Who is this course for?
— Students who want to learn artificial intelligence for academic purposes and master working with text- and visual-based neural network tools
— Prospective university students aiming to prepare for exams and higher education
What does the course include?
Core Module:
— How neural networks work: from machine learning to text generation
— Avoiding pitfalls: why AI makes mistakes and how to spot them
— Ethical dilemmas: copyright issues and academic integrity
— Prompt engineering workshop: learning to communicate with neural networks
Specialized module for students:
— Automating Routine Tasks: generate tests, create summaries, and streamline your study process.
— Practical AI Applications in Education: ready-to-use examples you can implement in your own learning.
The course features interviews with practitioners sharing real-world experiences of implementing AI technologies in education.
What will you learn?
Effective AI collaboration:
— Distinguish reliable results from erroneous conclusions
— Apply specialized platforms for research and text analysis
Automate routine work:
— Delegate standard (and non-standard) tasks to AI
— Structure information and extract data
Create innovatively:
— Generate personalized assignments
— Make the mundane exciting through interactive formats
Master key tools:
— Generative AI Assistants: DeepSeek, Perplexity, Qwen, Mistral, YandexGPT, GigaChat, Neuro, LLM Arena.ru
— Academic Research Tools: Scite.ai, Undermine, Litmaps, Research Rabbit
— AI-Powered Education Platforms: Twee, Brisk Teaching, Magic School
You’ll master neural networks not as a trend, but as a practical tool—with full awareness of their capabilities, pitfalls, and ethical boundaries.
The course is taught online and includes recorded lectures, tests, and additional materials.
Upon completing the course, participants will:
Know:
The fundamental principles of how neural networks operate.
The capabilities and key application areas of AI technologies in education.
The limitations and potential risks of using AI.
The main categories and examples of modern AI tools for education.
The principles of effective interaction with AI systems (including the basics of prompt design).
Ethical dilemmas and legal aspects related to AI use in academic settings.
Be able to:
Critically evaluate AI-generated results: distinguish reliable information from erroneous conclusions.
Formulate effective prompts to solve various educational tasks using generative assistants.
Apply specialized AI tools for research activities (literature search, source analysis, visualization of connections).
Use AI to automate routine tasks (structuring information, data extraction, test generation).
Analyze the feasibility and effectiveness of specific AI tools for solving given educational or research tasks.
Possess:
Skills in effectively interacting with generative conversational assistants for tasks such as creating summaries, overcoming procrastination, completing creative assignments, and organizing the learning process.
Proficiency in using research tools for analyzing scientific literature and supporting academic research.
