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AI in Education

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AI in Education



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A

  1. Algorithm: A step-by-step instruction for solving problems or tasks, often used in computer science.
  2. Artificial Intelligence (AI): Simulation of human intelligence by machines, especially computer systems.
  3. Asynchronous Learning: Use of AI tools for individual learning without all participants needing to be online at the same time.
  4. Adaptive Learning Systems: AI-based platforms that adapt to the needs and abilities of individual students.
  5. Augmented Reality (AR): Augmented reality that uses AI to integrate digital elements into the real world.
  6. Automation: Use of technologies such as AI to perform tasks without human intervention.
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B

  1. Big Data: Extremely large datasets that can be analyzed by AI to detect patterns and gain insights.
  2. Bot: An automated program that performs tasks online or within software systems.
  3. Bias in AI: Prejudices or distortions that may arise in AI systems due to unbalanced training data.
  4. Image Processing: AI technology that analyzes and interprets images, e.g., for visual recognition.
  5. Blockchain in Education: Technology that enables transparency and security in data storage.
  6. Educational AI: Systems specifically developed to support and improve the learning process.
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C

  1. Chatbots: AI-powered systems that interact with users and provide information.
  2. Cloud Computing: Provision of computing power and storage via the internet, often the foundation for AI systems.
  3. Computer Vision: Subfield of AI that enables computers to interpret visual data.
  4. Cybersecurity: Protection against digital threats, often with AI-powered systems.
  5. Curriculum Design: Use of AI to optimize curricula based on learning data.
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D

  1. Data Mining: Process of analyzing large datasets to extract patterns and knowledge.
  2. Deep Learning: Advanced AI technology using neural networks to solve problems.
  3. Digital Assistants: AI systems like Siri or Alexa that provide information and perform tasks.
  4. Data Privacy: Protection of personal data when using AI systems in education.
  5. Data Ethics: Study of moral questions related to the use of data and AI.
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E

  1. E-Learning: Learning with digital technologies, often supported by AI.
  2. AI Ethics: Study of the moral implications and responsibilities when dealing with AI.
  3. Explainable AI (XAI): AI systems whose decisions can be understood by humans.
  4. Applications of AI: Fields like education, medicine, economy, and transport where AI is used.
  5. Real-Time Data Processing: Analysis of data in real time, enabled by AI.
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F

  1. Feedback Systems: AI-based systems that provide individual feedback for learning.
  2. Fuzzy Logic: Method in AI that handles imprecise data to mimic human reasoning.
  3. Support through AI: Assistance for disadvantaged students through personalized learning offers.
  4. Formative Evaluation: Use of AI for continuous assessment and improvement of the learning process.
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G

  1. Gamification: Integration of playful elements into learning, often supported by AI.
  2. Generative AI: Systems like ChatGPT that generate content such as text, images, or videos.
  3. Facial Recognition: AI technology for identifying individuals by their faces.
  4. Global Competencies: Skills that students can develop for the future using AI-supported tools.
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H

  1. Human-in-the-Loop: Approach in which humans monitor and adjust AI systems.
  2. Holography: Use of AI to create 3D images for interactive learning environments.
  3. Blended Learning: Combination of classroom teaching and AI-supported online learning.
  4. Handwriting Recognition: AI technology that converts handwritten text into digital form.
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I

  1. Individualized Learning: Adapting learning content to the needs of individual students using AI.
  2. Internet of Things (IoT): Network of connected devices that can be controlled by AI.
  3. Interactive Learning Environments: AI-supported platforms that actively engage students in the learning process.
  4. Intelligent Tutoring Systems: AI systems that provide individual support and supervision for students.
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J

  1. Job Analysis: Use of AI to identify future job requirements.
  2. Just-in-Time Learning: Providing learning content exactly when it is needed using AI.
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K

  1. Artificial Intelligence (AI): Umbrella term for machines that simulate human-like intelligence.
  2. Collaborative Learning: Joint learning supported by AI platforms.
  3. Cognitive Load: Reduction of students' mental burden through AI-based learning aids.
  4. Knowledge Graphs: Representations of knowledge analyzed and used by AI.
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L

  1. Learning Analytics: Analysis of learning data to improve teaching.
  2. Learning Progress Monitoring: Use of AI to track and assess student progress.
  3. Learning Platforms: Online platforms that use AI to provide personalized learning content.
  4. Lifelong Learning: Promotion of continuous education through AI-supported offerings.
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M

  1. Machine Learning: Subfield of AI in which systems "learn" from data.
  2. Pattern Recognition: AI technology that identifies patterns in data, e.g., for analysis.
  3. Mental Models: Understanding of concepts supported by AI-driven visualizations.
  4. Media Literacy: Ability to use AI tools effectively and responsibly.
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N

  1. Neural Networks: Basis of many modern AI systems, inspired by the human brain.
  2. Natural Language Processing (NLP): AI technology for processing and analyzing human language.
  3. Sustainability: Use of AI to promote environmentally friendly solutions in education.
  4. AI Tutoring: Automated support for students across various subjects.
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O

  1. Open Educational Resources (OER): Free educational resources, often organized and provided by AI.
  2. Object Recognition: AI technology for identifying objects in images or videos.
  3. Curriculum Optimization: Use of AI to improve content and structure.
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P

  1. Personalization: Adapting content and methods to individual student needs through AI.
  2. Predictive Analytics: Use of AI to predict future trends or performance.
  3. Programming: Creating AI models, often using specialized programming languages.
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Q

  1. Quantum Computing: Future technology that could significantly boost AI capabilities.
  2. Quality Assurance: Use of AI to analyze and optimize educational content.
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R

  1. Robotics: Development of robots controlled by AI and used in education.
  2. Risk Management: Identifying and mitigating risks when implementing AI in schools.
  3. Recommender Systems: AI technology that recommends content based on preferences and needs.
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S

  1. Speech Recognition: AI technology for processing spoken language.
  2. Smart Classroom: Classroom equipped with AI-supported technology.
  3. Student Engagement: Promotion of active participation through AI-supported methods.
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T

  1. Text Generation: AI that automatically creates texts, e.g., for summaries.
  2. Technological Integration: Incorporation of AI into everyday teaching.
  3. Model Training: Process by which AI systems "learn" through data.
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U

  1. Supportive AI: Systems that help teachers and students to facilitate learning processes.
  2. Usability: User-friendliness of AI systems in education.
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V

  1. Virtual Reality (VR): Immersive learning environments supported by AI.
  2. Behavior Analysis: Use of AI to observe and analyze student behavior.
  3. Visualizations: Creation of data images or concepts using AI.
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W

  1. Knowledge Management: Use of AI to organize and make knowledge accessible.
  2. Wearables: AI-supported wearable devices that can assist learning.
  3. Vocabulary Expansion: Promotion of language skills through AI.
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X

  1. XAI (Explainable AI): AI systems whose decisions are transparent and understandable.
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Y

  1. YouTube Analysis: Use of AI to optimize learning content on platforms like YouTube.
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Z

  1. Future Perspectives: Potentials and challenges for the use of AI in education.
  2. Equity of Access: Ensuring equal opportunities for all students in using AI technologies.
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