Experiments report

This project explored various AI experiments using different natural language processing (NLP) models to inform the development of Retrieval-Augmented Generation (RAG) systems for the AI assistant, the AI tutor, and the recommendation engine. The experiments provided valuable insights that guided the selection of the most effective approaches for these applications.

This document explores methodologies, technologies, and AI-driven solutions for exploiting course content in a digital environment. We show how the ESCO standard (European Skills, Competences, Qualifications, and Occupations) can be used to map courses to skills, and by integrating advanced semantic matching and embedding models, we explore state-of-the-art technologies for enhancing educational resources and learner outcomes.

The structure of this document is designed to guide readers from foundational concepts to advanced AI applications. We begin by introducing the key concepts and data sources used, including insights into the ESCO ontology. We follow by exploring mechanisms of semantic matching and the innovative approaches employed to recommend and integrate ESCO skills into course classification.

The last section is dedicated to the design and implementation of different proofs-of-concept, detailing the architecture, data processing pipeline, and the critical choices made in embedding model selection. These choices inform a range of AI applications, from course content search and annotation to generative question-answering and hybrid retrieval systems.

Finally, conclusions and recommendations are presented, summarising the most important findings and focusing on practical deployment.