Who is this course for?
— Educators and researchers seeking to reduce time spent on routine tasks
— University instructors looking to diversify classes with digital tools
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 university educators & researchers:
— Automating routine tasks: grading assignments, generating tests, designing courses
— Replicable examples of AI integration in the classroom
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
Principles of effective interaction with AI systems (including the basics of prompt design)
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 activities (literature search, source analysis, visualization of connections)
Use AI to automate routine tasks (structuring information, data extraction, test generation, grading standard assignments, course element design)
Generate personalized learning materials and assignments with AI
Create interactive and creative learning/working formats using AI
Assess the feasibility and effectiveness of specific AI tools for solving given educational or research tasks
Possess:
Skills in effective interaction with generative conversational assistants for tasks such as: automated grading, test and assignment generation, course design, and research optimization
Proficiency in using research tools for analyzing scientific literature and supporting academic research
Competence in working with AI-powered educational platforms to optimize teaching practices
Methods for integrating AI tools into the educational process, considering their capabilities, limitations, and ethical norms
An approach to using neural networks as a practical tool while being aware of their boundaries
