AI for Students: Responsible AI Strategies for Academic Success（学生的人工智能：助力学业成功的负责任的人工智能策略）

Overview

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 educationWhat 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 networksSpecialized 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 analysisAutomate routine work:

— Delegate standard (and non-standard) tasks to AI— Structure information and extract dataCreate innovatively:

— Generate personalized assignments— Make the mundane exciting through interactive formatsMaster 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 SchoolYou’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.

Syllabus

• Module 1. Course Introduction
What Is This Course About?
Introduction. Reasons for Techno-Optimism
Introduction. Regional Polarity of Opinions
• Module 2. Psychological Aspects of Interacting with AI
Psychological Aspects of Interacting with AI
• Module 3. AI Fundamentals
Fundamentals of Artificial Intelligence and Machine Learning
Knowledge, Knowledge Bases, Knowledge Graphs
• Module 4. Generative AI: Large Language Models (LLMs)
How AI Understands and Generates Text
Basics of Prompting for Text-Based AI Assistants
• Module 5. Ethics and Legal Aspects of AI Development and Use
Legal Regulation of AI
Ethics and Policies for AI Use
• Module 7. Using AI in Research. Part 1
Using AI in Research Activities Without Violating Ethical Requirements
Approaches to Boosting Research Productivity
• Module 8. Using AI in Research. Part 2
Conducting Literature Reviews Using AI Assistants
AI Assistants for Analyzing Scientific Texts and Data
• Module 9. How Neural Networks Help You Learn
Applied Aspects of Using Neural Networks
• Final Exam

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Taught by

Anna N. Sytnik , Tatyana A. Gavrilova, Sergey Yu. Sevryukov, and Aleksandra K. Bordunos

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Subjects

Artificial Intelligence