Multilingual Health Education

In order to provide the support for efficient and correct knowledge acquisition in different languages, we suggest using a virtual assistant - driven by deep learning models. This system automates processes associated with knowledge retrieval and recommendation entirely allowing users to get relevant and personalized insights with less effort. Using data recognition, feature extraction and hybrid recommendations, the assistant works through the user needs, the pattern of learning and the context to make recommendations. The combination of both collaborative and content-based filtering techniques is integrated in the hybrid recommendation engine for generating personalized learning contents, articles and educational resources. It starts off identifying learning gaps, predicts the potential challenges that the user will face, and suggests the targeted content that will help improve the learning experience of the user. The system also carries out proactive data management and is aware of the user’s behavior and tries to prevent the data from being leaked for protection on user information. The virtual assistant shows experimental results having accuracy range as 88–96%, which exactly makes highly precise predictions and recommendation. With the help of metamaterials and deep learning the system improves both content quality and recommendation’s reliability. However, this technology provides multilingual solution, having language detection, speech-to-text and real time translation capabilities enabled and this makes it accessible to a larger audience. In the end, virtual assistant reduces acquisition of knowledge to being more efficient, personalized and user friendly in different educational and informational domains.