# Personalized Learning Path Planning with Goal-Driven Learner State Modeling

arXiv: https://arxiv.org/abs/2510.13215 · HTML: https://mlsanity.com/paper/2510.13215/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 52 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Builds on (most cited first, 10 of 10)

- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities](https://mlsanity.com/paper/2307.16376/) (2023)
- [GenRec: Large Language Model for Generative Recommendation](https://mlsanity.com/paper/2307.00457/) (2023)
- [Knowledge Graphs as Context Sources for LLM-Based Explanations of Learning Recommendations](https://mlsanity.com/paper/2403.03008/) (2024)
- [Reinforcement Learning for Education: Opportunities and Challenges](https://mlsanity.com/paper/2107.08828/) (2021)
- [Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report](https://mlsanity.com/paper/2508.01059/) (2025)
- [Personalized Education in the AI Era: What to Expect Next?](https://mlsanity.com/paper/2101.10074/) (2021)
- [Building Contextual Knowledge Graphs for Personalized Learning Recommendations using Text Mining and Semantic Graph Completion](https://mlsanity.com/paper/2401.13609/) (2024)
- [LearnMate: Enhancing Online Education with LLM-Powered Personalized Learning Plans and Support](https://mlsanity.com/paper/2503.13340/) (2025)
- [Constructing a personalized learning path using genetic algorithms approach](https://mlsanity.com/paper/2104.11276/) (2021)

## Built on by (most cited first, 2 of 2)

- [Evolutionary Reinforcement Learning based AI tutor for Socratic Interdisciplinary Instruction](https://mlsanity.com/paper/2512.11930/) (2025)
- [IB-GRPO: Aligning LLM-based Learning Path Recommendation with Educational Objectives via Indicator-Based Group Relative Policy Optimization](https://mlsanity.com/paper/2601.14686/) (2026)

## Abstract (arXiv)

Personalized Learning Path Planning (PLPP) aims to design adaptive learning paths that align with individual goals. While large language models (LLMs) show potential in personalizing learning experiences, existing approaches often lack mechanisms for goal-aligned planning. We introduce Pxplore, a novel framework for PLPP that integrates a reinforcement-based training paradigm and an LLM-driven educational architecture. We design a structured learner state model and an automated reward function that transforms abstract objectives into computable signals. We train the policy combining supervised fine-tuning (SFT) and Group Relative Policy Optimization (GRPO), and deploy it within a real-world learning platform. Extensive experiments validate Pxplore's effectiveness in producing coherent, personalized, and goal-driven learning paths. We release our code and dataset at https://github.com/Pxplore/pxplore-algo.