{"title":"高等教育在人工智能辅助工程教学中的数字化转型框架","authors":"Yin Zhang, Menglong Zhang, Liming Wu, Jin Li","doi":"10.1007/s11191-024-00575-3","DOIUrl":null,"url":null,"abstract":"<div><p>Rapid advancements in the information age have prompted significant digitalization in global higher education, with Chinese higher education particularly adapting to the influence of artificial intelligence. This study focuses on the digital transformation in China’s higher education, specifically within AI-assisted engineering education. It examines the digitalization of classrooms, expansion of teaching elements, and redesign of educational dynamics, while highlighting digital innovations in teaching methodologies and the integration of AI systems. Using the Engineering Cost Estimation as a case study, the paper showcases the practical application of AI in engineering education in China. The findings reveal the interplay between external societal, economic, political, and technological factors and internal academic aspects like curriculum quality. The study addresses the digital divide, advocates for equitable technology access, and emphasizes digital literacy as crucial in the twenty-first century. It predicts significant structural changes in universities, proposing borderless educational environments and flexible, interdisciplinary approaches, alongside a blockchain-based credit system.</p></div>","PeriodicalId":771,"journal":{"name":"Science & Education","volume":"34 2","pages":"933 - 954"},"PeriodicalIF":3.1000,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11191-024-00575-3.pdf","citationCount":"0","resultStr":"{\"title\":\"Digital Transition Framework for Higher Education in AI-Assisted Engineering Teaching\",\"authors\":\"Yin Zhang, Menglong Zhang, Liming Wu, Jin Li\",\"doi\":\"10.1007/s11191-024-00575-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Rapid advancements in the information age have prompted significant digitalization in global higher education, with Chinese higher education particularly adapting to the influence of artificial intelligence. This study focuses on the digital transformation in China’s higher education, specifically within AI-assisted engineering education. It examines the digitalization of classrooms, expansion of teaching elements, and redesign of educational dynamics, while highlighting digital innovations in teaching methodologies and the integration of AI systems. Using the Engineering Cost Estimation as a case study, the paper showcases the practical application of AI in engineering education in China. The findings reveal the interplay between external societal, economic, political, and technological factors and internal academic aspects like curriculum quality. The study addresses the digital divide, advocates for equitable technology access, and emphasizes digital literacy as crucial in the twenty-first century. It predicts significant structural changes in universities, proposing borderless educational environments and flexible, interdisciplinary approaches, alongside a blockchain-based credit system.</p></div>\",\"PeriodicalId\":771,\"journal\":{\"name\":\"Science & Education\",\"volume\":\"34 2\",\"pages\":\"933 - 954\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-10-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s11191-024-00575-3.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Science & Education\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11191-024-00575-3\",\"RegionNum\":1,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science & Education","FirstCategoryId":"95","ListUrlMain":"https://link.springer.com/article/10.1007/s11191-024-00575-3","RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
Digital Transition Framework for Higher Education in AI-Assisted Engineering Teaching
Rapid advancements in the information age have prompted significant digitalization in global higher education, with Chinese higher education particularly adapting to the influence of artificial intelligence. This study focuses on the digital transformation in China’s higher education, specifically within AI-assisted engineering education. It examines the digitalization of classrooms, expansion of teaching elements, and redesign of educational dynamics, while highlighting digital innovations in teaching methodologies and the integration of AI systems. Using the Engineering Cost Estimation as a case study, the paper showcases the practical application of AI in engineering education in China. The findings reveal the interplay between external societal, economic, political, and technological factors and internal academic aspects like curriculum quality. The study addresses the digital divide, advocates for equitable technology access, and emphasizes digital literacy as crucial in the twenty-first century. It predicts significant structural changes in universities, proposing borderless educational environments and flexible, interdisciplinary approaches, alongside a blockchain-based credit system.
期刊介绍:
Science Education publishes original articles on the latest issues and trends occurring internationally in science curriculum, instruction, learning, policy and preparation of science teachers with the aim to advance our knowledge of science education theory and practice. In addition to original articles, the journal features the following special sections: -Learning : consisting of theoretical and empirical research studies on learning of science. We invite manuscripts that investigate learning and its change and growth from various lenses, including psychological, social, cognitive, sociohistorical, and affective. Studies examining the relationship of learning to teaching, the science knowledge and practices, the learners themselves, and the contexts (social, political, physical, ideological, institutional, epistemological, and cultural) are similarly welcome. -Issues and Trends : consisting primarily of analytical, interpretive, or persuasive essays on current educational, social, or philosophical issues and trends relevant to the teaching of science. This special section particularly seeks to promote informed dialogues about current issues in science education, and carefully reasoned papers representing disparate viewpoints are welcomed. Manuscripts submitted for this section may be in the form of a position paper, a polemical piece, or a creative commentary. -Science Learning in Everyday Life : consisting of analytical, interpretative, or philosophical papers regarding learning science outside of the formal classroom. Papers should investigate experiences in settings such as community, home, the Internet, after school settings, museums, and other opportunities that develop science interest, knowledge or practices across the life span. Attention to issues and factors relating to equity in science learning are especially encouraged.. -Science Teacher Education [...]