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Débute 8 June 2026 09:25

Se termine 8 June 2026

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L'IA pour les éducateurs : des outils innovants qui révolutionnent l'éducation

Explorez les outils d'IA qui révolutionnent l'éducation : maîtrisez l'ingénierie des invites, automatisez la notation, générez des devoirs et intégrez des plateformes comme DeepSeek et Scite.ai tout en naviguant dans les aspects éthiques et légaux.
Saint Petersburg State University via XuetangX

Saint Petersburg State University

344 Cours


Not Specified

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Free Online Course

Amélioration optionnelle disponible

Aperçu

Who is this course for?— Educators and researchers seeking to reduce time spent on routine tasks— University instructors looking to diversify classes with digital toolsWhat 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 university educators & researchers:

— Automating routine tasks:

grading assignments, generating tests, designing courses— Replicable examples of AI integration in the classroomThe 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 operateThe capabilities and key application areas of AI technologies in educationThe limitations and potential risks of using AIThe main categories and examples of modern AI tools for educationPrinciples of effective interaction with AI systems (including the basics of prompt design)Ethical dilemmas and legal aspects related to AI use in academiaBe able to:

Critically evaluate AI-generated results:

distinguish reliable information from erroneous conclusionsFormulate effective prompts to solve various educational tasks using generative assistantsApply 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 AICreate interactive and creative learning/working formats using AIAssess the feasibility and effectiveness of specific AI tools for solving given educational or research tasksPossess:

Skills in effective interaction with generative conversational assistants for tasks such as:

automated grading, test and assignment generation, course design, and research optimizationProficiency in using research tools for analyzing scientific literature and supporting academic researchCompetence in working with AI-powered educational platforms to optimize teaching practicesMethods for integrating AI tools into the educational process, considering their capabilities, limitations, and ethical normsAn approach to using neural networks as a practical tool while being aware of their boundaries

Programme

  • Module 1. Introduction au cours
  • De quoi parle ce cours ?
    Introduction. Raisons pour le techno-optimisme
    Introduction. Polarité régionale des opinions
  • Module 2. Aspects psychologiques de l'interaction avec l'IA
  • Aspects psychologiques de l'interaction avec l'IA
  • Module 3. Fondamentaux de l'IA
  • Fondamentaux de l'intelligence artificielle et de l'apprentissage automatique
    Connaissance, bases de connaissances, graphes de connaissances
  • Module 4. IA générative : modèles de langage de grande taille (LLM)
  • Comment l'IA comprend et génère du texte
    Bases de la sollicitation pour les assistants IA basés sur le texte
  • Module 5. Aspects éthiques et juridiques du développement et de l'utilisation de l'IA
  • Régulation juridique de l'IA
    Éthique et politiques d'utilisation de l'IA
  • Module 7. Utiliser l'IA dans la recherche. Partie 1
  • Utiliser l'IA dans les activités de recherche sans violer les exigences éthiques
    Approches pour booster la productivité de la recherche
  • Module 8. Utiliser l'IA dans la recherche. Partie 2
  • Réalisation de revues de littérature à l'aide d'assistants IA
    Assistants IA pour l'analyse de textes scientifiques et de données
  • Module 9. Comment les réseaux neuronaux soutiennent les éducateurs
  • Préparation de cours et conception de supports
    Exemples d'utilisation de l'IA en classe que vous pouvez reproduire
  • Examen final

Enseigné par

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


Matières

Artificial Intelligence