Artificial Intelligence (AI)
Artificial intelligence, or AI, is expected to have a transformative impact on the future of education globally. Educators and students will require enhanced skills to identify and navigate the use of these AI systems. Educators need to prepare students for changing workplaces, including teaching them how to use emerging technologies.
This webpage provides an overview of foundational AI terms and concepts, high-level discussion questions regarding AI in education to consider at the Local Education Agency (LEA) leadership and classroom educator levels, resources to learn more about AI, resources for educators to teach their students about AI, as well as links to further research and reports regarding AI.
Foundational AI Terms and Concepts
Term/Concept | Definition | Example |
---|---|---|
Artificial Intelligence | Artificial Intelligence, or AI, refers to the development of computer systems that can perform tasks that typically require human intelligence, such as recognizing patterns, making decisions, and learning from experience. | Examples of AI include facial recognition, autonomous vehicles, text prediction, digital voice assistants, and many other applications. |
Machine Learning | Machine learning is a type of artificial intelligence that involves training algorithms to make predictions or decisions based on data. | Examples of AI systems that use machine learning are facial recognition, product and entertainment recommendations, email spam filtering, and predictive text. |
Deep Learning | Deep learning is a subset of machine learning that involves training multiple layers of interconnected nodes in an artificial neural network on large datasets to identify complex patterns and relationships in the data. | Examples of AI systems that use deep learning are self-driving cars, medical diagnosis applications, chatbots, and customer service bots. |
Data and Training Data | Within the context of AI, “data” are units of information about people or objects that are used by AI systems. “Training data” refers to large amounts of data that has been labeled/coded and is used to teach AI models or machine learning algorithms to make the most appropriate decisions to the best of its abilities. | In an AI system for a self-driving car, the training data will include images and videos labeled to identify cars versus street signs versus people. |
Generative AI | A type of narrow or weak AI that can generate new content, such as text or images, in response to a prompt. | Examples of generative AI systems include AI chatbots such as OpenAI’s ChatGPT and Google’s Gemini, Github’s programming AI system Copilot, and art creator Midjourney. |
AI System Classifications
Classification | Definition | Example |
---|---|---|
Weak or Narrow AI | AI systems that are designed to perform specific tasks or solve specific problems, rather than having a broad range of intelligence. | Examples of weak or narrow AI systems include digital voice assistants, predictive analytics, chatbots, and delivery bots. |
Strong or General AI | Strong AI, also known as artificial general intelligence (AGI) or general AI, is a type of theoretical AI system that would have intelligence and decision-making skills equal to humans. It would have a self-aware consciousness that could solve problems, learn, plan, and adapt independently. | There are currently no AI systems that can match human intelligence, but there are some instances of Strong AI systems in the form of research projects or hypothetical scenarios. |
Disclaimer
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