The problem Adaptive AI Questioning solves

Adaptive AI Questioning addresses the critical challenge of providing personalized learning experiences. By dynamically adjusting the difficulty of questions based on an individual's proficiency, it ensures an optimal learning curve for each user. This approach not only enhances engagement and comprehension but also promotes efficient knowledge retention and reduces frustration caused by mismatched question difficulty. The system is designed to support learners of diverse skill levels, making education more accessible, effective, and adaptive to individual needs.

Challenges we ran into

One significant challenge we faced during the development of this project was ensuring the proper alignment between the student's proficiency level and the question difficulty. Initially, the system occasionally selected questions that were either too easy or too hard, leading to skewed results in proficiency updates.

To overcome this, we fine-tuned the algorithm to improve the balance between exploration and exploitation by adjusting the epsilon value and incorporating a more robust calculation for proficiency-distance matching. We also implemented extensive testing and debugging to ensure the logic worked seamlessly under different scenarios.

Another hurdle was managing missing or inconsistent data in the question dataset. We resolved this by implementing data validation and preprocessing steps to handle missing values and inconsistencies effectively.