Who is this course for?
Teachers of foreign languages who want to get acquainted with the use of language models for creating educational materials, reviewing written papers and organizing interactive learning.
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 foreign language teachers:
— Automation of routine: we generate tests and exercises, create educational materials using AI and organize educational activities.
— Creation of visual materials for classes.
— Examples of the use of AI in teaching that you can repeat.
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 neural networks.
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 prompt design basics).
Ethical dilemmas and legal aspects related to AI use in academia.
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 (literature search, source analysis, visualization of connections).
Use AI to automate routine tasks (structuring information, data extraction, test generation, grading assignments, designing course elements).
Generate personalized learning materials and assignments using AI.
Create interactive and creative learning/work formats with AI.
Assess the feasibility and effectiveness of specific AI tools for solving educational or research tasks.
Possess:
Skills in effectively using generative conversational assistants for tasks such as: creating personalized assignments and programs, generating learning materials, organizing interactive learning, and testing language skills.
Proficiency in research tools for analyzing scientific literature and supporting academic research.
Skills in using AI-powered educational platforms to optimize teaching practices.
Methods for integrating AI tools into education, considering their capabilities, limitations, and ethical norms.
An approach to using neural networks as a practical tool while understanding their boundaries.
