【摘要】 针对传统高职教育评价中存在的主观性强、滞后明显、维度单一等问题，依托服务于轨道交通的计算机高水平专业群建设，探索人工智能技术在学生评价中的深度应用。通过整合“知识掌握、技能操作、项目实践、创新能力、职业素养”的多元异构教学数据，生成学生“认知—技能—创新”成长数字画像，形成“数据采集—智能分析—动态反馈—持续优化”的闭环机制，推动教育评价从经验判断向数据驱动转型。更多还原

【Abstract】 In response to persistent challenges in traditional higher vocational education evaluation,such as strong subjectivity,significant time lags,and overly narrow assessment dimensions.This study leverages the development of a high-level computer technology professional cluster serving the rail transit industry to explore the deep integration of artificial intelligence into student assessment.By integrating multi-source,heterogeneous teaching data encompassing knowledge mastery,skill operation,project practice,innovation capability,and professional competence,the system generates dynamic "cognition–skills–innovation" digital growth portraits for students.This approach establishes a closed-loop mechanism of "data collection–intelligent analysis–dynamic feedback–continuous optimization",thereby driving a paradigm shift in educational evaluation from experience-based judgment toward data-driven decision-making.更多还原