Study and analysis of the need for an intelligent lesson content management system for undergraduate digital technology education

Abstract

This research focuses on the development and implementation of an intelligent learning content management system (AI-LMS) designed for undergraduate digital technology education, aiming to meet user needs and improve personalized learning experiences. A mixed-method approach was used, involving the collection of data from a purposefully selected sample of students, teachers, and experts. Statistical analysis, including calculation of means and standard deviations, was used to assess system requirements and user expectations. The study identified 12 main elements for an effective AI-LMS, including ease of use, 24-hour chatbot support, online quizzes, personalized content recommendations, assessment tools, AI-driven analytics, cloud integration, and security. The results highlight the platform’s ability to support adaptive learning, enhance student engagement, and provide real-time insights to lecturers through interactive data visualizations. The AI-LMS framework enhances digital education by using artificial intelligence to enhance content delivery, assessment, and student progress tracking. The framework serves as a scalable, intelligent solution that promotes efficient and effective learning experiences. The system provides higher education institutions with a personalized, data-driven learning approach, enabling educators to improve teaching strategies while offering learners an engaging and flexible educational environment.