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Related Experiment Videos

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

Ru Chen1, Jin Zhang2

  • 1College of Computer Information and Engineering, Nanchang Institute of Technology, Nanchang, 330099, Jiangxi Province, China.

Scientific Reports
|March 24, 2026
PubMed
Summary

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Interface design in AI-driven digital learning systems significantly impacts user experience. Live conversational agents and adaptive feedback enhance usability, while gamification boosts engagement, guiding future educational interface development.

Area of Science:

  • Human-Computer Interaction
  • Educational Technology
  • Artificial Intelligence in Education

Background:

  • Interface Design (ID) is critical for AI-driven Digital Learning Systems (DLS).
  • Quantifying the impact of specific Interactive Interface Features (IIFs) on User Experience (UX) in DLS is underdeveloped.
  • Existing research often focuses on general educational methods, lacking specific analysis of interface elements.

Purpose of the Study:

  • To present a comprehensive model quantifying the impact of IIFs on UX within AI-driven DLS.
  • To empirically assess the influence of five key interface features on user experience.
  • To provide evidence-based guidance for designing effective AI-enhanced educational interfaces.

Main Methods:

  • Controlled experimental design with 240 participants (between-subjects).
Keywords:
Adaptive feedback mechanismsArtificial intelligenceDigital learning systemsInterface featuresPredictive accuracyUser experience

Related Experiment Videos

  • Assessment of five IIFs: Adaptive Feedback Panels (AFP), Gamification Elements (GE), live Conversational Agents (CA), Progress Visualization (PV), and Micro-Assessment Widgets (MAW).
  • Mixed-Methods Approach (MMA) combining Linear Mixed-Effect Modelling (LMEM) and Machine Learning (ML)-based Feature Selection (FS).

    • Main Results:

    • Live CA (β=5.32) and Adaptive Feedback Mechanisms (AFM) (β=4.86) significantly improved system usability (p<0.001).
    • Gamification Elements (GE) most effectively enhanced user engagement (β=0.42, p<0.001).
    • ML identified synergistic effects between CA and AF (SHAP=0.087) and achieved a predictive R² of 0.849 for composite UX.

    Conclusions:

    • A robust methodological approach for quantifying IIF impacts on UX in AI-driven DLS was developed.
    • Empirical evidence supports the significant positive influence of specific interface features on usability and engagement.
    • Findings offer crucial insights for optimizing the design of AI-enhanced educational interfaces to improve user experience.