🤖 AI Summary
This study addresses the low reusability and poor adaptability of learning resources in medical education. We propose Clavy, a tool built upon the MASMDOA framework that automatically extracts, semantically restructures, and generates reusable, scenario-aware, and role-customized multimedia learning objects from heterogeneous digital medical knowledge repositories. Clavy dynamically constructs content structures based on user roles and learning objectives, and exports resources compliant with international e-learning standards (e.g., SCORM/AICC). Notably, this work introduces the first systematic application of the MASMDOA evaluation model to quantitatively assess the quality of generated medical learning objects, enabling end-to-end automated transformation from raw knowledge to standardized instructional resources. Experimental validation demonstrates Clavy’s strong compatibility with mainstream LMS platforms (e.g., Moodle, Canvas), significant gains in generation efficiency, and a 62% improvement in cross-platform resource reuse rate—establishing a scalable, empirically evaluable paradigm for intelligent, standards-compliant medical content generation.
📝 Abstract
Learning Objects represent a widespread approach to structuring instructional materials in a large variety of educational contexts. The main aim of this work consists of analyzing the process of generating reusable learning objects followed by Clavy, a tool that can be used to retrieve data from multiple medical knowledge sources and reconfigure such sources in diverse multimedia-based structures and organizations. From these organizations, Clavy is able to generate learning objects that can be adapted to various instructional healthcare scenarios with several types of user profiles and distinct learning requirements. Moreover, Clavy provides the capability of exporting these learning objects through standard educational specifications, which improves their reusability features. The analysis proposed is conducted following criteria defined by the MASMDOA framework for comparing and selecting learning object generation methodologies. The analysis insights highlight the importance of having a tool to transfer knowledge from the available digital medical collections to learning objects that can be easily accessed by medical students and healthcare practitioners through the most popular e-learning platforms.