Modelling the impact of interactive interface features on user experience in artificial intelligence driven digital learning systems

In the evolving online education system, Interface Design (ID) plays a crucial role in facilitating the application of Artificial Intelligence (AI)- driven Digital Learning Systems (DLS). While significant research has commonly analyzed educational methods, the accuracy of quantifying the impact of specific Interactive Interface Features (IIFs) on User Experience (UX) remains underdeveloped. This research presents a complete model of the impact of IIF on UX in AI-driven Digital Learning Systems (DLS). This research study employed a controlled experimental design (n = 240) with a between-subjects method to assess five key interface features: Adaptive Feedback Panels (AFP), Gamification Elements (GE), live Conversational Agents (CA), Progress Visualization (PV), and Micro-Assessment Widgets (MAW). This work designed a multi-layer model that precisely manipulated these features while maintaining experimental control. User interactions were analyzed using a Mixed-Methods Approach (MMA), combining Linear Mixed-Effect Modelling (LMEM) with Machine Learning (ML)-based Feature Selection (FS) techniques. Results show that live CA (β = 5.32, p < 0.001) and Adaptive Feedback Mechanisms (AFM) (β = 4.86, p < 0.001) had the most effective positive impact on system usability, while GE most significantly enhanced user engagement (β = 0.42, p < 0.001). The FS method revealed synergistic effects between CA and Adaptive Feedback (AF) (SHAP interaction value = 0.087). ML validated these empirical results, identifying nonlinear relationships and achieving a predictive R² of 0.849 for the composite UX score. This research developed a robust methodological approach for quantifying IIF impacts and provides empirical proof to guide the design of AI-enhanced educational interfaces that optimize learning experiences.