Source-linked AI summary
EduPlanner: LLM-Based Multi-Agent Systems for Customized and Intelligent Instructional Design
Xueqiao Zhang, Chao Zhang, Jianwen Sun, Jun Xiao, Yi Yang, Yawei Luo
TL;DR
Teachers face time-consuming work tailoring instructional designs to students’ knowledge levels, while existing instructional design approaches have limitations in personalization and optimization. EduPlanner combines a Skill-Tree with evaluator, optimizer, and analyst agents and CIDPP assessment; the authors report improved design outcomes and identify subject-specific applicability as a limitation.
Problem
Method
EduPlanner uses a Skill-Tree to model student knowledge and coordinates evaluator, optimizer, and analyst agents in an adversarial design-and-feedback process.
Source: §I · p. 2, B. Skill-Tree Structure · p. 3Results
EduPlanner significantly improves instructional-design efficiency, reduces pedagogical burdens, and facilitates alignment with diverse student needs in CIDPP-based experiments on GSM8K and Algebra.
Source: §I · p. 2Takeaways & Limitations
A Skill-Tree and three-agent system can generate instructional designs reflecting differences in students’ knowledge levels and learning abilities.
Source: §VI · p. 10The Skill-Tree is tailored to instructional design within certain subjects, which may limit its applicability to other disciplines.
Source: A. Limitations · p. 10
Abstract
from arXiv · showhide
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
I. INTRODUCTION
Instructional design is laborious because teachers must account for diverse students and classroom conditions, while existing LLM approaches lack personalization or iterative optimization. EduPlanner combines student profiling, multi-agent generation and refinement, and five-dimensional evaluation; experiments on GSM8K and Algebra report improved instructional-design efficiency and alignment with diverse student needs.
I. INTRODUCTION
- Traditional lesson planning relies on teacher experience and trial and error, is laborious and inefficient, and lacks a standard metric for plan quality.Teachers must also account for varied student dispositions and unpredictable classroom environments.Source: §I · p. 1
- Existing zero-shot LLM methods often ignore individual student needs, while other methods generate and evaluate plans without optimizing them.The paper associates these gaps with plans that may lack case explanations and appropriate course difficulty levels.Source: §I · p. 2
- EduPlanner combines Evaluator, Optimizer, and Analyst agents in an adversarial collaboration to generate, evaluate, and optimize instructional designs.The Analyst examines error-prone examples for incorporation into subsequent plans.Source: §I · p. 2
- Compared with conventional methods, EduPlanner improves instructional-design efficiency, reduces pedagogical burdens, and facilitates alignment with diverse student needs on GSM8K and Algebra.The evaluation uses CIDPP-based experiments.Source: §I · p. 2
- A Skill-Tree profiles student knowledge and abilities to guide tailored instructional content, while CIDPP evaluates plans on five dimensions.CIDPP comprises Clarity, Integrity, Depth, Practicality, and Pertinence.Source: §I · p. 2
II. RELATED WORK
Related work spans AI-supported learning, LLM-based educational agents, general multi-agent systems, and LLM-as-judge evaluation. The cited work includes customized learning support and agent collaboration, while also noting inefficiency in some multi-model approaches and limitations of conventional evaluation for open-ended outputs.
A. Smart Education
- AI applications in smart education include speech recognition, customized learning, instant assistance, augmented-reality learning, and tools for data-driven learning.The cited examples include AI for speech recognition and customized learning and ChatGPT-based assistance.Source: A. Smart Education · p. 2
B. Generative AI for Educational Transformation
- Educational generative-AI work includes simulating student behavior, anticipating learning actions, generating virtual-student behaviors, and supporting assessment workflows.The examples include GPTeach, EduAgent [10], and Gradescope.Source: B. Generative AI for Educational… · p. 2
C. LLMs-based Multi-agent Systems
- Multi-agent research covers collaborative agents and role-playing systems, but approaches relying on multiple language models can be inefficient, particularly in open-ended settings.Examples include MetaGPT [34], Autogen [35], AutoGPT [36], and CAMEL.Source: C. LLMs-based Multi-agent Systems · pp. 2–3
D. Evaluation using LLMs
- Open-ended LLM evaluation lacks definitive reference answers, making rule-based and similarity metrics unsuitable and expert review costly.LLM judges and pairwise comparisons are explored as alternatives, including L-eval [44].Source: D. Evaluation using LLMs · p. 3
A. Overview
EduPlanner coordinates evaluator, optimizer, and analyst agents in an iterative adversarial process, outputting the highest-scoring design. Its Skill-Tree represents student abilities across multiple dimensions to support instructional designs tailored to differing knowledge backgrounds.
