AN AI-DRIVEN FRAMEWORK FOR PERSONALIZED VISUAL E-CONTENT DEVELOPMENT USING ADVANCED IMAGE PROCESSING TECHNIQUES FOR DIGITAL LEARNING ENVIRONMENTS

Abstract [English]

In an era where digital learning platforms are rapidly expanding, there is a great need for learner-centred and visually rich educational materials that can increase the engagement of the learner, understanding of learning concepts and deepen knowledge. The present study also puts forward a framework for designing visual e-content for personalized learning based on advanced image processing techniques in digital learning environments. The system combines AI, machine learning, and image enhancement techniques to create personalized learning content tailored to individual learners' interests, learning styles, and academic achievements. Advanced image processing techniques (image segmentation, feature extraction, contrast enhancement, object recognition, intelligent visualization, etc.) are used to enhance the quality and clarity of educational images, diagrams and multimedia resources. The proposed system processes the data of users' interaction and dynamically adjusts the visual learning resources to adapt to the specific learning needs of individual users in online and hybrid teaching systems. The framework also includes automated annotation, image compression and content optimization technologies to ensure efficient storage, rapid transmission and access to information on various digital devices. The system's AI-backed personalization features help improve learner motivation, minimize cognitive overload, and facilitate inclusive education for a wide variety of learners. Experimental results show that the proposed framework has a significant improvement in the quality of content presentation, students' involvement, and learning effectiveness compared to the traditional method in developing e-contents. Moreover, with the inclusion of smart visual analytics, teachers can develop smart digital learning materials, which offer interactive and adaptable learning content with less labor and higher learning efficiency. The suggested project would help in the development of smart education technologies and the integration of artificial intelligence and image processing methods in order to create scalable, efficient, and student-centric digital learning environments that would be suitable for contemporary educational institutions and e-learning platforms.