ORIGINAL RESEARCH article
Front. Educ.
Sec. Digital Education
Volume 10 - 2025 | doi: 10.3389/feduc.2025.1617132
From Experience to Engagement: A Mixed Methods Exploration of Learning Environments Using Artificial Intelligence and Extended Reality
Provisionally accepted
- An-Najah National University, Nablus, Palestine
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Student engagement significantly influences academic success, motivating educators to explore innovative technologies to enhance learning environments. This mixed-methods study systematically investigates the impact of Artificial Intelligence, Extended Reality, and their combined usage, compared with traditional learning environments, on high school students' cognitive, emotional, behavioral, and social engagement. Utilizing Kolb's Experiential Learning Theory, this research used a convergent parallel design, combining quantitative data from 888 students who took part in rigorous non-parametric analyses and semi-structured interviews with 40 students sampled for qualitative descriptive thematic analysis. Results indicated that the AI-XR environment outperformed all other environments across all four engagement categories. Findings reinforce the synergistic opportunities for learning with AI and XR when applied in educational environments. The XR environment conceptually yielded higher levels of emotional and cognitive engagement through the immersive visualization, while the AI-environment yielded high levels of personalized adaptive learning pathways for students. Meanwhile, traditional learning environments led to the lowest engagement levels, due to their passive and non-interactive teaching nature. These findings reveal the transformative power of combining AI and XR technologies, offering essential implications for teachers, policymakers, and instructional designers committed to fostering deeper and broader student engagement in the context of digitally enriched educational systems.
Keywords: student engagement, Experiential learning, artificial intelligence, Extended Reality, mixed methods, Educational Technology
Received: 23 Apr 2025; Accepted: 23 Jun 2025.
Copyright: © 2025 Hmoud, Daher and Ayyoub. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mohammad Hmoud, An-Najah National University, Nablus, Palestine
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