Personalized Knowledge Transfer Through Generative AI: Contextualizing Learning to Individual Career Goals

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 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.