EduPlanner: LLM-Based Multi-Agent Systems for Customized and Intelligent Instructional Design
Journal:
arXiv
Published Date:
Apr 7, 2025
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
