Description

Currently, the quiz generation system relies entirely on the ML model to create questions from a provided input paragraph. While this is effective, it lacks the flexibility for users (e.g., teachers, professors, or trainers) to add custom questions. This limitation can result in quizzes that don’t fully cover specific topics or meet the unique needs of the user.

Why This is Important

• Enhanced Flexibility: Users can supplement ML-generated questions with their own, ensuring quizzes are more comprehensive and tailored.
• Improved Usability: Educators and professionals often need to include specific questions that align with their teaching or training goals, which the ML model may not fully address.
• Better Coverage: Custom questions allow users to fill gaps in the generated content, ensuring critical concepts are included.

Example Use Case

A professor inputs a topic into the system, and the ML model generates a quiz. However, the professor notices that a key concept is missing and wants to manually add a few questions to ensure the quiz is complete and aligns with their curriculum.

Issue Type:

• Feature
• Bug
• Documentation
• Improvement
• Refactor
• Task