Combining Large Language Models and ontologies to build a collaborative learning digital environment

This paper discusses the architecture of a novel digital system designed to support TransDisciplinary (TD) research by fostering collaborative learning and knowledge integration across disciplines. The system integrates three primary components: an ontology, an AI component, and a user interface. The ontology provides a structured framework for organizing and connecting concepts from diverse fields, facilitating the development of TD definitions. The AI component, based on Large Language Models (LLMs), interprets users’ natural language queries, retrieves relevant concepts, and highlights TD relationships. The user interface enables researchers to explore these connections, promoting deeper understanding and collaborative knowledge building. By automating the generation of ontologies and supporting natural language interactions, this system addresses challenges in TD research, such as fragmentation and inefficient communication. It also enhances legal interpretative skills by integrating insights from multiple disciplines, contributing to a more inclusive and human-centered legal system. This paper outlines the system’s architecture, discusses its components, and highlights future research directions.