This research aimed to explore the concerning characteristics of information literacy in the physical education faculty of higher education institutions in Yunnan Province. This study provides a systematic meta-analysis of 33 peer-reviewed papers from 2019 to 2023. It discusses that information literacy includes basic research skills, critical thinking, and problem-solving, which include their application in the learning process. The paper describes some approaches that can be used to implement information literacy into teaching and learning, including courses with learning objectives, learner-centered approaches, and institutional support. The study also explored technology and its relation to adopting competencies for the growing technologies’ evolution within the region’s education sector. In addition, the following factors could have enhanced the process: time constraints, differences in discipline, and variations in the usage of information technology. The results indicate the need for context-specific professional learning and policy intervention to facilitate the practice of physical education faculty in Yunnan. The information collected here serves as the framework for effective regional policies regarding education, curriculum, and teacher training, among other related aspects.

Universities play a crucial role in supporting sustainable development. In recent decades, indicator-based assessment tools have emerged to quantify universities’ efforts towards sustainability. The most widely known is the UI GreenMetric World University Rankings (UI-GWUR): In our paper, we examine the sustainability performance of the three greenest Hungarian universities. The University of Pécs, the University of Szeged and the University of Sopron were among the top 200 higher education institutions (HEIs) in the UI-GWUR in 2023, which proves that they have successfully integrated sustainable development into the components of their system. The aim of the paper is to identify the sustainability measures implemented by the three-top Hungarian HEIs. Their experiences shed light on how it is possible to move forward in the UI GWUR for a Hungarian higher education institution. In order to evaluate the sustainability efforts of the universities, the UI GWUR database was first examined. The websites and sustainability reports of the three universities were also analyzed to gain insight into their activities. Identifying the sustainability actions of the three institutions will help other universities to successfully plan and implement their sustainability initiatives. In the last part of our paper, we evaluate how the three Hungarian universities communicate sustainability through their websites. The results show that advancement in the UI Green Metric World University Rankings primarily requires conscious planning, which means a deeper understanding of the ranking methodology on the one hand, and a clear strategy creation and implementation on the other hand.

Public signs in scenic spots play the role of guidance, instruction and warning, and are of great significance to promote the development of scenic spots. Guang’an District has a strong historical and cultural heritage and the rapid development of tourism, but the English translation of public signs in the scenic spot has become increasingly prominent, mainly including nonstandard translation, spelling errors, logical confusion and grammatical errors. In order to promote the solution of such problems, this paper will analyze the current situation of English translation of public signs in Guang’an scenic spots, and put forward solutions to the problems of English translation of public signs through hiring professional translators, cultural difference training and regional cooperation.

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.