AI for Students: Responsible AI Strategies for Academic Success

Who is this programme 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 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 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 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.

• 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.