ArticleScientific reports2026
Modelling the impact of interactive interface features on user experience in artificial intelligence driven digital learning systems.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.