The AI Dictionary for Educators

Even if you consider yourself a AI guru, this might be worth a bookmark.

Sometimes, all you need is an AI dictionary made for educators.

Last year, when I transitioned from the classroom to working in edtech, I thought I was tech-savvy. After all, I had a master’s degree in computer science, was a STEM teacher for almost a decade, and generally had a techy disposition.

Enter, hubris.

2023 has been something else. The changes happening in the AI space are extraordinary and the implications are rather unknown. And frankly, I get confused all the time. I have to stop, read things through, break things down, and sometimes even corner my AI-engineering colleagues to repeat themselves until I understand.

So a realization struck me. If I, with my above-average-technical educational background, felt confused, what about the teachers who never were exposed to this kind of language? Because while I may have transitioned from being a teacher to working at an AI company, my passion for education and technology remains unwavering.

To bridge this knowledge gap, I decided to make a quick little dictionary of AI terms specifically tailored for educators worldwide. Initially created for my own benefit, I’ve reworked my own AI Dictionary for Educators and expanded it to help my fellow teachers embrace the advancements AI brings to education.

Adaptive Learning

AI-powered systems that adjust the learning experience based on individual student needs, allowing for personalized instruction and targeted interventions.

AI Curriculum

The inclusion of AI-related topics, skills, and concepts in educational curricula, aiming to prepare students for the AI-driven future.

AI Ethics

(Also see “Ethical AI”): The study of ethical considerations and guidelines for the responsible development and use of AI, including topics such as bias, transparency, accountability, and privacy.

AI Integration

The process of incorporating AI tools, applications, or methodologies into educational settings to enhance teaching and learning experiences.

AI Literacy

The understanding of basic AI concepts, principles, and applications to effectively engage with and critically evaluate AI technologies.


A set of step-by-step instructions or rules followed by a computer program to perform a specific task or solve a problem.

Artificial General Intelligence (AGI)

Highly autonomous systems or machines possess the ability to understand, learn, and apply knowledge across a wide range of tasks and domains, similar to the general intelligence exhibited by humans. (Unlike narrow AI systems that are designed to excel at specific tasks.)

Artificial Intelligence (AI)

Utilizing advanced algorithms to enable machines to simulate human intelligence and perform tasks autonomously.

Augmented Reality (AR)

AR technology overlays virtual elements onto the real-world environment, offering opportunities for immersive and interactive learning experiences.


The use of technology, including AI, to automate repetitive tasks or processes, freeing up time for more complex or creative work.


(also known as “Algorithmic Bias” or “Machine Bias”) The potential for AI systems to produce or reinforce biased outcomes or decisions based on the data they are trained on. Understanding this concept is crucial for promoting fairness and equity in AI applications.

Big Data

Large volumes of data are collected and analyzed to gain insights and make informed decisions.


A computer program that can converse with humans, typically through messaging platforms or websites. E.g., ChatGPT.


A variant of OpenAI’s GPT (Generative Pre-trained Transformer) language model specifically designed for interactive conversations.

Data Preprocessing

The process of cleaning, transforming, and organizing data before it can be used for AI analysis or modeling.

Data Privacy

Understanding the ethical considerations and best practices for handling and protecting student data in AI-driven educational environments.

Deep Learning

A subset of machine learning that uses artificial neural networks to process and understand complex patterns and data.


The intersection of education and technology, encompassing the use of digital tools, platforms, and applications to enhance teaching and learning experiences.

Ethical AI

The responsible and ethical implementation of AI technologies in education, considering factors such as privacy, data security, consent, and student well-being.


The ability of an AI system to provide clear explanations or justifications for its decisions or recommendations, allowing users to understand the underlying reasoning.

Expert System

AI software that emulates the decision-making ability of a human expert in a specific field.

Facial Recognition

Technology that analyzes and identifies individuals based on their unique facial features.

Feature Engineering

The process of selecting, transforming, or creating input variables (features) from raw data to improve the performance and interpretability of AI models.

Genetic Algorithms

Algorithms based on principles of genetics that are used to efficiently and quickly find solutions to complex problems.

Humanoid Robot

A robot designed to resemble and interact with humans, often with human-like features and movements.

