In times of increasingly personalized educational content, designing a data-driven platform which offers the opportunity to create content for different use cases is arguably the only solution to handle the massive amount of information. Therefore, we developed the software "Machina Callida" (MC) in our project CALLIDUS (Computer-Aided Language Learning: Vocabulary Acquisition in Latin using Corpus-based Methods) which is funded by the German Research Foundation. The main focus of this research project is to optimize the vocabulary acquisition of Latin by using a data-driven language learning approach for creating exercises. To achieve that goal, we were facing problems concerning the quality of externally curated research data (e.g. annotated text corpora) while curating educational materials ourselves (e.g. predefined sequences of exercises). Besides, we needed to build an interface which would be user-friendly both for teachers and students. While teachers would like to create an exercise or test and use them (even as printed out copies) in class, students would like to learn on the fly and right away.
As a result we offer a repository, a file exporter for various formats and, above all, interactive exercises so that learners are actively engaged in the learning process. In this paper we show the workflow of our software and explain the architecture focusing on the integration of Articial Intelligence (AI) and data curation. Ideally, we want to use AI technology to facilitate the process and increase the quality of content creation, dissemination and personalization for our end users.