Personalized Knowledge Transfer Through Generative AI: Contextualizing Learning to Individual Career Goals
Ronja Mehlan, Claudia Hess, Quintus Stierstorfer, Kristina Schaaff

TL;DR
This study demonstrates that personalized learning content generated by AI, aligned with individual career goals, significantly improves learner engagement, satisfaction, and efficiency in digital education environments.
Contribution
The paper introduces a scalable AI-based approach for customizing educational content to individual career aspirations, enhancing motivation and practical relevance.
Findings
Increased session duration for personalized content
Higher satisfaction ratings with goal-aligned learning
Modest reduction in study duration
Abstract
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…
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