AI for Educators: Innovative Tools Revolutionizing Education

Who is this programme for?
— Educators and researchers seeking to reduce time spent on routine tasks
— University instructors looking to diversify classes with digital tools

What does the programme 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 programme 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 programme 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