Artificial Intelligence Driven Adaptive Multimedia Framework for Real-Time Personalized Learning in Remote Education
DOI:
https://doi.org/10.54536/ijmdt.v1i1.6685Keywords:
AI, Adaptive Learning, E-learning, Multimedia, Remote Education, Real-Time PersonalizationAbstract
The rapid shift to remote education has highlighted the need for personalized, engaging, and effective e-learning systems. Traditional online learning platforms often provide static content, which limits learner engagement and reduces learning outcomes. This study proposes an AI-driven adaptive multimedia framework capable of real-time personalization based on multimodal learner data, including facial expressions, speech patterns, and interaction behaviors. Using a quasi-experimental design, 150 students were randomly assigned to experimental (adaptive system) and control (traditional e-learning) groups. Results indicate that the AI-driven system significantly improved post-test scores (35.6% vs. 13.6%), knowledge retention (31.4% improvement), and engagement metrics. Qualitative feedback from students and instructors corroborated the system’s effectiveness. These findings demonstrate the potential of AI-driven adaptive multimedia frameworks to transform remote learning, offering scalable, personalized, and evidence-based solutions for diverse educational contexts.
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Copyright (c) 2026 Kamal Singh Kunwar (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
