Revolutionizing Primary School History Subject through the Development of an AI-Powered Quizizz Module

Abstract: The integration of Artificial Intelligence (AI) in education has transformed the landscape of teaching and learning by enabling automation, personalization, and adaptive learning experiences. However, teaching the History subject in primary schools continues to face challenges including low student engagement, difficulty in content retention, and limited use of interactive teaching materials. Existing platforms like Quizizz promote engagement through gamification but lack intelligent features that can automatically generate adaptive, curriculum-based questions. This study aimed to develop and evaluate an AI-powered Quizizz module designed to support History teachers in creating interactive and adaptive quizzes aligned with primary school curriculum. The research employed the ADDIE instructional design model, encompassing analysis, design, development, implementation, and evaluation phases. During the evaluation phase, content and interface validity were assessed by three History teachers and three multimedia lecturers, respectively. For effectiveness testing, 30 primary school History teachers participated in a pre-test and post-test design using a five-point Likert scale. Data were analyzed using a paired sample t-test to compare teachers’ perceptions before and after using the module. Results showed significant improvements across all four dimensions: perceived effectiveness, content quality, student engagement, and teaching support (p < .001). The findings indicate that the AI-powered Quizizz module effectively enhanced teaching quality, increased student motivation, and reduced teachers’ workload in quiz preparation. The study concludes that integrating AI features within gamified learning platforms can revolutionize History education by promoting interactive, data-driven learning environments. Future work should involve large-scale testing with students to measure learning outcomes directly and the integration of advanced AI features such as automated feedback, question difficulty prediction, and performance analytics to further enhance adaptive learning.