从观点结构化组织到人机协同创生:线上知识建构环境演进脉络与发展趋势  

From Opinion Structuring to Human-Machine Collaborative Creation:Evolution and Development Trend of Online Knowledge Building Environment

作　　者：董延庆 陈菲 马志强 DONG Yanqing;CHEN Fei;MA Zhiqiang(Jiangnan University,Wuxi 214122,China)

机构地区：[1]江南大学,江苏无锡214122

出　　处：《开放学习研究》2025年第1期14-23,共10页Journal of Open Learning

基　　金：2024年度江苏省研究生科研与实践创新计划项目“基于生成式人工智能的协同知识建构环境设计研究”(项目编号:KYCX24_2477);江南大学2023年本科教育教学改革研究项目“临床内科学建立跨院联合虚拟教研室的探索”(项目编号:JG2302078)的研究成果。

摘　　要：线上知识建构环境通过动态呈现观点关系、支持共同体知识共享、重构知识建构活动模式等方式来推动知识建构共同体观点与知识的深度发展,对于发展学生批判性思维、问题解决等高阶思维能力具有重要价值。研究分析了近10年国内外知识建构环境的相关研究文献,梳理出了线上知识建构环境发展趋势。研究发现,线上知识建构环境经历了以下三个发展阶段:一是群体观点结构化与可视化支持,二是多社区空间并行的观点线程发展,三是人机协同支持的深度观点创生。未来研究需要从数据驱动个性化资源自适应推荐、跨平台社交学习网络分布式集成、生成式AI支持观点多模态创作等角度来进一步提升线上知识建构环境的可行性与应用价值。Online knowledge building environments promote the in-depth development of perspectives and knowledge within knowledge building communities by dynamically visualizing conceptual relationships,supporting collective knowledge sharing,and reconstructing knowledge building activity patterns,which hold significant value for cultivating students’higher-order thinking skills such as critical thinking and problem solving.This article analyzed domestic and international literature on knowledge building environments over the past decade,and systematically mapping the evolutionary trajectory of online knowledge-building environments.It is found that online knowledge building environments have undergone three developmental phases:1)structured and visual representation of collective ideas,2)parallel development of idea threads across multi-community spaces,and 3)human-machine collaborative support for deep idea generation.Future research should enhance the feasibility and application value of online knowledge building environments through three key approaches:data-driven adaptive recommendation of personalized resources,distributed integration of cross-platform social learning networks,and generative AI-supported multimodal idea creation.

关 键 词：知识建构 学习环境设计 知识进化 生成式人工智能 

分 类 号：G434[文化科学—教育学]