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

• URL: http://arxiv.org/abs/2508.04070v1
• Date: Wed, 06 Aug 2025 04:03:56 GMT
• Title: Personalized Knowledge Transfer Through Generative AI: Contextualizing Learning to Individual Career Goals
• Authors: Ronja Mehlan, Claudia Hess, Quintus Stierstorfer, Kristina Schaaff,
• Abstract summary: We investigate how career goal-based content adaptation in learning systems based on generative AI (GenAI) influences learner engagement, satisfaction, and study efficiency.<n> Quantitative results show increased session duration, higher satisfaction ratings, and a modest reduction in study duration compared to standard content.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Study [50.065744358362345]
  Large language models (LLMs) have shown impressive capabilities across tasks such as mathematics, coding, and reasoning.<n>Yet their learning ability, which is crucial for adapting to dynamic environments and acquiring new knowledge, remains underexplored.
  arXiv  Detail & Related papers  (2025-06-16T13:24:50Z)
• LLM-powered Multi-agent Framework for Goal-oriented Learning in Intelligent Tutoring System [54.71619734800526]
  GenMentor is a multi-agent framework designed to deliver goal-oriented, personalized learning within ITS.<n>It maps learners' goals to required skills using a fine-tuned LLM trained on a custom goal-to-skill dataset.<n>GenMentor tailors learning content with an exploration-drafting-integration mechanism to align with individual learner needs.
  arXiv  Detail & Related papers  (2025-01-27T03:29:44Z)
• Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance [15.064064880549232]
  The concept of hybrid intelligence is still at a nascent stage.<n>How learners can benefit from a symbiotic relationship with various agents is still unknown.<n>ChatGPT may promote learners' dependence on technology and potentially trigger metacognitive laziness.
  arXiv  Detail & Related papers  (2024-12-12T14:32:39Z)
• KaLM: Knowledge-aligned Autoregressive Language Modeling via Dual-view Knowledge Graph Contrastive Learning [74.21524111840652]
  This paper proposes textbfKaLM, a textitKnowledge-aligned Language Modeling approach.<n>It fine-tunes autoregressive large language models to align with KG knowledge via the joint objective of explicit knowledge alignment and implicit knowledge alignment.<n> Notably, our method achieves a significant performance boost in evaluations of knowledge-driven tasks.
  arXiv  Detail & Related papers  (2024-12-06T11:08:24Z)
• Integrating Competency-Based Education in Interactive Learning Systems [1.0052074659955383]
  This paper describes how to make Artemis capable of competency-based education. We show how instructors can define relations between competencies to create a competency relation graph. We present the results of a user study regarding the usability of the newly designed competency visualization.
  arXiv  Detail & Related papers  (2023-08-25T15:11:53Z)
• From Psychological Curiosity to Artificial Curiosity: Curiosity-Driven Learning in Artificial Intelligence Tasks [56.20123080771364]
  Psychological curiosity plays a significant role in human intelligence to enhance learning through exploration and information acquisition. In the Artificial Intelligence (AI) community, artificial curiosity provides a natural intrinsic motivation for efficient learning. CDL has become increasingly popular, where agents are self-motivated to learn novel knowledge.
  arXiv  Detail & Related papers  (2022-01-20T17:07:03Z)
• Transferability in Deep Learning: A Survey [80.67296873915176]
  The ability to acquire and reuse knowledge is known as transferability in deep learning. We present this survey to connect different isolated areas in deep learning with their relation to transferability. We implement a benchmark and an open-source library, enabling a fair evaluation of deep learning methods in terms of transferability.
  arXiv  Detail & Related papers  (2022-01-15T15:03:17Z)
• A Network Science Perspective to Personalized Learning [0.0]
  We examine how learning objectives can be achieved through a learning platform that offers content choices and multiple modalities of engagement to support self-paced learning. This framework brings the attention to learning experiences, rather than teaching experiences, by providing the learner engagement and content choices supported by a network of knowledge.
  arXiv  Detail & Related papers  (2021-11-02T01:50:01Z)
• Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
  The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal. In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
  arXiv  Detail & Related papers  (2021-01-19T12:23:32Z)
• A Review on Intelligent Object Perception Methods Combining Knowledge-based Reasoning and Machine Learning [60.335974351919816]
  Object perception is a fundamental sub-field of Computer Vision. Recent works seek ways to integrate knowledge engineering in order to expand the level of intelligence of the visual interpretation of objects.
  arXiv  Detail & Related papers  (2019-12-26T13:26:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.