用生成式AI根据职业目标定制学习内容，提升学习动机与效率。
Personalized Knowledge Transfer Through Generative AI: Contextualizing Learning to Individual Career Goals
- 基于职业目标生成个性化学习场景，动态适配学习内容。
- 4000+学员参与，个性化组学习时长更长、满意度更高，学习效率略升。
- 适合关注AI赋能教育、职业导向学习系统的设计者与研究者。
随着人工智能深度融入数字学习环境，将学习内容个性化以契合学习者的个体职业目标，有望显著提升学习投入度和长期动机。本研究探讨了基于生成式AI（GenAI）的职业目标驱动内容自适应对学习者参与度、满意度及学习效率的影响。一项混合方法实验涉及超过4000名学习者，其中一组接收与其职业目标匹配的学习情景，另一组为对照组。定量结果显示，个性化组的会话时长增加，满意度评分更高，学习时长略有缩短；定性分析表明，学习者认为个性化内容更具激励性和实用性，促进了深度认知投入，并产生强烈内容认同感。研究结果凸显将教育内容与职业目标对齐的价值，表明可扩展的AI个性化技术能有效弥合学术知识与职场应用之间的鸿沟。
原文摘要 · Abstract (English)
As artificial intelligence becomes increasingly integrated into digital learning environments, the personalization of learning content to reflect learners' individual career goals offers promising potential to enhance engagement and long-term motivation. In our study, we investigate how career goal-based content adaptation in learning systems based on generative AI (GenAI) influences learner engagement, satisfaction, and study efficiency. The mixed-methods experiment involved more than 4,000 learners, with one group receiving learning scenarios tailored to their career goals and a control group. Quantitative results show increased session duration, higher satisfaction ratings, and a modest reduction in study duration compared to standard content. Qualitative analysis highlights that learners found the personalized material motivating and practical, enabling deep cognitive engagement and strong identification with the content. These findings underscore the value of aligning educational content with learners' career goals and suggest that scalable AI personalization can bridge academic knowledge and workplace applicability.
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