Dise\u00f1o y desarrollo de un entorno educativo interactivo que combina Deep Learning (DL) y Realidad Extendida (XR)

Background: Theglobaltransition toward digital education has exposed critical limitations in traditional engineering pedagogy, particularly in teaching complex abstract concepts like electrical circuits. Existing technological solutions often operate in isolation, failing to leverage the synergistic potential of artificial intelligence and immersive technologies. Objective: This study develops and empirically validates an innovative hybrid framework integrating Deep Learning (DL) for personalized adaptive instruction with Extended Reality (XR) for immersive simulation in electrical engineering education. Methods: A comprehensive mixed-methods approach was employed using a quasi-experimental pretest-posttest control group design with 20 undergraduate engineering students (10 experimental, 10 control), equally divided by gender (10 male, 10 female). This pilot study is projected to be followed by a larger-scale implementation with 100 students (50 male, 50 female). The technological implementation combined TensorFlow\/PyTorch-based adaptive learning algorithms with Unity3D XR environments deployed on Oculus Quest 3 headsets. Quantitative measures included academic performance assessments, knowledge retention tests, engagement metrics, and user experience questionnaires (UEQ, SUS), while qualitative data encompassed semi-structured interviews and focus groups. Results: Theexperimental group demonstrated statistically significant improvements across all measured outcomes: 35.2% higher post-test scores (ANCOVA F(1,17)=18.92, p\u00a10.001, \u03b72=0.31, Cohen\u2019s d=1.28), 42% increased engagement metrics (\u03c72(1)=9.78, p=0.002, \u03d5=0.49), and superior knowledge retention at 6-week follow-up (t(18)=4.12, 1 p\u00a10.001, d=0.92). User experience scores exceeded excellence thresholds (SUS=85.4, UEQ\u00bf+1.5 across dimensions). Qualitative analysis revealed enhanced conceptual understanding, increased confidence, and strong appreciation for personalized learning pathways. Conclusion: The integrated DL-XR framework represents a transformative approach to engineering education, effectively bridging theoretical abstraction and practical application through adaptive personalization and immersive experimentation. This pilot research establishes a promising model for technology-enhanced STEM education, with significant implications for curriculum design and institutional implementation, warranting further investigation in the planned larger-scale study. Keywords: Deep Learning, Extended Reality, Adaptive Learning, Engineering Education, Electrical Circuits, Immersive Learning, Educational Technology, Personalized Instruction