预见学习的未来:人工智能学习准备度的框架研制及实践进路

Foresee the Future of Learning: Framework Development and Practical Approach of Artificial Intelligence Learning Readiness

为人工智能的未来做好准备是推进教育生态高质量、可持续发展的关键抓手。然而,学习者应提前做出哪些准备?学界尚缺乏科学完善的标准和依据。鉴于此,该研究采用文献元分析、德尔菲和层次分析法,遵照“确定指标体系→计算指标权重”的研制流程,确立了人工智能学习准备度的指标结构及各指标权重。研究结果显示,4个一级指标权重依次为:智能能力准备(0.5184)>智能知识准备(0.1850)>技术思维准备(0.1603)>智能条件与支持(0.1363)。10个二级指标权重依次为:非认知能力(0.3911)>认知能力(0.1273);软知识(0.1460)>硬知识(0.0390);人机协同思维(0.0679)>主动转变思维(0.0553)>计算思维(0.0213)>设计思维(0.0158);智能化投入(0.1019)>社会性支持(0.0345)。36个三级指标中,排在前三位的是:智能心智知识(0.1228)>智能志趣(0.1134)>技术焦虑管理(0.0815)。基于度量结果,为增强人工智能学习的充分就绪性,提出未来优化方向:管理技术焦虑,提升学习者的非认知能力;重视情意心智,增长学习者的智能软知识;适应人机协同,转变学习者的智能化思维;优化制度保障,调动学习者的主动性心态。

Preparing for the future of artificial intelligence is the key to promoting the high-quality and sustainable development of the education ecology. However, what preparations should learners make in advance? The academic circle still lacks scientific and perfect standards and basis. In view of this, the research adopted literature meta-analysis, Delphi, and analytic hierarchy process, and followed the development process of “determine index system → calculate index weight” to establish the index structure and the weight of each index of artificial intelligence learning readiness. The results showed that the weights of the four first-level indicators were as follows: intelligent ability readiness(0.5184)>intelligent knowledge readiness(0.1850)>technical thinking readiness(0.1603)>intelligent conditions and support(0.1363). The weights of the 10 second-level indicators were as follows: non-cognitive ability(0.3911)>cognitive ability(0.1273);soft knowledge(0.1460)>hard knowledge(0.0390);human-machine collaborative thinking(0.0679)>active transformation thinking(0.0553)>computational thinking(0.0213)>design thinking(0.0158);intelligent investment(0.1019)>social support(0.0345). Among the 36 three-level indicators, the top three were: intelligent mental knowledge(0.1228)>intelligent interest(0.1134)>technical anxiety management(0.0815).Based on the measurement results, in order to enhance the full readiness of artificial intelligence learning, the future optimization direction is proposed: manage technical anxiety, improve learners’ non-cognitive ability;attach importance to affection and mind, increase learners’ intelligent soft knowledge;adapt to human-machine collaboration, transform learners’ intelligent thinking;optimize system guarantee, and mobilize learners’ initiative mentality.