RISS 검색 - 국내학술지논문 상세보기

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다국어 초록 (Multilingual Abstract)

With the advent of the digital transformation era, radical changes are taking place throughout the economy and society, including science and technology, jobs, and the global value chain. Therefore, in order to respond to these changes, timely learning and systematic training of scientific and technological manpower, which has an important impact on national productivity, is urgently needed. In this study, we proposed a learning platform based on artificial intelligence technology that induces optimized learning in consideration of the characteristics of scientists and engineers. An important function of the platform is to extract key keywords using the natural language processing method and to recommend learners with high similarity through learning through machine learning. In the new platform, the scope of learning was expanded from formal learning to informal learning, and internal and external integrated data including the entire research activities of scientists and engineers were collected to increase the connectivity between science and technology personnel. Scientists and engineers network with learners who have similar interests and research topics based on the learning platform, share the latest scientific and technological information, and realize true self-directed learning. Education data, which is science and technology accumulated through the operation of the learning platform, is high-value data that has not been systematically collected before. It can be used as a scientific basis for learners, instructors, educators, and policy makers to formulate an efficient and optimized human resource development plan. In addition, the platform will contribute to creating a learning ecosystem that enables continuous self-development as an online system where knowledge and experiences can be shared and developed through exchanges with fellow scientists and engineers in the research field.