Rule-Based vs. AI-Driven: Comparing PolyAQG Framework and Generative AI Models

This comparative analysis examines the PolyAQG framework and Generative AI models (e.g., ChatGPT, Gemini) across ten key criteria for question generation. The PolyAQG framework, a rule-based approach, is well-suited for structured content and excels in generating consistent questions for educational purposes. However, it may be limited in creativity and depth. Generative AI models, while capable of covering broader topics and interpreting complex contexts, require more computational resources and may introduce inaccuracies in specialized domains. The PolyAQG framework offers scalability within specific domains and predictable error handling. Generative AI models, although scalable across topics, may require fine-tuning for accuracy. Furthermore, Generative AI enables dynamic user interaction and fosters critical thinking, while the PolyAQG framework provides a more limited user interface. The choice between PolyAQG and generative AI depends on application needs. PolyAQG is ideal for structured questions and consistency, while generative AI excels in creativity, adaptability, and user interaction.