黄冈职业技术学院学报

新质生产力与数智技术（AIGC等）正在重塑职教范式，但电子信息领域传统“双师型”教师评价滞后，难以适应人机协同的教学新要求。基于分布认知理论与CIPP模式，采用混合研究法，利用Python挖掘招聘数据提取初始指标，通过15位专家两轮德尔菲法咨询进行筛选，并运用层次分析法（AHP）确定权重，确立了包含4个一级、9个二级和16个三级指标的评价体系，验证了“技教融合”的核心特征.据此提出构建基于数据画像的分层诊改、产教耦合的“逆向工程”培养及技术赋能的伴随式评价机制。

New quality productive forces and digital-intelligent technologies(e.g. AIGC) are reshaping the paradigm of vocational education. However, the traditional evaluation of “dual-qualified” teachers in the electronic information sector lags behind, making it difficult to adapt to the new requirements of distributed human-machine collaborative teaching. Based on cognition theory and the CIPP model, this study adopts a mixed research method. Python is used to mine recruitment data and extract initial indicators, which are then screened through two rounds of Delphi consultations with 15 experts. The weight of each indicator is determined by the Analytic Hierarchy Process(AHP). An evaluation system consisting of 4 first-level, 9 second-level and 16 third-level indicators is established, verifying the core feature of “integration of technology and education”. Accordingly, this paper proposes to construct a hierarchical diagnosis and improvement mechanism based on data portraits, a “reverse engineering” training model coupled with industry and education, and a technology-enabled accompanying evaluation mechanism.