AI in Education
A
- Algorithm: A step-by-step instruction for solving problems or tasks, often used in computer science.
- Artificial Intelligence (AI): Simulation of human intelligence by machines, especially computer systems.
- Asynchronous Learning: Use of AI tools for individual learning without all participants needing to be online at the same time.
- Adaptive Learning Systems: AI-based platforms that adapt to the needs and abilities of individual students.
- Augmented Reality (AR): Augmented reality that uses AI to integrate digital elements into the real world.
- Automation: Use of technologies such as AI to perform tasks without human intervention.
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B
- Big Data: Extremely large datasets that can be analyzed by AI to detect patterns and gain insights.
- Bot: An automated program that performs tasks online or within software systems.
- Bias in AI: Prejudices or distortions that may arise in AI systems due to unbalanced training data.
- Image Processing: AI technology that analyzes and interprets images, e.g., for visual recognition.
- Blockchain in Education: Technology that enables transparency and security in data storage.
- Educational AI: Systems specifically developed to support and improve the learning process.
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C
- Chatbots: AI-powered systems that interact with users and provide information.
- Cloud Computing: Provision of computing power and storage via the internet, often the foundation for AI systems.
- Computer Vision: Subfield of AI that enables computers to interpret visual data.
- Cybersecurity: Protection against digital threats, often with AI-powered systems.
- Curriculum Design: Use of AI to optimize curricula based on learning data.
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D
- Data Mining: Process of analyzing large datasets to extract patterns and knowledge.
- Deep Learning: Advanced AI technology using neural networks to solve problems.
- Digital Assistants: AI systems like Siri or Alexa that provide information and perform tasks.
- Data Privacy: Protection of personal data when using AI systems in education.
- Data Ethics: Study of moral questions related to the use of data and AI.
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E
F
- Feedback Systems: AI-based systems that provide individual feedback for learning.
- Fuzzy Logic: Method in AI that handles imprecise data to mimic human reasoning.
- Support through AI: Assistance for disadvantaged students through personalized learning offers.
- Formative Evaluation: Use of AI for continuous assessment and improvement of the learning process.
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G
- Gamification: Integration of playful elements into learning, often supported by AI.
- Generative AI: Systems like ChatGPT that generate content such as text, images, or videos.
- Facial Recognition: AI technology for identifying individuals by their faces.
- Global Competencies: Skills that students can develop for the future using AI-supported tools.
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H
- Human-in-the-Loop: Approach in which humans monitor and adjust AI systems.
- Holography: Use of AI to create 3D images for interactive learning environments.
- Blended Learning: Combination of classroom teaching and AI-supported online learning.
- Handwriting Recognition: AI technology that converts handwritten text into digital form.
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I
J
K
L
M
N
O
P
- Personalization: Adapting content and methods to individual student needs through AI.
- Predictive Analytics: Use of AI to predict future trends or performance.
- Programming: Creating AI models, often using specialized programming languages.
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Q
R
- Robotics: Development of robots controlled by AI and used in education.
- Risk Management: Identifying and mitigating risks when implementing AI in schools.
- Recommender Systems: AI technology that recommends content based on preferences and needs.
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S
T
U
- Supportive AI: Systems that help teachers and students to facilitate learning processes.
- Usability: User-friendliness of AI systems in education.
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V
W
X
Y
- YouTube Analysis: Use of AI to optimize learning content on platforms like YouTube.
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Z
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