Given the heavy workload faced by teachers, automatic speaking scoring systems provide essential support. This study aims to consolidate technological configurations of automatic scoring systems for spontaneous L2 English, drawing from literature published between 2014 and 2024. The focus will be on the architecture of the automatic speech recognition model and the scoring model, as well as on features used to evaluate phonological competence, linguistic proficiency, and task completion. By synthesizing these elements, the study seeks to identify potential research areas, as well as provide a foundation for future research and practical applications in software engineering.

With society’s continuous development and progress, artificial intelligence (AI) technology is increasingly utilized in higher education, garnering increased attention. The current application of AI in higher education impacts teachers’ instructional methods and students’ learning processes. While acknowledging that AI advancements offers numerous advantages and contribute significantly to societal progress, excessive reliance on AI within education may give rise to various issues, students’ over-dependence on AI can have particularly severe consequences. Although many scholars have recently conducted research on artificial intelligence, there is insufficient analysis of the positive and negative effects on higher education. In this paper, researchers examine the existing literature on AI’s impact on higher education to explore the opportunities and challenges presented by this super technology for teaching and learning in higher educational institutions. To address our research questions, we conducted literature searches using two major databases—Scopus and Web of Science—and we selected articles using the PRISMA method. Findings indicate that AI plays a significant role in enhancing student efficiency in academic tasks and homework; However, when considering this issue from an ethical standpoint, it becomes apparent that excessive use of AI hinders the development of learners’ knowledge systems while also impairing their cognitive abilities due to an over-reliance on artificial technology. Therefore, our research provides essential guidance for stakeholders on the wise use of artificial intelligence technology.

This research looks into the differences in technological practices across Gen-X, Gen-Y, and Gen-Z employees in the workplace, with an emphasis on motivation, communication, collaboration, and productivity gaps. The study uses a systematic literature review to identify factors that contribute to these variations, taking into account each generation’s distinct experiences, communication methods, working attitudes, and cultural backgrounds. Bridging generational gaps, providing ongoing training, and incorporating cross-generational and technology-enhanced practices are all required in today’s workplace. This study compares the dominating workplace generations, Gen-X and Gen-Y, with the emerging Gen-Z. A review of the literature from 2010 to 2023, which was narrowed down from 1307 to 20 significant studies, emphasizes the importance of organizational management adapting to generational changes in order to increase productivity and maintain a healthy workplace. The study emphasizes the need of creating effective solutions for handling generational variations in workplace.

This research aims to comprehensively investigate contemporary practices and factors influencing global tax avoidance and evasion through a systematic literature review (SLR) and bibliometric analysis. This study methodology uses SLR with bibliometric analysis to map this research trend. Relevant literature was collected and systematically analyzed to identify key themes, methodologies, and findings regarding tax avoidance and evasion. The study’s findings identify various tax avoidance practices, including profit shifting, transfer pricing strategies, and the use of tax havens by multinational corporations. The report also highlights various forms of tax evasion, such as money laundering and illicit financial flows. Key factors influencing tax behavior are ethical considerations, government policies, economic factors, and corporate governance practices. This analysis highlights the complex interactions between these factors in shaping tax practices globally. The study’s conclusions show that effective tax governance requires a multifaceted approach integrating ethical standards, a strong regulatory framework, and international cooperation. Ethical corporate behavior and transparent reporting are essential in promoting tax compliance and fairness. This study emphasizes the critical role of policymakers in designing and enforcing tax policies that discourage aggressive tax planning while encouraging economic growth and equality. Recommendations from these findings are to increase transparency in multinational operations, strengthen international tax cooperation, and improve global tax administration capabilities. In addition, increasing taxpayer education and awareness can foster a culture of compliance and ethical tax practices. Future research should prioritize longitudinal studies and case-specific analyses to deepen understanding and develop targeted interventions to address tax avoidance and evasion challenges.