arXiv:2504.05370v1 Announce Type: new
Abstract: Large Language Models (LLMs) have significantly advanced smart education in the Artificial General Intelligence (AGI) era. A promising application lies in the automatic generalization of instructional design for curriculum and learning activities, focusing on two key aspects: (1) Customized Generation: generating niche-targeted teaching content based on students’ varying learning abilities and states, and (2) Intelligent Optimization: iteratively optimizing content based on feedback from learning effectiveness or test scores. Currently, a single large LLM cannot effectively manage the entire process, posing a challenge for designing intelligent teaching plans. To address these issues, we developed EduPlanner, an LLM-based multi-agent system comprising an evaluator agent, an optimizer agent, and a question analyst, working in adversarial collaboration to generate customized and intelligent instructional design for curriculum and learning activities. Taking mathematics lessons as our example, EduPlanner employs a novel Skill-Tree structure to accurately model the background mathematics knowledge of student groups, personalizing instructional design for curriculum and learning activities according to students’ knowledge levels and learning abilities. Additionally, we introduce the CIDDP, an LLM-based five-dimensional evaluation module encompassing clarity, Integrity, Depth, Practicality, and Pertinence, to comprehensively assess mathematics lesson plan quality and bootstrap intelligent optimization. Experiments conducted on the GSM8K and Algebra datasets demonstrate that EduPlanner excels in evaluating and optimizing instructional design for curriculum and learning activities. Ablation studies further validate the significance and effectiveness of each component within the framework. Our code is publicly available at https://github.com/Zc0812/Edu_Planner
Advancing Smart Education with Large Language Models and EduPlanner
Large Language Models (LLMs) have revolutionized the field of smart education in the era of Artificial General Intelligence (AGI). One promising application of LLMs is the automatic generalization of instructional design for curriculum and learning activities. This includes generating niche-targeted teaching content based on students’ varying learning abilities and states, as well as iteratively optimizing content based on feedback from learning effectiveness or test scores.
However, a single large LLM may not be sufficient to effectively manage the entire process, presenting a challenge in designing intelligent teaching plans. To address this issue, the researchers have developed EduPlanner, an LLM-based multi-agent system that comprises three agents working together in adversarial collaboration:
- Evaluator Agent: This agent is responsible for evaluating the quality of instructional design based on the criteria of clarity, integrity, depth, practicality, and pertinence. It utilizes a novel module called CIDDP (Clarity, Integrity, Depth, Practicality, Pertinence) to comprehensively assess the quality of mathematics lesson plans.
- Optimizer Agent: The optimizer agent uses the feedback gathered from the evaluator agent to iteratively optimize the instructional design. It aims to improve the effectiveness of the curriculum and learning activities based on the performance of the students.
- Question Analyst: This agent analyzes the questions asked by students during the learning process and provides insights into their understanding and knowledge gaps. This information is then used to personalize the instructional design for curriculum and learning activities.
EduPlanner takes mathematics lessons as an example and employs a novel Skill-Tree structure to accurately model the background mathematics knowledge of student groups. This allows for personalized instructional design tailored to individual students’ knowledge levels and learning abilities. By leveraging LLMs, EduPlanner can generate customized and intelligent instructional design, enhancing the overall learning experience.
The researchers conducted experiments using the GSM8K and Algebra datasets to evaluate the performance of EduPlanner. The results demonstrate that EduPlanner excels in evaluating and optimizing instructional design for curriculum and learning activities. Ablation studies further validate the significance and effectiveness of each component within the framework.
The multi-disciplinary nature of this work is noteworthy. It combines expertise in natural language processing, educational psychology, and computer science to develop a system that leverages the power of LLMs for intelligent teaching plans. The integration of the CIDDP evaluation module adds a comprehensive and objective assessment of the quality of instructional design, ensuring that the curriculum and learning activities are of high standards.
In conclusion, EduPlanner represents a significant advancement in the field of smart education. By leveraging LLMs and a multi-agent system, it enables the generation of customized and intelligent instructional design for curriculum and learning activities. This work has the potential to greatly improve the effectiveness of education, especially in subjects like mathematics, and pave the way for further developments in AGI-based smart education.