生成式人工智能与人文社科毕业生主体性重构

Generative Artificial Intelligence and the Reconstruction of Subjectivity among Humanities and Social Sciences Graduates

摘要: 生成式人工智能正在进入资料检索、文本生成、知识整合和成果表达等环节，并改变人文社科知识生产的任务分工与评价依据。研究聚焦毕业学年、求职准备和入职初期，采用概念分析与文献综合方法，从社会技术共演化视角讨论人文社科毕业生的主体性变化。分析表明，知识任务重组、算法中介、责任边界调整和评价标准变化，在提高资料处理、文本表达与跨界协作效率的同时，也使认知外包、解释贫乏、责任模糊和职业行动不确定等问题更加突出。针对这些问题，高校可把“反思性知识实践者”作为培养定位，在专业课程、过程评价、真实项目和就业服务中训练人机协同、证据校验与知识重构、批判解释与价值判断、跨界转化和职业行动能力。结论限于共性机制与培养原则，其群体适用性仍需经验检验。

Abstract: Generative artificial intelligence is entering information retrieval, text generation, knowledge integration, and the presentation of outputs, changing how work is divided and evaluated in humanities and social sciences knowledge production. Focusing on the final year of study, job-search preparation, and the early stage of employment, the study uses conceptual analysis and literature synthesis to examine changes in the subjectivity of humanities and social sciences graduates from a socio-technical co-evolution perspective. The analysis shows that the reorganization of knowledge tasks, algorithmic mediation, adjustment of responsibility boundaries, and changing evaluation standards can improve the efficiency of information processing, textual expression, and cross-boundary collaboration, while making cognitive outsourcing, impoverished interpretation, ambiguous responsibility, and uncertainty in career action more pronounced. In response, universities can adopt the “reflective knowledge practitioner” as a training orientation and develop five competencies through disciplinary courses, process-based assessment, authentic projects, and career services: human-AI collaboration, evidence verification and knowledge reconstruction, critical interpretation and value judgment, cross-boundary transformation, and career action. The conclusions concern common mechanisms and educational principles; their applicability across graduate groups requires empirical examination.