Enhancing Academic Performance with Generative AI-Based Quiz Platform
Chia-Kai Chang, Lee-Chia-Tung Chien
Abstract
In this work, we build a QuizGPT web interface for learners to learn Python via quizzing and chatting. Additionally, we evaluate the impact of generative AI on learning. To effectively evaluate learners’ learning achievements, the quizzes must be diverse, and the options should be challenging to ensure a complete understanding of the material. It is a time-consuming challenge for teachers to create adaptive quizzes based on learners’ levels for individual learning needs. This study generates 366 quiz questions automatically via generative AI in three difficulty levels (easy, medium, hard), based on Python knowledge points. A positive correlation observed between the QuizGPT platform activities and learners’ test scores. These activities include the number of quizzes attended, the correctness rates, and the frequency of participation in generative AI sessions. Additionally, QuizGPT platform activities could predict learners’ test scores with a Root Mean Squared Error percentage (%RMSE) of 3.58%. Remarkably, interaction with generative AI proves to enhance Python programming skills more effectively than the number of quiz attempts. Survey analysis reveals that QuizGPT can reduce learning anxiety and enhance learning interest. Our findings suggest that generative AI, as implemented in the QuizGPT platform, is a potent tool for academic improvement, significantly enhancing self-regulation and engagement in learning Python programming.