Educational Personalized Learning Path Planning with Large Language Models
It addresses personalized education for learners, offering a novel method but is incremental as it applies existing LLMs with new prompts to a domain-specific problem.
This paper tackled the problem of traditional Personalized Learning Path Planning (PLPP) systems lacking adaptability, interactivity, and transparency by integrating Large Language Models (LLMs) with prompt engineering, resulting in significant improvements in accuracy, user satisfaction, and learning path quality, particularly with GPT-4.
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.