Automatic Generation of Educational Exercises Using Large Language Models: A Practical Exploration of a Master's Research Project

This article introduces the GitHub open-source master's project "Creating Worksheets with Large Language Models", which systematically explores the application of LLMs in automatic educational exercise generation. It covers key issues such as prompt engineering, quality control, and adaptation to educational scenarios, with open-source code and methodologies, providing practical references for AI-assisted educational content generation.

Traditional exercise generation faces problems of manual inefficiency and lack of flexibility and diversity in templates. LLMs can understand complex instructions and generate diverse content, bringing new possibilities to this field. Core project question: How to effectively use LLMs to generate high-quality, customizable educational exercises? It involves practical issues such as technical implementation, quality control, and evaluation of educational adaptation.

Prompt Engineering Strategies

Need to consider difficulty grading (adapting to specific grades/knowledge levels), diversity of question types (multiple choice, fill-in-the-blank, short answer, etc.), knowledge point coverage (matching learning objectives), and language style (suitable for the target age group).

Quality Evaluation Framework

1. Accuracy verification (correctness of questions and answers); 2. Difficulty calibration (verifying difficulty with test data); 3. Educational appropriateness (adapting to target learners); 4. Diversity indicators (avoiding repetition and homogenization).

1. Personalized learning: Teachers generate targeted exercises based on students' weak points;
2. Adaptive systems: Dynamically adjust content difficulty and type;
3. Rapid prototyping: Developers quickly generate candidate questions to improve content production efficiency.

1. Accuracy issues: LLMs are prone to "hallucination" errors, requiring verification mechanisms;
2. Difficulty control: It is difficult to accurately distinguish between sub-levels of educational stages;
3. Cultural adaptation: General-purpose LLMs need fine-tuning to adapt to different educational systems;
4. Cost issues: The cost of large-scale use of commercial LLM APIs is relatively high.

Implications

The project promotes the implementation of AI from the laboratory to educational scenarios, and the combination of academia and engineering promotes technological progress; the open-source nature supports collaborative iteration.

Future Directions

Multimodal generation (with images/charts), interactive questions, interdisciplinary integration, and real-time feedback to optimize generation strategies.

The project demonstrates the feasible paths and challenges of AI-assisted educational content generation; the technical value lies in combining scenario needs. It provides references for educational technology-related personnel, and we look forward to more innovations as LLM capabilities improve.