AUTOMATED LECTURE NOTE-TAKING SYSTEM

Background. In the modern world, information technologies evolve rapidly, constantly altering our approaches to learning, work, and daily life. One significant aspect of this evolution is the automation of various processes, including education. Students and teachers are faced with large volumes of information that need to be processed, stored, and used in the educational process. Taking lecture notes is still an important task that requires a lot of time and effort, thus automating this process is both relevant and necessary. Automated lecture note-taking based on video and audio materials greatly facilitates the lives of students and teachers by providing quick access to structured information. The use of speech recognition and artificial intelligence technologies to create notes from lecture materials opens up new opportunities for effective learning. These systems can significantly save time, improve the quality and accuracy of notes, and ensure their accessibility to all participants in the educational process. These systems can not only create notes but also structure them by highlighting key points and providing easy access to information. This allows students to focus on understanding and comprehending the material rather than writing it down, thereby improving the quality of learning and knowledge acquisition. Objective. The purpose of the paper is to simplify the note-taking process and improve its quality by developing a system for automated lecture note-taking based on video and audio materials, ensuring the efficient and rapid creation of structured notes from lecture materials. Methods. Analysis of Literature and Contemporary Studies: Studying scientific articles, monographs, and dissertations related to the topic of automated lecture note-taking, speech recognition, and artificial intelligence. System Analysis: Defining system requirements, analysing possible approaches and tools for implementation. Experimental Method: Developing, implementing, and testing the system. Comparative Analysis: Evaluating the effectiveness of different speech recognition tools and AI models for creating notes. Modelling and Prototyping: Creating a system prototype, testing it, and improving it based on the obtained results. Results. During the implementation of the automated lecture note-taking system, an effective Telegram bot was created, which uses "whisper-1" and "gpt-4" models to provide high-quality speech recognition and the generation of structured notes from video and audio materials. Conclusions. The developed system of automated note-taking of lectures based on video and audio materials significantly simplifies the preparation of materials for students and teachers. Integration with Telegram and implementation of the system through a Telegram bot ensure cross-platform, accessibility and ease of use and at the same time provide an opportunity to avoid creating additional web or mobile applications for a wide range of users. The use of OpenAI's "whisper-1" model demonstrates high accuracy of speech recognition, which allowed improving the quality of transcriptions compared to other tools such as Vosk or FasterWhisper.