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@article{173344,
        author = {A. Uma Maheswari},
        title = {Augmented Adaptive Learning Theory (AALT): A New Paradigm for AI-Driven Education},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3-9},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173344},
        abstract = {The rapid advancement of Artificial Intelligence (AI), Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) is transforming education by facilitating personalized learning, adaptive content delivery, and immersive experiences. However, existing learning theories, Behaviorism, Cognitivism, Constructivism, and Connectivism fail to comprehensively address the cognitive, ethical, and adaptive challenges associated with AI-driven education. Issues such as cognitive overload in immersive environments, algorithmic bias, and the absence of human-AI collaboration frameworks necessitate the development of a structured learning model. This study introduces the Augmented Adaptive Learning Theory (AALT) as an integrated theoretical framework designed to bridge these gaps by synthesizing principles from established pedagogical theories with AI-driven innovations.  AALT is structured around four key components: AI-Augmented Personalized Learning (AILP), Adaptive Cognitive Load Regulation in AR/VR (VRCLM), AI-Enhanced Human Collaboration (AIHC), and Bias-Free Ethical AI Governance (BFAI). By integrating these elements, AALT ensures that AI functions as a learning facilitator rather than a replacement for traditional education. The study proposes an empirical validation framework utilizing Structural Equation Modeling (SEM), cognitive load analysis, and machine learning analytics to assess the impact of AI-enhanced learning on student engagement, knowledge retention, and ethical AI implementation.  The findings of this study contribute to advancing AI pedagogy by ensuring that AI, AR, VR, and MR technologies enhance learning outcomes while preserving human-centered instructional integrity. The proposed framework provides actionable insights for educators, policymakers, and AI developers, ensuring that AI-powered educational tools remain transparent, equitable, and pedagogically sound. Future research should focus on longitudinal studies, cross-cultural validation, and the integration of Explainable AI (XAI) models to further refine AI-driven learning environments.},
        keywords = {Adaptive Learning, AI in Education, Augmented Reality, Cognitive Load Management, Ethical AI Governance, Virtual Reality.},
        month = {February},
        }