Development of a neural network-based learning course generation system

1.

Development of a neural
network-based learning
course generation system
In this presentation, we will examine the process of developing a system
that generates learning courses based on neural networks. The aim of the
thesis is to create a bot that will allow users to receive personalised
learning programmes that meet their individual needs. We will examine the
structure of the bot in detail, the process of selecting the appropriate
neural network, the stages of creating the bot itself, and the methods of
evaluating the quality of the final result. The work presented is aimed at
members of the diploma commission and students interested in neural
networks and bot creation.
Dmitry Zheleznyak 4221

2.

Bot Structure: The Foundation of Personalised
Learning
The first step in developing the system is to create a clear bot structure. The structure has already been developed and includes the
following key elements:
Request Input Module: The user formulates their learning needs.
Request Processing Module: The neural network analyses the request and identifies key topics.
Content Generation Module: Based on the analysis, learning material is generated.
Content Presentation Module: The created course is provided to the user in a convenient format.
This structure allows the bot to respond flexibly to various requests and create courses that best match the user's interests and
level of preparation.

3.

Choosing a Neural Network: A Key Element of Quality
Selecting the right neural network is a critical step. I am currently in the testing phase, evaluating several neural networks to determine the optimal one.
Various queries will be used, corresponding to the developed structure. The quality of the results generated by each neural network will be assessed by
experts and potential users.
The evaluation will focus on the relevance of the content to the query, the logical structure, and the presence of up-to-date and useful information. Based
on the collected data, the winner will be chosen - the neural network that provides the best quality for the generated learning courses.

4.

Creating a Telegram Bot: Practical Implementation
The bot will be implemented in Telegram, chosen due to its widespread use and convenient API. To create the bot, the following is
required:
Develop an interaction interface: Provide an intuitive way for users to input requests and receive results.
Integrate the selected neural network: Establish a connection between the bot and the neural network to process requests and
generate content.
Implement editing capabilities: Allow users to refine their requests and clarify the results.
The development process will involve the use of specialized libraries and frameworks for creating Telegram bots.

5.

Quality Assurance: Evaluation and Improvements
After the development is completed, a thorough quality check of the system will be conducted. Experts from various fields will be
involved as specialists who will evaluate the bot based on the following criteria:
Completeness and relevance of the content.
Logical flow and structure of the course.
Ease of use of the bot.
Alignment of results with requests.

6.

Additional Features: Expanding Functionality
To expand the bot's functionality, the plan is to implement additional features:
Support for multiple languages: Will allow users to access courses in different languages.
Interactive elements: Adding tests, quizzes, and other interactive elements to improve the absorption of the material.
Personalised recommendations: The bot will suggest courses that match the user's interests based on an analysis of their previous requests and
learning outcomes.

7.

System Advantages: Personalised Approach for Each
User
The developed system has a number of advantages that make it attractive to users:
Personalised approach: Each course is created taking into account the individual needs of the user.
Time saving: The user does not need to spend time searching for and structuring information.
Ease of use: The bot is available in Telegram, which allows learning at any time and in any place.

8.

Conclusions and Next Steps
As a result of completing the thesis, a system will be created that generates personalised training courses based on neural networks. The
Telegram bot will become a convenient and effective tool for acquiring knowledge and skills.
Further plans include expanding the bot's functionality, integrating new neural networks, and conducting additional research to improve
the quality of the generated courses. Thank you for your attention!