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探寻世界一流大学人工智能人才培养的奥秘——斯坦福大学人工智能人才培养模式的整体性分析 被引量:6

Exploring the Mystery of Cultivating AI Talents in World-class Universities——An Overall Analysis of the Training Mode of AI Talents in Stanford University
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摘要 随着人工智能发展国家战略的逐步落实和人工智能核心地位的日益凸显,人工智能人才面临严重的供需矛盾,相关高校正在积极采取措施来加大人工智能人才培养力度。然而,作为新兴领域,我国的人工智能人才培养尚处在摸索阶段,需要积极学习和借鉴世界一流大学的丰富经验。斯坦福大学在人工智能领域始终保持世界前列,而其人才培养模式具有独特的优势。为了更加有效地探寻斯坦福大学人工智能人才培养的秘密,本研究采用扎根理论研究方法,选择斯坦福人工智能课程培养方案文本进行三级编码分析,立足微观层面自下而上地总结出以培养理念为主线,培养过程为主体,素养要求与结果评价为保障的人才培养模式。具体表现为:坚持规范、诚信和效率至上的培养理念;遵循有厚度、有内涵、有张力的素养需求;强调融合、多元、人性化的培养过程;构建以综合性能力提升为目标的科学评价机制。 With the gradual implementation of the national strategy for the development of artificial intelligence(AI) and the increasingly prominent position of the core of AI, AI talents are facing a serious contradiction between supply and demand. Relevant colleges and universities are actively taking measures to increase the cultivation of AI talents. However, as a new field, the cultivation of AI talents in China is still in the exploratory stage, which requires active learning from the rich experience of world-class universities. Stanford University has always been at the forefront of the world in the field of AI, and its talent training model has unique advantages. In order to explore the secrets of the cultivation of AI talents at Stanford University more effectively, this study adopts the method of grounded theory research with the programs of Stanford AI courses. Through a bottom-up three-level coding analysis, the paper induces a micro-level talent training mode, which takes the cultivation idea as the main line, the cultivation process as the main body, and the quality requirements and outcome evaluation as the guarantee mechanism. Specifically, it includes: adhering to the cultivation idea of standardization, integrity and efficiency first, following the quality requirements with thickness, connotation and tension, emphasizing the cultivation process of integration, diversity and humanization, and constructing a scientific evaluation mechanism aiming at the improvement of comprehensive ability.
作者 黄蓓蓓 钱小龙 HUANG Bei-bei;QIAN Xiao-long(Institute of Future Education,Nantong University,Nantong,Jiangsu,226019;Institute of Education,Nanjing University,Nanjing,Jiangsu,210093)
出处 《清华大学教育研究》 CSSCI 北大核心 2022年第3期33-41,共9页 Tsinghua Journal of Education
基金 国家社科基金一般项目“全民终身学习视野下的国家在线教育体系发展研究”(20BSH053)。
关键词 人工智能 人才培养模式 斯坦福大学 artificial intelligence talents training model Stanford University
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