This study employs a transfer matrix, dynamic degree, stability index, and the PLUS model to analyze the spatiotemporal changes in forest land and their driving factors in Yibin City from 2000 to 2022. The results reveal the following: (1) The land use in Yibin City is predominantly characterized by cultivated land and forest land (accounting for over 95% of the total area). The area of cultivated land initially increased and then decreased, while forest land continued to decline and construction land expanded significantly. The rate of forest land loss has slowed (with the dynamic degree decreasing from −0.62% to −0.04%), and ecosystem stability has improved (the F-value increased from 2.27 to 2.9). The conversion of cultivated land to forest land is the primary driver of forest recovery, whereas the conversion of forest land to cultivated land is the main cause of reduction; (2) cultivated land is concentrated in the central and northeastern regions, while forest land is distributed in the western and southern mountainous areas. Construction land is predominantly located in urban areas and along transportation routes. Areas of forest land reduction are mainly found in the central and southern regions with rapid economic development, while areas of forest land increase are concentrated in high-altitude zones or key ecological protection areas. Stable forest land is distributed in the western and southern ecological conservation zones; (3) changes in forest land are primarily influenced by annual precipitation, elevation, and distance to rivers. Road accessibility and GDP have significant impacts, while slope, annual average temperature, and population density exert moderate influences. Distance to railways, aspect, and soil type have relatively minor effects. The findings of this study provide a scientific basis for the sustainable management of forest resources and ecological conservation in Yibin City.

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.

The present study, developed under a quantitative approach, explanatory scope and causal correlational design, aims to determine the influence of invisible learning on the research competence of high school students in two private schools in the city of Lima, Peru, whose educational models seek to develop autonomous learning and research through discovery learning and experimentation. Two questionnaires were applied to 120 students of the VII cycle of basic education, one to measure the perception regarding invisible learning with 20 items and the other to measure investigative competencies with 21 items; both instruments underwent the corresponding validity and reliability tests before their application. Among the main findings, descriptive results were obtained at a medium level for both variables, the correlations found were significant and moderate, and as for influence, the coefficient of determination R² yielded a value of 0.13, suggesting that 13% of investigative competence is predicted by invisible learning. These results show that autonomy, the use of digital technologies, metacognition and other aspects that are part of invisible learning prepare students to solve problems of varying complexity, allowing them to face the challenges of contemporary knowledge in an innovative and effective manner.

The study’s goal was to investigate the impact of e-learning determinants on student satisfaction and intention to use e-learning tools. The dependent and independent variables in this study were based on the technological acceptance model. The study examines three determinants, including usefulness, ease of use, and facilitating conditions, as independent variables, while student satisfaction and intention to use were used as dependent variables. Additionally, this study is unique by adding student satisfaction as a dependent variable and a mediator to examine the relationship between e-learning determinants and intention to use. A questionnaire was prepared and distributed to 324 undergraduate students from Jordan’s private universities on the basis of a convenience sample. The proposed hypotheses were investigated using the quantitative techniques of regression in SPSS and SEM in AMOS. The findings of this study revealed that student satisfaction and intention to use e-learning were positively impacted by e-learning determinants. It found that intention to use was positively impacted by student satisfaction. Furthermore, e-learning intention to use was found to be positively impacted by e-learning determinants via student satisfaction. Universities and other educational institutions are advised to identify the appropriate e-learning determinants that satisfy students’ demands and motivate them to use e-learning tools in light of the study’s findings. Private universities can accomplish their goals, stay ahead of the competition, and obtain a competitive advantage by properly understanding e-learning determinants, student satisfaction, and the application of successful e-learning solutions.