Synthesizing High-Quality Programming Tasks with LLM-based Expert and Student Agents
Manh Hung Nguyen, Victor-Alexandru Pădurean, Alkis Gotovos, Sebastian Tschiatschek, Adish Singla·April 10, 2025
Summary
PYTASKSYN, a synthesis technique, bridges AI-generated and expert-created programming tasks. It uses expert and student agents to validate AI-generated tasks, ensuring alignment with target concepts, comprehensibility, and absence of critical issues. PYTASKSYN significantly improves task quality, reduces workload, and costs, and enhances engagement compared to existing techniques. It decomposes programming task synthesis into stages, using generative models to simulate expert, tutor, and student agents. Through a multi-agent system, it validates task quality, improving Python task synthesis with minimal cost.
Introduction
Background
Overview of AI-generated and expert-created programming tasks
Challenges in ensuring quality, alignment, and comprehensibility
Objective
To introduce PYTASKSYN as a synthesis technique that validates AI-generated tasks through expert and student agents
Highlight the benefits of PYTASKSYN in improving task quality, reducing workload, and costs, and enhancing engagement
Method
Data Collection
Gathering AI-generated programming tasks for validation
Collecting expert and student feedback on task quality, alignment, and comprehensibility
Data Preprocessing
Cleaning and organizing collected data for analysis
Identifying patterns and issues in AI-generated tasks
Model Development
Designing generative models to simulate expert, tutor, and student agents
Training models to understand task creation, validation, and learning processes
Multi-Agent System
Implementing a system that integrates expert, tutor, and student agents
Facilitating interactions between agents to validate task quality
Validation Process
Using the multi-agent system to assess AI-generated tasks
Iteratively refining tasks based on expert and student feedback
Cost and Engagement Analysis
Comparing costs and engagement levels with existing techniques
Quantifying improvements in task quality and user experience
Results
Quality Improvement
Metrics for task quality enhancement
Case studies demonstrating improved task alignment and comprehensibility
Workload and Cost Reduction
Quantitative analysis of reduced workload and costs
Comparison with traditional task creation methods
Engagement Enhancement
Feedback from students and experts on engagement levels
Strategies for further improving user interaction and satisfaction
Conclusion
Summary of PYTASKSYN's Contributions
Recap of PYTASKSYN's role in AI-generated task validation
Discussion of its impact on programming task synthesis
Future Directions
Potential areas for further research and development
Recommendations for integrating PYTASKSYN into educational and professional settings
Conclusion
Reiterating the significance of PYTASKSYN in advancing AI-generated programming tasks
Emphasizing its potential to revolutionize task creation and validation processes
Basic info
papers
computers and society
artificial intelligence
Advanced features
Insights
What roles do expert, tutor, and student agents play in the PYTASKSYN system?
In what ways does PYTASKSYN enhance engagement and reduce costs compared to existing techniques?
What are the key stages in the decomposition of programming task synthesis in PYTASKSYN?
How does the multi-agent system in PYTASKSYN validate the quality of AI-generated programming tasks?