As teachers and school leaders, you are likely aware of the increasing interest in artificial intelligence (AI) and its potential to transform education. However, the technical terms and concepts related to AI can be complex and overwhelming. In this post, we’ll explain some of the most common technical terms related to AI in a way that is accessible and relevant to educators.


AI: Artificial Intelligence

At its core, artificial intelligence (AI) refers to machines that can perform tasks that would normally require human intelligence, such as learning, problem-solving, reasoning, and decision-making. In education, AI can be used to personalize learning, support student assessment, and provide real-time feedback to students.

Machine Learning

Machine learning is a type of AI that involves training machines to learn from data, without being explicitly programmed. This means that machine learning algorithms can improve their performance over time by analyzing and adapting to new data. In education, machine learning can be used to provide adaptive learning experiences that adjust to the needs and abilities of individual students.

Deep Learning

Deep learning is a type of machine learning that uses deep neural networks to analyze complex data, such as images, speech, and natural language, and make predictions or decisions based on that data. In education, deep learning can be used to analyze student data, identify learning gaps, and provide targeted interventions.

Neural Networks

Neural networks are a type of artificial neural network that mimics the structure and function of the human brain. They consist of interconnected nodes, or “neurons,” that work together to process information and make predictions or decisions based on that information. In education, neural networks can be used for natural language processing, speech recognition, and computer vision.


An algorithm is a step-by-step procedure or set of rules designed to solve a specific problem or accomplish a particular task. It is a fundamental concept in computer science and plays a crucial role in the field of artificial intelligence by acting as the backbone of intelligent systems. They enable machines to process vast amounts of data, learn from patterns, make decisions, and perform tasks with precision and efficiency.

Natural Language Processing

Natural language processing (NLP) is a subfield of AI that focuses on analyzing, understanding, and generating human language. NLP algorithms can be used to analyze and categorize text data, translate between languages, and even generate human-like responses in chatbots and virtual assistants. In education, NLP can be used to analyze student writing, provide feedback on grammar and syntax, and assist with language translation.

Chat GPT In Action

In a talk from the cutting edge of technology, OpenAI cofounder Greg Brockman explores the underlying design principles of ChatGPT and demos some mind-blowing, unreleased plug-ins for the chatbot that sent shockwaves across the world. After the talk, head of TED Chris Anderson joins Brockman to dig into the timeline of ChatGPT’s development and get Brockman’s take on the risks, raised by many in the tech industry and beyond, of releasing such a powerful tool into the world.

Strategies for Getting Started

Dive in and give it a try!

Jumping into using AI in the classroom may seem daunting at first, but it’s important to remember that the best way to learn is often by doing. Don’t be afraid to dive in and start experimenting with different AI tools and resources for yourself. By getting hands-on experience, you’ll be able to better understand how these technologies can support your teaching and learning goals, as well as any challenges you may encounter along the way. Just like with any new technology, it’s important to start small, set realistic goals, and be patient with the learning process. Remember, every small step forward is a step toward integrating AI in a way that best benefits your students.

When it Comes to Prompting, Be Creative

When it comes to working with AI engines, one of the most important things is to be creative with your prompts. There is no one-size-fits-all approach to prompting, and it’s important to experiment with different strategies and techniques to see what works best for you and your students. By being creative and open-minded, you can unlock the full potential of AI in the classroom and create truly engaging and personalized learning experiences for your students. Throughout this blog series, we will explore some of the best strategies for prompting AI engines, as well as some creative ideas for how to use them in your teaching practice. So let’s dive in and get started!

Here are some general strategies to use when prompting an AI engine:

  1. Be specific: Provide clear and concise prompts that give the AI engine a clear understanding of what you are looking for. Avoid vague or overly general prompts.
  2. Use natural language: Many AI engines are designed to understand and respond to natural language. Use complete sentences and avoid using jargon or overly technical terms.
  3. Use examples: Providing examples can help the AI engine better understand what you are looking for. If possible, provide multiple examples to give the engine a better sense of the range of possible responses.
  4. Test and iterate: Experiment with different prompts and evaluate the results. Refine your prompts based on the feedback you receive to improve the accuracy and relevance of the responses.
  5. Be patient: AI engines are not perfect and may require multiple attempts to provide the desired response. Give the engine time to process your prompt and provide a response before assuming that it is not working.

It is becoming increasingly important to understand the key terms and concepts related to AI to effectively integrate these technologies into the classroom. By using AI tools like machine learning, deep learning, and natural language processing, teachers can create personalized and engaging learning experiences for their students. However, it’s equally important to be creative with prompting AI engines and try different strategies to see what works best for you and your students. Remember to be patient and experiment with different prompts, using clear and concise language, examples, and testing and iterating to refine your approach. With these strategies and a willingness to try new things, educators can embrace the potential of AI in education and create a better learning experience for their students.

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