A. Overview
- Evaluator feedback guides the Optimizer and Analyst, and evaluation scores drive an iterative adversarial process whose highest-scoring queued design is output.The output includes knowledge-point explanations and question explanations.Source: A. Overview · p. 3
B. Skill-Tree Structure
- The Skill-Tree models student competencies to generate instructional designs for different knowledge backgrounds; the paper illustrates the approach with mathematics while noting other subjects can also be applied.The structure is intended to help the evaluator capture differences in student abilities.Source: B. Skill-Tree Structure · pp. 3, 5
- The Skill-Tree uses five specialized subnodes under each primary node to quantify abilities across dimensions and describe individual knowledge bases and learning abilities.This offers a more detailed profile than a binary good-or-bad evaluation.Source: B. Skill-Tree Structure · p. 5
- Its mathematics example represents Numerical Calculation, Abstract Thinking, Logical Reasoning, Analogy Association, and Spatial Imagination abilities.Spatial Imagination is noted as particularly important for solving geometric problems in algebraic equations.Source: Student Abilities (Mathematics as… · p. 5
C. Instructional Design Evaluation
The Evaluator Agent assesses instructional designs using CIDPP and student Skill-Tree scores, then gives the Optimizer Agent feedback on strengths and weaknesses.
C. Instructional Design Evaluation
- CIDPP evaluates instructional designs across Clarity, Integrity, Depth, Practicality, and Pertinence.The dimensions address clear goals, completeness, deep thinking, practical application, and diverse learning needs.Source: C. Instructional Design Evaluation · p. 5, 1) Evaluation Criteria · p. 5
- The Evaluator Agent Expert Evaluation (EAEE) assesses designs against students’ Skill-Tree knowledge and average scores on exam questions.It evaluates the design across test questions and summarizes advantages and disadvantages for the Optimizer Agent.Source: 3) Instructiona Design Evaluation… · p. 6
- The Evaluator Agent returns feedback, including a design’s advantages and disadvantages, to guide optimization.The EAEE process summarizes feedback gathered across test-question evaluations for the Optimizer Agent.Source: 3) Instructiona Design Evaluation… · p. 6
D. Instructional Design Optimization
The Optimizer Agent uses evaluator feedback to revise instructional designs, while a score-sorted queue retains the top candidates. An example analyzer combines examples from repeated question-specific outputs into the final design.
D. Instructional Design Optimization
- The optimizer’s goal is to maximize the Evaluator Agent’s score, using evaluator feedback to generate new instructional designs.The optimization function uses feedback and queued designs with their scores to generate a new design.Source: 1) Optimizer Agent · p. 6
- The design queue inserts each newly evaluated design by score, sorts candidates, and trims the queue to its top P designs.The queue is dynamically sorted and has a maximum capacity P.Source: 1) Optimizer Agent · p. 6
- For each test question, the optimizer generates a revised design and the example analyzer extracts examples; suggestions and examples are combined into the final design.The initial prompt includes the student description, initial design, evaluation and optimization tasks, and a test question.Source: 2) Instructional Design Optimization… · p. 7
E. Instructional Design Analysis
The Analyst Agent adds likely student error points to examples in instructional designs, using common algebra mistakes and students’ knowledge backgrounds to tailor the reminders.
E. Instructional Design Analysis
- The Analyst Agent identifies three error-prone points for each example, tailored to students’ corresponding knowledge backgrounds.The system compiles 50 common algebra-equation mistakes into five groups.Source: 1) Analyst Agent · p. 7
- It inserts the identified error points into example explanations to alert students and help them avoid mistakes.The reminders are intended to help students solve problems correctly.Source: 1) Analyst Agent · p. 7, 2) Error-prone Point Analysis Algorithm · p. 7
- The Analyst Agent Expert Analysis (AAEA) extracts examples from instructional designs to identify common student errors.The algorithm uses an annotated common-mistakes resource as a reference for generating potential errors for each example.Source: 2) Error-prone Point Analysis Algorithm · p. 7
IV. EXPERIMENTS
The experiments section says the authors evaluate instructional-design quality with CIDPP and use ablation experiments to examine framework modules.
IV. EXPERIMENTS
- The authors report conducting experiments to verify the results of the generation framework.The passage does not provide specific experiment results.Source: §IV · p. 7
- CIDPP is proposed as an evaluation system for analyzing instructional-design quality.Source: §IV · p. 7
- Ablation experiments are conducted to demonstrate the roles of the framework’s modules.Source: §IV · p. 7
A. Hyperparameter Configuration
The evaluator, optimizer, and analyzer use different temperature settings to balance stable evaluation with diverse generation. The evaluator is configured at 0.0, the optimizer at 1.0, and the analyzer at 0.7.
