Beyond One-Size-Fits-All: An AI-Driven Approach for Personalized Quizzes Using Clustering and ChatGPT
Liyuan Liu · International Journal of Information and Education Technology · 2025
With rapid advancements in Generative AI (GenAI), educators have the opportunity to enhance student engagement and learning through personalized quizzes. Despite their potential, the adoption of customized learning assessments remains limited due to challenges in student grouping, difficulty calibration, and content fairness. This study proposes a structured, three-step framework leveraging AI to address these issues. Firstly, diverse student data—including academic performance, behavior, interaction with learning materials, demographics, psychological attributes, and feedback—is aggregated and normalized into multi-dimensional vectors. Kmeans clustering with Euclidean distance is then applied. Secondly, detailed profiles are created for each cluster by calculating their centroid, reflecting the unique characteristics and preferences of students. Finally, these profiles guide a GenAI system-ChatGPT to generate personalized quiz questions relevant to each group’s learning style and field of study. Implementing this approach with 105 business statistics students at a university in the USA, statistical tests demonstrated significant improvement in student performance on customized quizzes compared to traditional assessments. The findings underscore the transformative potential of AI-driven personalization in educational settings, promoting more effective, tailored learning experiences.