Artificial Intelligence (AI) in education has both positive and negative impacts, particularly in term of increasing plagiarism. This research analyzes Indonesia’s plagiarism regulations and offers solutions. It uses doctrinal methods with legislative, case, and comparative studies, revealing that plagiarism is regulated but not specifically for AI involvement. The results show that plagiarism in scientific work has actually been regulated through several regulations. On the other hand, there is no regulation governing the involvement of AI in the process of preparing scientific articles. Comparative studies show that the US, Singapore, and the EU have advanced regulations for AI in education. The US has copyright laws for AI works and state regulations, Singapore’s Ministry of Education has guidelines for AI integration and ethics, and the EU has the Artificial Intelligence Act. To tackle AI-related plagiarism in Indonesia, the study suggests enacting AI-specific laws and revising existing ones. Ministerial and Rector statutes should address technical aspects of AI use and plagiarism checks. The Ministry should issue guidelines for universities to develop Standard Procedures for Writing and Checking Scientific Work, using reliable AI-checking software. These measures aim to prevent plagiarism in Indonesia’s educational sector.

This study aimed to analyze government policies in education during the Covid-19 pandemic and how teachers exercised discretion in dealing with limitations in policy implementation. This research work used the desk review method to obtain data on government policies in the field of education during the Covid-19 pandemic. In addition, interviews were conducted to determine the discretion taken in implementing the learning-from-home policy. There were three learning models during the pandemic: face-to-face learning in turns (shifts), online learning, and home visits. Online learning policies did not work well at the pandemic’s beginning due to limited infrastructure and human resources. To overcome various limitations, the government provided internet quota assistance and curriculum adjustments and improved online learning infrastructure. The discretion taken by the teachers in implementing the learning-from-home policy was very dependent on the student’s condition and the availability of the internet network. The practical implication of this research is that street-level bureaucrats need to pay attention to discretionary standards when deciding to provide satisfaction to the people they serve.

Corporate finance courses are increasingly adopting data-driven teaching methods. Modern corporate finance courses are focusing more on students' career development. Through simulation practice and career planning guidance, students are better prepared to face challenges in the workplace after graduation. Students need to learn how to utilize data analysis tools and techniques to extract useful information from large datasets and make more accurate decisions. Data-driven teaching is a significant innovation in current curriculum reforms. In recent years, with the development of technology and the emergence of financial innovation, corporate finance courses have been undergoing continuous changes and innovations. These courses have started to emphasize emerging areas such as digital finance, blockchain technology, and sustainable development. Taking the example of corporate finance, this paper integrates the demands of skill development in the era of digital finance, focusing on aspects like teaching methods, reform methodologies, practical experiments, feedback mechanisms, and data analysis.

This paper explores the distribution of educational resources from the perspective of public service equalization in China, with a particular focus on government responsibility and fiscal input. Initially, the paper reviews the theoretical foundations and empirical studies concerning the distribution of educational resources, analyzing the role of government in educational equity and the impact of fiscal expenditure. By employing quantitative analysis methods, this study utilizes data on provincial education expenditures over several years to examine the relationship between government fiscal input and the equalization of educational resources. Empirical results indicate that increasing educational fiscal input and optimizing the allocation mechanism significantly enhance the level of equalization in educational resources. Furthermore, through case analyses of several local governments, effective policy recommendations are proposed to promote the fair distribution and optimization of educational resources. Lastly, the paper discusses potential obstacles in policy implementation and suggests corresponding strategies.