Plants serve as the basis for ecosystems and provide a wide range of essential ecological, environmental, and economic benefits. However, forest plants and other forest systems are constantly threatened by degradation and extinction, mainly due to misuse and exhaustion. Therefore, sustainable forest management (SFM) is paramount, especially in the wake of global climate change and other challenges. SFM ensures the continued provision of plants and forests to both the present and future generations. In practice, SFM faces challenges in balancing the use and conservation of forests. This review discusses the transformative potential of artificial intelligence (AI), machine learning, and deep learning (DL) technologies in sustainable forest management. It summarizes current research and technological improvements implemented in sustainable forest management using AI, discussing their applications, such as predictive analytics and modeling techniques that enable accurate forecasting of forest dynamics in carbon sequestration, species distribution, and ecosystem conditions. Additionally, it explores how AI-powered decision support systems facilitate forest adaptive management strategies by integrating real-time data in the form of images or videos. The review manuscript also highlights limitations incurred by AI, ML, and DL in combating challenges in sustainable forest management, providing acceptable solutions to these problems. It concludes by providing future perspectives and the immense potential of AI, ML, and DL in modernizing SFM. Nonetheless, a great deal of research has already shed much light on this topic, this review bridges the knowledge gap.

Camptotheca acuminata Decne. is an endemic and valuable tree species in China that is renowned for its medicinal and economic value due to secondary metabolites like camptothecin, a potent anti-cancer compound. With wild resources dwindling, it is a key protected species. Predicting and analyzing its suitable habitats under different future environmental scenarios is essential for conservation, introduction, development, and planting strategies. This study used 1008 distribution points and 32 environmental factors, applying the MaxEnt v3.4.4 model and ArcGIS v10.7 software to predict C. acuminata’s potential distribution under four greenhouse gas emission scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) for the present, 2050, and 2070. This study identifies the key environmental factors influencing its distribution and analyzes habitat trends under various ecological scenarios. The dominant environmental factors are Bio6 (contribution 23%; importance 59.8%), human activity factor (contribution 18.6%; importance 15.7%), Slope2 (contribution 1%; importance 7%), Slope3 (contribution 5.1%; importance 3.4%), elevation (contribution 0.9%; importance 1.7%), and Bio14 (contribution 41.2%; importance 1%). The total potential suitable habitat area for C. acuminata is 1.5796 × 10⁴ km². Except under RCP8.5, where the habitat area continuously increases, the habitat area shows a trend of first increasing and then decreasing. When human activity is considered, the total potential suitable habitat area is 1.8495 × 10⁴ km², with a consistent decrease under all scenarios except RCP8.5. Centroid migration analysis shows that, driven by global warming, the suitable habitats for C. acuminata are shifting toward higher latitudes. This study provides theoretical support for the conservation, resource management, and germplasm protection of C. acuminata under future ecological and environmental changes.

The main responsibility of universities is to cultivate high-quality students. The quality of student cultivation can be enhanced by improving university teachers’ pedagogical competence, and thus enhancing students’ learning experiences and satisfaction. The purpose of this study was to examine the impact of university teachers’ pedagogical competence on students’ learning experience and satisfaction, as well as the mediating effect of university students’ perception of learning experience. Structural equation modeling was employed to examine the relationships between pedagogical competency of university teachers, students’ learning experiences and classroom satisfaction, based on 569 questionnaires. The results indicate that teachers’ professional knowledge ( β = 0.325, p < 0.001) and teaching skills (β = 0.155, p < 0.001) have a direct positive effect on students’ classroom satisfaction. Both factors indirectly influence satisfaction through learning experiences, with professional knowledge showing a standardized path coefficient of 0.615 ( p < 0.001) on learning experiences, and teaching skills showing a coefficient of 0.335 ( p < 0.001). Learning experiences have a direct effect on satisfaction with a coefficient of 0.536 (p < 0.001). Bootstrap testing confirms that the mediation effect of learning experiences is significant, with professional knowledge accounting for 50.4% of the mediation effect (total effect = 0.655, indirect effect = 0.330), and teaching skills accounting for 53.9% (total effect = 0.334, indirect effect = 0.179). The study suggests that university teachers can enhance classroom satisfaction indirectly by optimizing the depth of knowledge, teaching methods, and classroom interactions, thereby improving students’ learning experiences. This study recommends that universities strengthen training in teachers’ professional knowledge and teaching skills, and establish dynamic feedbacks mechanism for teaching outcomes. This study also offers insights for universities to enhance the various dimensions of teachers’ pedagogical competence and optimize administrative and management strategies through the learning experience and satisfaction of university students.

Forests play a central role in biodiversity conservation, climate mitigation, and sustainable development, requiring highly skilled professionals trained through effective graduate education. Grounded in Comparative Education Theory, this study conducts a comparative analysis of ten flagship forestry graduate programs across developed and developing countries, examining how institutional characteristics influence curriculum design, professional preparation, and student outcomes. Using a structured document review of program handbooks, curriculum guides, faculty profiles, and sectoral reports from 2015 to 2025, the study analyzes six dimensions of program design: curriculum structure, faculty and research capacity, industry engagement, infrastructure resources, funding models, and international partnerships. Findings reveal substantial variation across institutions. Programs in developed countries often benefit from stronger research environments, diversified funding, and well-established collaborations with industry and government agencies. Several developing-country programs face constraints related to funding, infrastructure, and technological capacity, yet demonstrate strengths in traditional ecological knowledge and community-based training. Across cases, graduate employment outcomes are shaped by sectoral dynamics, program specialization, and opportunities for experiential learning. The study highlights institutional and not national patterns and emphasizes that broader generalizations require larger, more representative datasets. Insights may inform program enhancement, capacity development, and future research on global forestry education.