Internet of Things (IoT)

The network of physical devices, vehicles, and appliances embedded with sensors, software, and connectivity to exchange data and interact with each other.

Job Automation

The use of AI and robotics to replace or augment human labor in various industries and tasks.

Knowledge Graph

A knowledge base that organizes information in a structured format, allowing AI systems to understand relationships between different concepts.

Large Language Models (LLMs):

Advanced AI models, such as OpenAI’s GPT-3, that are trained on massive amounts of text data and can generate human-like text based on prompts or queries.

Learning Analytics

Applying analytics techniques to educational data to gain insights into student performance, engagement, and learning behaviors, helping educators make data-informed decisions.

Machine Learning

The process of training machines to learn from data and improve their performance without being explicitly programmed.

Machine Vision

The ability of AI systems to interpret and understand visual information, enabling applications such as image recognition, object detection, or visual data analysis.

Model Evaluation

The process of assessing the performance and effectiveness of an AI model using various metrics and techniques, ensuring its reliability and suitability for the intended task.

Natural Language Processing (NLP)

AI technology that enables computers to understand, interpret, and generate human language.

Neural Network

A computational model inspired by the structure and function of the human brain, used in deep learning to process and analyze data.


An artificial intelligence research organization that develops advanced AI technologies and tools, including ChatGPT.

Optical Character Recognition (OCR)

Technology that recognizes and extracts text from scanned documents or images.

Personalized Assessment

Leveraging AI to create tailored assessments that adapt to students’ knowledge levels and provide targeted feedback, allowing for more accurate measurement of student understanding.

Predictive Analytics

Using historical data and statistical algorithms to predict future events or outcomes.

Privacy-Preserving AI

Techniques and practices that aim to protect individuals’ privacy while utilizing AI technologies, including methods such as differential privacy or secure multi-party computation.

Quantum Computing

A cutting-edge technology that leverages principles of quantum physics to perform computations exponentially faster than traditional computers.


The branch of AI that deals with designing, developing, and operating robots for various applications.

Speech Recognition

AI technology that converts spoken language into text, enabling applications such as automated transcription, language learning, or voice-controlled interfaces.

Sentiment Analysis

AI techniques used to analyze and interpret the emotions, attitudes, and opinions expressed in text or speech.

Social and Emotional Learning (SEL)

The integration of AI technologies to support the development of social and emotional skills in students, fostering well-being and resilience.

Student Data Analytics:

Leveraging AI and data analysis techniques to gain insights into student performance, engagement, and learning patterns, facilitating data-informed decision-making.

Supervised Learning

A type of machine learning where AI models are trained using labeled examples, with input-output pairs, to learn patterns and make predictions on new, unseen data.

Turing Test

A test to determine if a machine exhibits behavior indistinguishable from that of a human, evaluating its ability to mimic human intelligence.

Unsupervised Learning

A type of machine learning where algorithms learn patterns and relationships in data without labeled examples or guidance.

Virtual Assistant

AI-powered software that provides assistance and performs tasks based on voice commands or text input, such as Siri or Alexa.

Virtual Classroom

An online learning environment that replicates key aspects of a physical classroom, enabling remote teaching and learning experiences through AI-powered platforms.

Virtual Reality (VR)

Immersive VR experiences that transport students to virtual environments, facilitating immersive simulations, virtual field trips, and experiential learning.

Wearable Technology

Devices worn on the body, often equipped with sensors and AI capabilities, to track health and fitness or provide personalized information.

Yield Optimization

AI techniques used in manufacturing or agriculture to maximize production efficiency and output.

Zero-Shot Learning

A machine learning approach where models can generalize and make predictions for classes they have yet to be explicitly trained on.

Cut through the jargon, and keep this AI Dictionary for Educators handy.

Rather than fearing the rise of AI in classrooms, we can leverage its potential as a valuable assistant. AI alone cannot replace human teachers, but when harnessed effectively, it allows educators to personalize learning experiences for their students—an aspiration often challenging to achieve amidst large class sizes of dozens. The thing is though, while AI did add a level of complexity to our lives, it also ironically adds simplicity. But only if we understand it. I hope this AI Dictionary for Educators does just that.

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