# Smart System for the Retrieval of Digital Educational Content
## 6 Conclusions  
This research has presented an architecture for the retrieval of educational content from partner organizations, called AIREH (Architecture for Intelligent Retrieval of Educational content in Heterogeneous Environments). To optimize the retrieval of digital content, this work proposes a retrieval model whose core is an agent-based virtual organization architecture. Multi-agent systems are known for their ability to adapt quickly and effectively to changes in their environment. The model allows for the development of an open and flexible architecture that supports dynamic-search-related services for distributed digital content. AIREH is a multi-agent architecture that can search and integrate heterogeneous educational content through a retrieval model that uses federated search. The main novelty of the proposed architecture is that it is based on a model with dynamic and adaptive planning capabilities, making it possible to distribute tasks optimally among the different agents of the organizations, thus enables intelligent content retrieval and flexibility in a highly dynamic environment. of modeling an efficient system for managing open systems. The model and the technologies presented in this research demonstrate the advantages of using agent-based virtual organizations in the retrieval of labeled digital content. The proposed model is flexible, customizable, comprehensive and efficient The model of agent-based virtual organizations includes an organization with self-adaptive capabilities for highly dynamic environments at runtime. In conclusion, AIREH proposes a flexible architecture that allows for the development of a solution whose functionalities make it possible to manage the content stored in LO repositories. Nevertheless, this architecture can be developed further and improved through the implementation of other standards.  
The objective of this research work has been to propose a tool for the retrieval of educational content, nevertheless, the proposed solution is applicable to any other field given the popularity of labelling mechanisms for large data repositories. The semantics of the context vary in every field of application, however this only changes the labelling of resources and the information that is used to generate a case base. An example of a direct application would be Biobanks or Biorepositories because they are digital collections of metadata that give access to information on biology resources. A representative example of such Biobanks is the one supported by the European Institute of Bioinformatics with web-based solutions for genomic data.  
Conceptualization, A.B.G. and F.d.l.P.; methodology, A.B.G., F.d.l.P., and S.R.; software, A.B.G. and F.d.l.P.; validation, A.B.G., F.d.l.P., and S.R.; formal analysis, A.B.G., F.d.l.P., and S.R.; investigation, A.B.G., F.d.l.P., and S.R.; writing--original draft preparation, A.B.G. and F.d.l.P.; writing--review and editing, A.B.G. and F.d.l.P.; visualization, A.B.G. and F.d.l.P.; supervision, S.R. and J.M.C.; funding acquisition, J.M.C. and S.R.  
This work was supported by the Spanish government and European FEDER funds, project InEDGEMobility: Movilidad inteligente y sostenible soportada por Sistemas Multi-agentes y Edge Computing (RTI2018-095390-B-C32).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.