This study provides empirical data on the impact of generative AI in education, with special emphasis on sustainable development goals (SDGs). By conducting a thorough analysis of the relationship between generative AI technologies and educational outcomes, this research fills a critical gap in the literature. The insights offered are valuable for policymakers seeking to leverage new educational technologies to support sustainable development. Using Smart-PLS4, five hypotheses derived from the research questions were tested based on data collected from an E-Questionnaire distributed to academic faculty members and education managers. Of the 311 valid responses, the measurement model assessment confirmed the validity and reliability of the data, while the structural model assessment validated the hypotheses. The study’s findings reveal that New Approaches to Learning Outcome Assessment (NALOA) significantly contribute to achieving SDGs, with a path coefficient of 0.477 (p < 0.001). Similarly, the Use of Generative AI Technologies (UGAIT) has a notable positive impact on SDGs, with a value of 0.221 (p < 0.001). A Paradigm Shift in Education and Educational Process Organization (PSEPQ) also demonstrates a significant, though smaller, effect on SDGs with a coefficient of 0.142 (p = 0.008). However, the Opportunities and Risks of Generative AI in Education (ORGIE) study did not find statistically significant evidence of an impact on SDGs (p = 0.390). These findings highlight the potential opportunities and challenges of using generative AI technologies in education and underscore their key role in advancing sustainable development goals. The study also offers a strategic roadmap for educational institutions, particularly in Oman to harness AI technology in support of sustainable development objectives.

On the basis of the enlightenment of international engineering education accreditation for the reform and development of higher education in China, combined with the important measures of the national “double first-class” construction, new challenges have been proposed for innovative talent cultivation among engineering majors in the context of promoting national development. These challenges also promote the reform of science-oriented courses among engineering majors. As a core mandatory course for engineering majors, biochemistry plays a crucial role in the entire educational process at universities, serving as a bridge between basic and specialized courses. To address challenges such as limited course resources, insufficient development of students’ advanced thinking and innovation skills, and overly standardized assessment methods, the bioengineering major from Guilin University of Technology restructured the biochemistry course content. A blended teaching model termed “three integrations, three stages, one sharing”, was implemented. This effort has yielded significant results, providing a research foundation for constructing an innovative talent cultivation system that is oriented toward industry needs within modern industrial colleges. It also offers valuable insights into and reference points for the cultivation of engineering talents and curriculum reform in local universities.

The current with the rapid development of Internet and new media technology, the information openness and diversity makes ideological education is facing big challenge, in accordance with the "five a three-ring four law" teaching mode,the fundamental task of implementing ideological and political education, fostering values and cultivating talents is comprehensively carried out. We are advancing the resonance of the “three classrooms” and promoting the synchronous implementation of the “four transformations”, aiming to enhance the “five capacities” of students, according to the current construction of" big education courses "concept, change education thought and idea.

With the deep integration of artificial intelligence technology in education, the development of AI integration capabilities among pre-service teachers—as the core of future educational human resources—has become crucial for enhancing educational quality and driving digital transformation in education. Based on the AI-TPACK (Artificial Intelligence-Technological Pedagogical Content Knowledge) theoretical framework, this study employs questionnaire surveys and structural equation modeling to explore the structural characteristics, influencing factors, and formation mechanisms of AI-TPACK competencies among pre-service teachers in Chinese universities. Findings indicate that while pre-service teachers demonstrate moderately high overall AI-TPACK levels, their technical knowledge (AI-TK) and technological integration competencies (e.g., AI-TPK, AI-TCK) remain relatively weak. School technical support, technological attitudes, and technological competence significantly influence their AI-TPACK capabilities, with institutional level and teaching experience serving as important external moderating factors. Building on these findings, this paper proposes a systematic framework for developing pre-service teachers' AI integration capabilities from a human resource development perspective. This framework encompasses four dimensions: curriculum optimization, practice enhancement, resource support, and policy guidance. It aims to provide theoretical foundations and practical pathways for pre-service teacher training and teacher human resource development in higher education institutions.

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.