Exploring Adaptive, Adaptable, Mixed, and Static Arabic Rule-Based Chatbot Effects on Usability, Learning Success, and Engagement

Personalized e-learning emphasizes a learner-centered instruction approach by adapting educational content to individual needs. This study empirically evaluates three personalization methods (adaptive, adaptable, and mixed) alongside a non-personalized static method, implemented within Moalemy, an Arabic rule-based educational chatbot. A 4 × 3 within-subjects experiment involving 52 students across three levels of task difficulty (easy, medium, and hard) was performed to examine usability, learner engagement, and learning outcomes as key indicators of user experience in educational chatbots. Results showed no statistically significant differences in learning gain or relative learning gain across the four methods, indicating comparable effectiveness in supporting learning success. Engagement differed significantly by method, with the adaptable approach obtaining the highest scores. Descriptive usability results further suggested that the adaptable method achieved numerically higher scores in SUS, efficiency, and effectiveness under lower task difficulty, while static and adaptive showed comparatively stronger tendencies at higher levels. However, these patterns represent exploratory trends rather than statistically confirmed performance advantages. This study provides a controlled comparison of system-driven, learner-driven, shared-control, and non-personalization strategies in an Arabic e-learning context, offering empirical insights into how different learning methods influence usability, learning success, and engagement within Arabic educational chatbots.