人工智能背景下技术知识学习效能提升研究OA

Artifical intelligence reshapes the learning paradigm of technical knowledge by simulating human cognitive model,especially in knowledge production,integration,acquisition and migration.However,this process is also accompanied by multiple hidden worries:cognitive outsourcing may weaken learners'independent judgment ability,and it becomes more difficult to understand and transmit"tacit knowledge".The self-awareness of learners is at risk of being weakened,and the evaluation system is prone to"adaptation crisis".In order to cope with the above challenges,a smart learning space featuring human-computer synergy should be constructed to promote the deep integration of cognition and experience;adaptive professional clusters and curriculum systems should be developed to facilitate the synchronous advancement of knowledge acquisition and competency development;knowledge tracking functions should be leveraged,with modular projects serving as the guide to steer learning toward deep learning;and big data and metadata should be comprehensively utilized to build a smart growth model,thereby enhancing technical comprehension and self-efficacy,and realizing the balanced development and humanistic return of technical knowledge learning in the intelligent era.