The Impact of AI, XR, and Combined AI-XR on Student Satisfaction: A Moderated Mediation Analysis of Engagement and Learner Characteristics | Zendy

Mohammad Hmoud | Zendy; Wajeeh Daher | Zendy; Abedalkarim Ayyoub | Zendy

Open Access

The Impact of AI, XR, and Combined AI-XR on Student Satisfaction: A Moderated Mediation Analysis of Engagement and Learner Characteristics

Author(s) -

Mohammad Hmoud,

Wajeeh Daher,

Abedalkarim Ayyoub

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3597239

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Emerging educational technologies such as Artificial Intelligence (AI) and Extended Reality (XR) promise to transform traditional classrooms into dynamic, personalized, and immersive learning environments. Yet, the potential of combining these innovative technologies and understanding their nuanced impacts on learners remains underexplored. This study addresses this gap by examining how AI, XR, and a combined AI-XR approach influence student satisfaction, mediated by student engagement and moderated by individual learner characteristics. Utilizing a cluster-randomized experimental design with 888 high school students, the research compares traditional teaching methods against AI-enhanced, XR-enhanced, and combined AI-XR instructional settings. Grounded in Kolb’s Experiential Learning Theory and Self-Determination Theory, findings reveal that student engagement significantly mediates the relationship between advanced learning environments and student satisfaction, with the combined AI-XR condition demonstrating the most potent effects. Further analysis highlights the moderating role of learner characteristics: students with high technological proficiency and male students show heightened engagement and satisfaction in immersive XR-based contexts, whereas AI-based personalized support notably benefits learners with lower initial motivation. Self-efficacy showed no significant moderating effect. The study underscores engagement as a critical mechanism for achieving positive outcomes through innovative educational technology and emphasizes the importance of designing instructional experiences that account for diverse learner profiles. The research offers valuable in-sights for educators and instructional designers seeking to harness AI and XR technologies effectively to enhance learner experiences in secondary education.

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