通过生成式人工智能实现个性化知识迁移：将学习情境化到个人职业目标
Personalized Knowledge Transfer Through Generative AI: Contextualizing Learning to Individual Career Goals
AI总结：
本研究通过超4000名学习者的混合方法实验，证明基于生成式AI的职业目标导向内容个性化能提升参与度、满意度并缩短学习时长，弥合学术与职场应用差距。
摘要：
随着人工智能日益融入数字化学习环境，根据学习者个人职业目标对学习内容进行个性化调整，有望提升参与度和长期学习动力。在我们的研究中，我们探讨了基于生成式人工智能（GenAI）的学习系统中，以职业目标为导向的内容适配如何影响学习者的参与度、满意度和学习效率。这项混合方法实验涉及超过4000名学习者，其中一组接受针对其职业目标量身定制的学习情境，另一组为对照组。定量结果显示，与标准内容相比，个性化内容的会话时长增加、满意度评分更高，且学习时长略有减少。定性分析强调，学习者认为个性化材料具有激励性和实用性，能够促进深度认知参与和对内容的强烈认同。这些发现凸显了将教育内容与学习者职业目标对齐的价值，并表明可扩展的人工智能个性化能够弥合学术知识与职场应用之间的鸿沟。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
