# Educational Personalized Learning Path Planning with Large Language Models

arXiv: https://arxiv.org/abs/2407.11773 · HTML: https://mlsanity.com/paper/2407.11773/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Visual In-Context Learning for Large Vision-Language Models](https://mlsanity.com/paper/2402.11574/) (2024)
- [ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification](https://mlsanity.com/paper/2203.02225/) (2022)
- [EventBERT: A Pre-Trained Model for Event Correlation Reasoning](https://mlsanity.com/paper/2110.06533/) (2021)
- [The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs](https://mlsanity.com/paper/2303.12961/) (2023)
- [Improving Cross-modal Alignment for Text-Guided Image Inpainting](https://mlsanity.com/paper/2301.11362/) (2023)
- [Building Contextual Knowledge Graphs for Personalized Learning Recommendations using Text Mining and Semantic Graph Completion](https://mlsanity.com/paper/2401.13609/) (2024)
- [FOKE: A Personalized and Explainable Education Framework Integrating Foundation Models, Knowledge Graphs, and Prompt Engineering](https://mlsanity.com/paper/2405.03734/) (2024)
- [Constructing a personalized learning path using genetic algorithms approach](https://mlsanity.com/paper/2104.11276/) (2021)

## Built on by (most cited first, 5 of 5)

- [Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2502.06872/) (2025)
- [Towards Transparent AI: A Survey on Explainable Language Models](https://mlsanity.com/paper/2509.21631/) (2025)
- [Personalized Knowledge Transfer Through Generative AI: Contextualizing Learning to Individual Career Goals](https://mlsanity.com/paper/2508.04070/) (2025)
- [Stay Hungry, Stay Foolish: On the Extended Reading Articles Generation with LLMs](https://mlsanity.com/paper/2504.15013/) (2025)
- [Enhancing Learning Path Recommendation via Multi-task Learning](https://mlsanity.com/paper/2507.05295/) (2025)

## Abstract (arXiv)

Educational Personalized Learning Path Planning (PLPP) aims to tailor learning experiences to individual learners' needs, enhancing learning efficiency and engagement. Despite its potential, traditional PLPP systems often lack adaptability, interactivity, and transparency. This paper proposes a novel approach integrating Large Language Models (LLMs) with prompt engineering to address these challenges. By designing prompts that incorporate learner-specific information, our method guides LLMs like LLama-2-70B and GPT-4 to generate personalized, coherent, and pedagogically sound learning paths. We conducted experiments comparing our method with a baseline approach across various metrics, including accuracy, user satisfaction, and the quality of learning paths. The results show significant improvements in all areas, particularly with GPT-4, demonstrating the effectiveness of prompt engineering in enhancing PLPP. Additional long-term impact analysis further validates our method's potential to improve learner performance and retention. This research highlights the promise of LLMs and prompt engineering in advancing personalized education.