconnections among organizational components, it enhances the capacity to improve performance, ensure patient-centered care, and support sustainable change. Nevertheless, implementation is hindered by obstacles such as resistance to change, lack of shared understanding, and difficulties in measuring systemic outcomes. To address these barriers, the study highlights strategies including leadership development, staff training, and the establishment of appropriate evaluation tools. These measures strengthen organizational readiness and support a culture of continuous learning. The study concludes that systems thinking offers valuable insights for healthcare organizations to adapt to evolving challenges, but its success requires long-term commitment, supportive leadership, and systematic implementation. Future research should further explore the integration of systems thinking with emerging digital health technologies and assess its long-term impact on health outcomes and organizational resilience.

Research on Artificial Intelligence (AI) in finance has been growing significantly alongside its increasing implementation in the financial sector. This development raises questions about the specific financial areas and AI technology applications that are most frequently explored as research topics within AI in finance. This study aims to address these questions by employing a systematic literature review (SLR) method, analyzing journal articles indexed in Scopus (Q1–Q4) and published between 2020 and 2024. A search conducted using Publish or Perish on the Scopus database identified 496 records, which were subsequently filtered to 94 articles using the PRISMA protocol. The selected articles were examined through bibliometric analysis using VOSviewer, followed by content analysis. The findings reveal that fintech and risk management are the most frequently discussed financial areas in AI in finance research. Moreover, machine learning emerges as the most commonly addressed AI technology application in this domain. Notably, the combination of machine learning and risk management stands out as the most prominent research topic.    

Patient safety encompasses a system of services designed to ensure that patients feel secure. This includes proper identification, learning from incidents, injury prevention due to errors, follow-up actions, risk analysis, patient incident assessment, and risk management related to reporting. Implementing solutions to minimize risks also involves preventing injuries caused by employee negligence in performing incorrect actions. The objective of this research is to determine the impact of nurses' knowledge, attitudes, and practices regarding patient safety on the risk of patient falls. The study design is quasi-experimental (Pre and Post Test Without Control). Data analysis utilized univariate and bivariate methods. In this study, 50 respondents participated in an intervention. The average pre-test and post-test scores were analyzed to evaluate the influence of nurses' knowledge, attitudes, and practices on patient safety concerning the incidence of fall risk. The results indicated that there was no significant influence of knowledge and application of patient safety on fall risk (p-Value 0.230 > 0.05). However, there was a significant influence of nurses' attitudes on fall risk events (p-Value 0.000 < 0.05).