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

arXiv: https://arxiv.org/abs/2508.04070 · HTML: https://mlsanity.com/paper/2508.04070/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [Educational Personalized Learning Path Planning with Large Language Models](https://mlsanity.com/paper/2407.11773/) (2024)
- [Implementing Learning Principles with a Personal AI Tutor: A Case Study](https://mlsanity.com/paper/2309.13060/) (2023)
- [Revolutionising Distance Learning: A Comparative Study of Learning Progress with AI-Driven Tutoring](https://mlsanity.com/paper/2403.14642/) (2024)
- [When Generative AI Meets Workplace Learning: Creating A Realistic & Motivating Learning Experience With A Generative PCA](https://mlsanity.com/paper/2405.15561/) (2024)

## Built on by (most cited first, 1 of 1)

- [Using AI-based Learning Assistants in Higher Education: A Large-Scale Descriptive Analysis](https://mlsanity.com/paper/2607.08748/) (2026)

## Abstract (arXiv)

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.