This paper presents a brief review of risk studies in Geography since the beginning of the 20th century, from approaches focused on physical-natural components or social aspects, to perspectives that incorporate a systemic approach seeking to understand and explain risk issues at a spatial level. The systemic approach considers principles of interaction between multiple variables and a dynamic organization of processes, as part of a new formulation of the scientific vision of the world. From this perspective, the Complex Systems Theory (CST) is presented as the appropriate conceptual-analytical framework for risk studies in Geography. Finally, the analysis and geographic information integration capabilities of Geographic Information Systems (GIS) based on spatial analysis are explained, which position it as a fundamental conceptual and methodological tool in risk analysis from a systemic approach.

This study evaluates the effectiveness of human resources (HR) practices on teaching and learning outcomes in primary education. The research was guided by four research questions and two research hypotheses. The study utilized a survey design via Google Forms for efficient data collection on human resources practices’ effectiveness in primary education. The questionnaire, validated by experts, garnered 60 responses within a month. Data analysis in Statistical Package for the Social Sciences (SPSS) included descriptive statistics and analysis of variance (ANOVA) techniques, adhering to ethical standards. The findings highlight the importance of HR practices that accommodate diversity, support inclusivity, and foster a sense of belonging for all students. Challenges in implementing inclusive HR practices are also identified, emphasizing the need for ongoing efforts to promote inclusivity and equity in primary education. The study concludes by advocating for the development and implementation of effective HR strategies to enhance teaching and learning outcomes in primary education.

With the gradual penetration of artificial intelligence technology into various fields of society, it has brought many deeper and broader impacts, gradually improving the status of artificial intelligence in talent cultivation and education to adapt to the current development of social intelligence technology. Therefore, as the core course of artificial intelligence education in universities, machine learning needs to deeply analyze and explore the main factors that affect its development, in order to better mobilize students' learning enthusiasm and teachers' educational innovation, enhance the teaching and learning effectiveness of the course, and maximize the exploration of the educational achievements of artificial intelligence.

As an important part of modern higher education, this topic mainly studies the construction of innovative teachers' team in local applied colleges and universities. After analyzing the problem, we found that there are many problems in the construction of innovative teachers in local applied colleges and universities, such as the lack of effective cultivation mechanism and the lack of corresponding incentives. Therefore, this paper aims to put forward some suggestions on how to establish innovative teachers' team, in order to provide a reference basis for the development of innovative teachers' team in local applied colleges and universities.