A. Hyperparameter Configuration
- The optimizer agent uses GPT-4 at temperature 1.0 to generate diverse instructional-design content.The paper specifies temperature 1.0 for the optimizer’s base model.Source: A. Hyperparameter Configuration · p. 7
- The evaluator agent uses Meta-Llama-3-70B-Instruct at temperature 0.0 for stable output.The evaluator’s base model is configured with temperature 0.0.Source: A. Hyperparameter Configuration · p. 7
- The GPT-4 analyzer uses temperature 0.7 to balance stability and diversity.The paper gives 0.7 as the analyzer’s temperature setting.Source: A. Hyperparameter Configuration · p. 7
B. Contrastive Results
The contrastive results report that EduPlanner scores higher than He’s method [15] and has a smoother optimization process under matched hyperparameters. The ablation discussion attributes distinct gains to the combined agents, analyst, and Skill-Tree mechanism.
B. Contrastive Results
- The comparison generates algebraic-equation lesson designs directly with GPT-3.5-turbo, Llama-3-70B-Instruct, and GPT-4, then evaluates them with CIDPP.The generation prompt requests knowledge-point and example explanations in no more than 200 words; the evaluation standard lists clarity, integrity, depth, practicality, and pertinence.Source: B. Contrastive Results · pp. 7–8
- EduPlanner scores higher than He’s method [15] and produces a smoother optimization process under matched hyperparameters.The comparison uses hyperparameters consistent with the initial instructional design.Source: Figure 5 · p. 8, B. Contrastive Results · p. 8
- EduPlanner preserves the evaluator and optimizer from baseline [15] while adding a Skill-Tree structure and analyst agent.The paper presents these additions as part of its optimization framework.Source: B. Contrastive Results · p. 8
- The ablation study measures each component’s individual contribution to optimization results using CIDPP.The study covers the instructional-design optimization framework and refers to Tab. II and Figs. 6–7.Source: B. Contrastive Results · p. 8
- Adding the analyst agent notably improves practicality and pertinence, while the Skill-Tree improves other metrics without a pronounced clarity gain.The Skill-Tree maps student competencies and aligns educational content with individual knowledge backgrounds.Source: B. Contrastive Results · p. 8
- The evaluator and optimizer together establish a strong baseline in clarity, integrity, and depth; either agent alone is less favorable.The reported contrastive results say a standalone evaluator or optimizer does not yield the most favorable results.Source: B. Contrastive Results · p. 8
V. DISCUSSION
The discussion frames instructional design as labor-intensive because teachers must account for individual student characteristics and knowledge while adapting teaching strategies. It presents EduPlanner’s agents, Skill-Tree, and five-dimensional evaluation system as a way to evaluate and improve designs for individual needs.
V. DISCUSSION
- The discussion situates personalized education within a broader shift toward dynamically adjusting resources and teaching strategies to individual student needs.It describes smart education as receiving increasing attention as a future educational trend.Source: §V · pp. 8–9
- Figure 6 illustrates each framework component’s influence on each evaluation metric in CIDDP using a bar chart, while Figure 7 uses a radar chart to portray each component’s incremental effects.Source: Figure 6 · p. 9, Figure 7 · p. 9
- Traditional instructional-design preparation is time-consuming because teachers must understand individual student characteristics and knowledge levels to align content with their needs.The discussion also notes that changing educational requirements may make previous teaching methods insufficient.Source: §V · p. 9
- Clear assessment and feedback mechanisms for quickly adjusting teaching strategies remain a challenge, alongside educators’ need to master and integrate educational technologies.The discussion cites multimedia tools and online platforms as examples of such technologies.Source: §V · p. 9
- EduPlanner combines an instructional-design evaluation system, a student-ability Skill-Tree, and three collaborative agents to customize quality improvement for individual needs.The passage characterizes these elements as enabling comprehensive evaluation and improvement.Source: §V · p. 9
- CIDPP evaluates instructional designs across clarity, integrity, depth, practicality, and pertinence, aligning its criteria with pedagogical principles.The discussion links the dimensions to goal-oriented teaching, comprehensive learning, deep-level learning, real-world application, and personalized teaching.Source: §V · p. 9
VI. CONCLUSION
EduPlanner automates instructional-design evaluation and optimization, with experiments reporting superior performance and ablations identifying key components. The paper also notes limits involving subject scope, resource costs, and practical-tool integration, and outlines future research directions.
VI. CONCLUSION
- EduPlanner automates instructional-design evaluation and optimization, and experiments report superior performance while ablations identify the Skill-Tree and Analyst Agent as crucial components.The system comprises the Evaluator, Optimizer, and Example Analyzer.Source: §VI · p. 10
A. Limitations
- The Skill-Tree is tailored to certain subjects, which may limit its applicability to other disciplines.Source: A. Limitations · p. 10
- EduPlanner’s substantial LLM resource consumption leads to high costs in time and money.Source: A. Limitations · p. 10
- EduPlanner lacks adaptive integration with existing tools in practical teaching environments.Source: A. Limitations · p. 10
B. Future Work
- Future work should develop subject-tailored models, improve real-world educational simulations, and combine disciplines to support knowledge integration.The paper also points to applying instructional-design optimization knowledge in other smart-education domains.Source: B. Future Work · p. 10