随着生成式人工智能技术(GAI)的快速发展,其在教育领域的应用越来越广泛,相关学者在该领域展开了大量研究。然而,此前一直缺乏国内生成式人工智能应用于教育领域研究内容以及成果的归纳总结,这可能阻碍生成式人工智能技术在教育领域的...随着生成式人工智能技术(GAI)的快速发展,其在教育领域的应用越来越广泛,相关学者在该领域展开了大量研究。然而,此前一直缺乏国内生成式人工智能应用于教育领域研究内容以及成果的归纳总结,这可能阻碍生成式人工智能技术在教育领域的进一步发展。因此,本文以中国知网(CNKI)数据库中2019年1月至2024年5月的期刊文献为研究样本,运用CiteSpace文献计量分析软件,从发文量、作者、研究机构和关键词四个方面进行可视化图谱分析,对国内生成式人工智能应用于教育领域的研究现状,研究热点和研究趋势进行总结。结果表明:(1) 发文量在2023年迎来拐点,发文量呈现增长趋势并持续至今;(2) 尚未形成核心作者群体,研究机构以高校教育学院为主,存在国际合作和交流,呈现多学科交叉;(3) 研究热点主要集中在人工智能、复合脑、教育、新文科和人才培养等方面,研究方向呈现多元化和精细化的趋势。该领域研究未来应致力于深化生成式人工智能与教育实践的整合,探索生成式人工智能技术对教育领域冲击的应对策略,并加强技术伦理的研究。As the rapid advancement of Generative Artificial Intelligence (GAI) technology, its application in the field of education has become increasingly widespread, spurring extensive research by scholars in this domain. However, a significant gap exists in a comprehensive summary of domestic research on the application of GAI in education, potentially hindering further development of GAI technology in educational settings. Therefore, this study has utilized journal articles from the China National Knowledge Infrastructure (CNKI) database, ranging from January 2019 to May 2024. Using CiteSpace bibliometric analysis software, a visual mapping analysis has been conducted, summarizing the current state, hotspots, and trends of domestic research on the application of GAI in education. The results indicate: (1) A turning point in publication volume occurred in 2023, with an upward trend that continues to the present;(2) No core group of authors has emerged, with research led predominantly by university education faculties, instances of international cooperation and exchange are observed, and a multidisciplinary intersection is noted;(3) Research hotspots are concentrated in areas such as artificial intelligence, composite brain, education, new liberal arts, and talent cultivation, with research directions exhibiting tendencies towards diversification and refinement. Future research in this field ought to concentrate on deepening the integration of generative AI with educational practice, exploring strategies to address the impact of GAI technology on the field of education, and enhancing the study of technological ethics.展开更多
文摘随着生成式人工智能技术(GAI)的快速发展,其在教育领域的应用越来越广泛,相关学者在该领域展开了大量研究。然而,此前一直缺乏国内生成式人工智能应用于教育领域研究内容以及成果的归纳总结,这可能阻碍生成式人工智能技术在教育领域的进一步发展。因此,本文以中国知网(CNKI)数据库中2019年1月至2024年5月的期刊文献为研究样本,运用CiteSpace文献计量分析软件,从发文量、作者、研究机构和关键词四个方面进行可视化图谱分析,对国内生成式人工智能应用于教育领域的研究现状,研究热点和研究趋势进行总结。结果表明:(1) 发文量在2023年迎来拐点,发文量呈现增长趋势并持续至今;(2) 尚未形成核心作者群体,研究机构以高校教育学院为主,存在国际合作和交流,呈现多学科交叉;(3) 研究热点主要集中在人工智能、复合脑、教育、新文科和人才培养等方面,研究方向呈现多元化和精细化的趋势。该领域研究未来应致力于深化生成式人工智能与教育实践的整合,探索生成式人工智能技术对教育领域冲击的应对策略,并加强技术伦理的研究。As the rapid advancement of Generative Artificial Intelligence (GAI) technology, its application in the field of education has become increasingly widespread, spurring extensive research by scholars in this domain. However, a significant gap exists in a comprehensive summary of domestic research on the application of GAI in education, potentially hindering further development of GAI technology in educational settings. Therefore, this study has utilized journal articles from the China National Knowledge Infrastructure (CNKI) database, ranging from January 2019 to May 2024. Using CiteSpace bibliometric analysis software, a visual mapping analysis has been conducted, summarizing the current state, hotspots, and trends of domestic research on the application of GAI in education. The results indicate: (1) A turning point in publication volume occurred in 2023, with an upward trend that continues to the present;(2) No core group of authors has emerged, with research led predominantly by university education faculties, instances of international cooperation and exchange are observed, and a multidisciplinary intersection is noted;(3) Research hotspots are concentrated in areas such as artificial intelligence, composite brain, education, new liberal arts, and talent cultivation, with research directions exhibiting tendencies towards diversification and refinement. Future research in this field ought to concentrate on deepening the integration of generative AI with educational practice, exploring strategies to address the impact of GAI technology on the field of education, and enhancing the study of technological ethics.
