Introducing a Semantic Model for Smart Composition of E-Learning Content

This paper presents a comprehensive survey of expert opinions on integrating semantic Artificial Intelligence (AI) in digital education, focusing on the conversion and composition of Learning Objects (LOs) to support diverse educational contexts. It highlights the role of semantic computing in bridging the semantic gap between human cognitive processes and machine operations, facilitating more intuitive and context-aware human-computer interactions. Through structured qualitative interviews with experts, the study explores the interoperability and modularity of LOs as defined by the IEEE Learning Object Metadata (LOM) Standard, categorizing them into Action and Object classes to better align with instructional purposes. The findings underscore the importance of semantic AI in enhancing educational content’s personalization and adaptability, demonstrating its potential to transform digital learning through more effective, scalable, and human-centered educational tools. This research provides insights into the evolving role of semantic AI in education, emphasizing its capacity to enhance learning experiences by enabling the meaningful collaboration of educational content authors and AI technologies.