Modern technologies are penetrating the field of education, transforming traditional teaching methods and making the work of specialists easier. The introduction of AI-based solutions, such as smart chatbots, helps improve educational processes, for example, creating developmental programs without lengthy development. center accredited by the Russian Ministry of Education decided to use chatbots. To bring their ideas to life, specialists turned to MCN Telecom, and this is what came out of it. Client Features Our client is an educational organization operating throughout the country in close cooperation with the Ministry of Education. The educational and methodological center specializes in organizing multifunctional educational environments in schools and preschool institutions, produces physical education complexes and construction sets aimed at developing spatial thinking in children, develops educational programs and conducts seminars for teachers.
The organization has accumulated a significant laos telegram data volume of methodological materials and manuals that require systematization and convenient access for teachers. At the same time, there is a clear request from the center's clients for support and service after the implementation of educational products. Finding a solution To solve the problems of the educational center, the option of creating a smart chatbot was considered, which would help the center’s teacher-clients in planning lessons and providing information. For the basis of the chatbot, our specialists suggested using the center’s educational and methodological materials with processing according to the following scheme. The materials are broken down into parts that data scientists call chunks. Each such “piece” of text is converted into a numerical vector that reflects its meaning.
All digitalized information is collected in a specialized database, where it is grouped by subject area. When a user enters a text query, it is also converted into a numeric vector. The system compares this vector with the database and finds the most relevant "piece" of data. This information is then added to the user's query and fed into the GPT language model. This way, the model gets not only the user's query, but also additional context, allowing it to provide more accurate and useful answers. The choice of this technology was due to its ability to efficiently process large volumes of data and provide relevant answers to user queries. With its help, it was possible to implement the following chatbot use cases.