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    智慧学习环境中的人机协同设计

    Design of Human-Machine Collaboration in Smart Learning Environment

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【作      者】:

武法提, 杨重阳, 李 坦


【关 键 词 】:

学习环境; 人机协同; 智慧学习环境; 人机协同模式; 人机协同设计


【栏      目】:

学习环境与资源


【中文摘要】:

作为教育数字化转型的首要任务,智慧学习环境建设过分强调技术之于教育的能力,而忽略教育主体的价值与地位,涌现出场景割裂、数据孤岛等问题。人机协同旨在充分发挥人与机器的优势,弥补彼此的劣势,成为指导智慧学习环境创设与优化的最优解。研究将人机协同视为智慧学习环境设计的基线思维,构建了由数据模型层、技术支撑层和场景应用层三个层级,包含场景、数据、模型、资源、工具与服务等六个要素的智慧学习环境概念模型。基于普瑞斯的人机功能分配决策矩阵理论,提出了AI讲师、执行型AI+人类助手、伙伴型AI+人类同侪、助教型AI+人类教练、人类导师等五种人机协同模式。在此基础上,研究制定了智慧学习环境各层级的设计原则,分析了数据模型层的决策协同设计、技术支撑层的交互协同设计和场景应用层的流程协同设计,讨论了人机协同模式中人机互信和价值对齐的建构策略,以期指导智慧学习环境中的人机协同设计。


【英文摘要】:

As the primary task of digital transformation of education, the construction of smart learning environment excessively emphasizes the ability of technology in education, but ignores the value and status of educational subjects, resulting in problems such as scene fragmentation and data silos. Human-machine collaboration aims to fully leverage the strengths of humans and machines, compensates for each other's weaknesses, and becomes the optimal solution to guide the creation and optimization of smart learning environment. In this study, human-machine collaboration is regarded as the baseline thinking for the design of smart learning environment. A conceptual model of smart learning environment is constructed, which consists of three layers of the data model layer, the technical support layer, and the scene application layer, and six elements of scenes, data, models, resources, tools, and services. Based on the decision matrix of human-machine function allocation, five human-machine collaboration models are proposed, including AI instructor, executive AI+human teaching assistant, partner AI+human peer, assistant AI+human coach, and human mentor. On this basis, the study develops the design principles for each level of smart learning environment, analyses the design of decision-making collaboration at the data model layer, the design of interactive collaboration at the technical support layer, and the design of process collaboration design at the scene application layer. Strategies for constructing human-machine trust and value alignment in human-machine collaboration model are discussed, with a view to guiding the design of human-machine collaboration in smart learning environment.

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