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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.04070 |
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| _version_ | 1866915430447185920 |
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| author | Mehlan, Ronja Hess, Claudia Stierstorfer, Quintus Schaaff, Kristina |
| author_facet | Mehlan, Ronja Hess, Claudia Stierstorfer, Quintus Schaaff, Kristina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04070 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Personalized Knowledge Transfer Through Generative AI: Contextualizing Learning to Individual Career Goals Mehlan, Ronja Hess, Claudia Stierstorfer, Quintus Schaaff, Kristina Artificial Intelligence Computers and Society 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. |
| title | Personalized Knowledge Transfer Through Generative AI: Contextualizing Learning to Individual Career Goals |
| topic | Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2508.04070 |