从“可记录”到“可教学”：AI赋能非遗教育转化的机制与路径研究——以“遗韵新汇”项目为例

From “Recordable” to “Teachable”: A Study on the Mechanisms and Pathways for Leveraging AI in Transforming Intangible Cultural Heritage Education—A Case Study of the “Yiyun Xinhui” Project

摘要: 在非物质文化遗产保护领域，长期存在“可记录却不可教学”的实践困境：现有数字化手段能够完整记录非遗技艺的视觉成果，却难以将依赖口传心授的隐性知识转化为学校教师可直接使用的标准化教学内容。本文以“遗韵新汇”项目为案例，运用探索性单案例研究法，系统考察AI技术介入下非遗技艺的“可教学化”转化机制。研究发现：(1) 可教学化转化的关键在于“可教学化拆解”，即将整体技艺分解为具备独立教学目标、可操作步骤与可评估标准的模块单元；(2) AI技术在非遗美育中的核心价值在于替代传承人的“过程性指导”功能，通过智能评估与实时反馈构建教学闭环；(3) 收益共享机制是打通“学–创–商”链条、维系传承人持续参与的制度基础。本文提出“可教学化”作为非遗数字化保护的新分析维度，区别于既有研究聚焦的“可记录化”维度，为理解AI技术赋能非遗教育提供了机制层面的理论解释。

Abstract: In the field of intangible cultural heritage (ICH) preservation, a long-standing practical dilemma has persisted: while existing digital tools can comprehensively document the visual manifestations of ICH techniques, they struggle to transform the tacit knowledge—which relies on oral and hands-on transmission—into standardized instructional content that can be directly utilized by school teachers. Using the “Yiyun Xinhui” project as a case study and employing an exploratory single-case study methodology, this paper systematically examines the mechanism through which ICH techniques can be “made teachable” with the integration of AI technology. The findings reveal that: (1) The key to making techniques “teachable” lies in “teachable decomposition”—that is, breaking down the entire technique into modular units each possessing independent instructional objectives, actionable steps, and assessable criteria; (2) The core value of AI technology in ICH aesthetic education lies in its ability to serve as a substitute for the inheritor’s “process-oriented guidance” function, thereby establishing a closed-loop teaching system through intelligent assessment and real-time feedback; (3) A benefit-sharing mechanism serves as the institutional foundation for bridging the “learning-creation-business” chain and sustaining the continuous participation of inheritors. This paper proposes “teachable” as a new analytical dimension for the digital preservation of ICH, distinct from the “recordable” dimension that has been the focus of previous research, and provides a theoretical explanation at the mechanistic level for understanding how AI technology can empower ICH education.