Improved Framework Model to Train and Evaluate Difficulty of Interview Question Using Generative AI

Abstract

Research on question difficulty estimation is in demand for accurate assessments tools and other models for different purposes. Recent years include many implementations on this topic based on BERT (bidirectional encoder representations), RNN (recurrent neural networks), and other classification models. Traditional methods for question difficulty estimation have primarily focused on linguistic and structural analysis and trained on large pre-labeled datasets of questions and their difficulty level. This chapter presents an approach that combines these conventional techniques with Generative AI for more accurate question difficulty estimation. The principle behind the method is that as the AI system dives deeper into documentation to formulate questions, the questions generated are likely to be more complex or rare and, therefore, deemed more difficult. By utilizing this multi-dimensional approach, the research aims to refine the question difficulty estimator to better reflect the true complexity of technical questions.