IA para estudiantes: estrategias de IA responsable para el éxito académico

Resumen

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.

Programa

• Módulo 1. Introducción al Curso
¿De qué trata este curso?
Introducción. Razones para el Tecno-Optimismo
Introducción. Polaridad Regional de Opiniones
• Módulo 2. Aspectos Psicológicos de Interactuar con la IA
Aspectos Psicológicos de Interactuar con la IA
• Módulo 3. Fundamentos de la IA
Fundamentos de Inteligencia Artificial y Aprendizaje Automático
Conocimiento, Bases de Conocimiento, Grafos de Conocimiento
• Módulo 4. IA Generativa: Modelos de Lenguaje de Gran Tamaño (LLMs)
Cómo la IA Entiende y Genera Texto
Conceptos Básicos de Promoción para Asistentes de IA Basados en Texto
• Módulo 5. Aspectos Éticos y Legales del Desarrollo y Uso de la IA
Regulación Legal de la IA
Ética y Políticas para el Uso de la IA
• Módulo 6. Generación de Imágenes Potenciada por IA
• Módulo 7. Uso de la IA en la Investigación. Parte 1
Uso de la IA en Actividades de Investigación Sin Violar Requisitos Éticos
Enfoques para Aumentar la Productividad de la Investigación
• Módulo 8. Uso de la IA en la Investigación. Parte 2
Realización de Revisiones Bibliográficas Usando Asistentes de IA
Asistentes de IA para Analizar Textos Científicos y Datos
• Módulo 9. Cómo las Redes Neuronales Ayudan a Aprender
Aspectos Aplicados del Uso de Redes Neuronales
• Examen Final

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Impartido por

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

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Materias

Artificial Intelligence