This chapter presents a recommender system for non-traditional educational resources, such as software, events, and experts, developed within the iTEC project. The system, called the iTEC Scenario Development Environment (SDE), is designed to assist teachers in selecting the most suitable educational resources for classroom activities. The recommender is based on an ontology developed collaboratively by experts and uses semantic web technologies to provide personalized recommendations. It considers contextual factors when calculating the relevance of resources and integrates with third-party applications through an API. The SDE also includes a knowledge base enriched with information from both human users and software agents performing web scraping. The system has been tested with teachers in Spain, England, and Finland, and has shown promising results in enhancing educational authoring tools with semantic web-based recommendations. The chapter discusses the development of the SDE, the use of multi-criteria decision analysis, context-aware recommendations, and semantic technologies. It also presents two successful client applications, the Composer and AREA, that integrate SDE recommendations. The system has been evaluated with end-users, and the results indicate that recommendations on non-traditional educational resources can foster innovation in the classroom. The main contributions of the research include the development of a semantic model for the iTEC project, the integration of external sources through enrichment techniques, and the use of a flexible approach to ontology design. The chapter concludes that the SDE provides a valuable tool for educators to select appropriate resources for learning activities, and that the system's API is publicly available for integration with other applications.This chapter presents a recommender system for non-traditional educational resources, such as software, events, and experts, developed within the iTEC project. The system, called the iTEC Scenario Development Environment (SDE), is designed to assist teachers in selecting the most suitable educational resources for classroom activities. The recommender is based on an ontology developed collaboratively by experts and uses semantic web technologies to provide personalized recommendations. It considers contextual factors when calculating the relevance of resources and integrates with third-party applications through an API. The SDE also includes a knowledge base enriched with information from both human users and software agents performing web scraping. The system has been tested with teachers in Spain, England, and Finland, and has shown promising results in enhancing educational authoring tools with semantic web-based recommendations. The chapter discusses the development of the SDE, the use of multi-criteria decision analysis, context-aware recommendations, and semantic technologies. It also presents two successful client applications, the Composer and AREA, that integrate SDE recommendations. The system has been evaluated with end-users, and the results indicate that recommendations on non-traditional educational resources can foster innovation in the classroom. The main contributions of the research include the development of a semantic model for the iTEC project, the integration of external sources through enrichment techniques, and the use of a flexible approach to ontology design. The chapter concludes that the SDE provides a valuable tool for educators to select appropriate resources for learning activities, and that the system's API is publicly available for integration with other applications.