当前我国高校毕业生就业市场结构性矛盾日益突出，传统职业规划模式因信息滞后与个性化不足，难以有效支持大学生的就业决策。本文以生成式人工智能（Generative AI）为研究对象，探讨其在职业规划中的赋能机制及对就业决策效能的影响。研究基于文献综述与典型案例分析，归纳生成式AI在职业规划中的三类功能：数据分析、场景模拟与内容生成。结果表明，生成式AI能够通过实时数据挖掘与岗位匹配，缓解岗位供需失衡与考研避业现象；通过虚拟场景实训提升学生的实践经验积累；通过可视化内容生成促进学生薪资与岗位期望的理性调整。典型企业、高校与政府案例显示，该技术显著提升大学生的就业决策清晰度与匹配度。然而，算法偏见、数据隐私及过度依赖等问题仍需警惕。研究最后提出政府、高校与学生三方协同的优化路径，为构建“技术赋能+人文引导”的职业规划新模式提供参考。At present, the structural contradictions in China’s college graduate employment market have become increasingly prominent. Traditional career planning models, plagued by information lag and lack of personalization, struggle to effectively support college students' employment decisions. This paper takes Generative Artificial Intelligence (Generative AI) as the research object, exploring its empowerment mechanisms in career planning and its impact on the effectiveness of employment decision-making. Based on a literature review and analysis of typical cases, the study summarizes three core functions of Generative AI in career planning: data analysis, scenario simulation, and content generation.The results show that Generative AI can alleviate the imbalance between job supply and demand as well as the phenomenon of postgraduate exam preparation as an avoidance of employment through real-time data mining and job matching; enhance students' accumulation of practical experience via virtual scenario training; and promote the rational adjustment of students' salary and job expectations through visual content generation. Cases from typical enterprises, universities, and government departments indicate that this technology significantly improves the clarity and matching degree of college students' employment decisions. However, issues such as algorithmic bias, data privacy risks, and excessive dependence on the technology still require vigilance.Finally, the study proposes a tripartite collaborative optimization path involving the government, universities, and students, aiming to provide a reference for constructing a new "technology empowerment + humanistic guidance" career planning model.
钟嘉仪 et al. (Fri,) studied this question.