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This glossary defines some Artificial Intelligence terms to familiarize teachers and interested parties with various digital educational resources, providing valuable information about their socialization.
Adaptive Learning
Adaptive learning employs data-driven instruction to adjust each student’s learning experiences in terms of difficulty, pace, etc. This type of learning tracks various data, such as progress, engagement, and performance to design and implement personalized learning experiences.
AIED refers to using AI in the educational field (Artificial Intelligence in Education). It has taken on four prevalent roles: Intelligent tutor, mentee, learning tool/peer, and policy-making advisor.
AI Literacy
This is the capacity to understand, interact with, and use AI effectively and ethically within a wide range of socio-cultural contexts. It implies knowing its operation and principles.
It can be said that it is the set of competencies that allows for evaluating, communicating, and collaborating effectively and critically with this technology.
Algorithm
The algorithms are a series of instructions for performing calculations, operations, or processes, i.e., a step-by-step procedure to complete a task.
Its operation begins with gathering training data, which helps it learn to perfect itself. Some algorithms learn by themselves (machine learning); others require a programmer to carry out this process.
Therefore, a complex set of algorithms is the driver of a functioning AI.
Artificial Intelligence
Artificial intelligence is the computer science branch responsible for replicating the thinking and decision-making capabilities of the human brain. AI systems are responsible for performing complex tasks, which require algorithms and data to function. These systems can be rule-based or machine-learning algorithms.
Several authors point out that AI is volatile and constantly evolving so as time passes, new definitions for the term “AI” will adjust this concept to the corresponding advances in the technology.
Bias
Bias refers to systemic and repeatable errors in an algorithm that produce prejudices or inclinations, resulting in discrimination in favor of specific groups over others.
Chatbot
A chatbot functions via a program that simulates a human conversation with the user. It is important to emphasize that not all chatbots use AI, but it is now more common for these to integrate AI technology.
Chatbots are very relevant in customer service, electronic commerce (e-commerce), and areas that require a virtual assistant.
Computer Vision
Computer Vision is a field of artificial intelligence that allows systems to analyze visual content to make decisions or recommendations based on that content.
Data
Units of information that have been transformed, so that they can be processed and transferred efficiently.
Deep Learning
Deep learning is a subset of machine learning, using deep neural networks to learn from data. This type of learning is inspired by the human brain’s data processing. It also recognizes complex patterns (texts, sounds, images, etc.) to analyze information and make concrete predictions.
Deep learning functionality can be found in voice-activated TV controls, generative AI, fraud detection, product recommendations, autonomous vehicles, chatbots, and facial recognition, among others.
Ethical AI
It is responsible for studying and responding to the ethical and social dilemmas associated with the design, development, and implementation of AI.
Generative AI
Generative artificial intelligence or Gen AI is an AI system capable of generating original content, whether text, audio, image, code, etc., using machine learning models (deep learning models) to understand patterns and subsequently generate new data.
Integration
Integration occurs when a program or product synchronizes with AI capabilities and benefits to improve various aspects like functionality, performance, customization, or security. Therefore, the program/product and AI should not be seen as two separate tools but as an embodiment of both.
Intelligence Augmentation
Intelligence Augmentation or Augmented AI focuses on developing technology to improve human beings’ cognitive abilities, without replacing them.
Large Language Models
Large language models (LLM) are systems trained with large amounts of data that can generate content in natural language. Examples of this technology are ChatGPT, Gemini, and Google Translate.
Learning Analytics
Learning analytics is an emerging field in education with a multidisciplinary dimension because it integrates computer science, educational sciences, statistics, data mining, pedagogy, and behavioral sciences. In other words, it uses data to understand and improve teaching-learning processes.
Its objectives include supporting instructional strategies, identifying at-risk students to provide effective interventions, and improving learning experiences by tracking activities and feedback. It is used in virtual reality (VR), augmented reality (AR), and intelligent assistants, among others.
Machine Learning
Machine learning is a subfield of AI focused on developing computer algorithms and processes to learn from data to make decisions or predictions.
Some types of machine learning are supervised, unsupervised, and reinforced. Machine learning allows AI systems to learn and develop algorithms by identifying rules and patterns in the data (which they perform on their own). It requires training with massive amounts of data to make proper, informed decisions.
Natural Language Processing
Natural Language Processing (NLP) is an area of AI and computational linguistics that examines the interactions of human language so that the NLP systems can recognize and understand human language to perform an infinite number of tasks, like translation or responding to voice commands (virtual assistants such as Alexa), among others.
Neural Network
Also known as artificial neural network (ANN) or simulated neural network (SNN), it is a machine learning model inspired by the functioning of the human brain. The neural networks, although complex, mimic the functioning of neurons. Therefore, they require training with data to function correctly for learning and improving accuracy.
Artificial neurons are arranged in layers to form a network. The three types of layers: are input, hidden (processing), and output.
Prompt
A prompt is a word, phrase, or set of instructions (input) given to an AI system to generate a response.
Smart Tutoring System
Also known as an Intelligent Tutoring System (ITS), it is an AI program that simulates a tutor/teacher to provide a personalized experience. It can be used for various scenarios in education, such as monitoring, advising, providing feedback, and simulating learning environments.
Strong AI
Strong artificial intelligence or artificial general intelligence (AGI) is a theoretical form that aims to develop mental capacities and functions that mimic the human brain. Although it is still a theoretical concept, it would have the ability to learn and reason, easily adapt to new situations, and understand various mental states.
Teaching about AI
This refers to the use and application of knowledge about AI to develop tools, programs, and other educational needs, for example, curricula that integrate programming and robotics in the classroom.
Teaching for AI
It refers to developing knowledge, skills, and competencies to use AI responsibly and efficiently (related to AI literacy). It can be implemented at any educational level and is necessary for students to fully develop in today’s world.
Furthermore, it entails increasing critical thinking skills, problem-solving, an ethical understanding of AI, and basic technology concepts, among others.
Teaching with AI
Implies integrating AI in education to drive teaching-learning processes.
Weak AI
Weak artificial intelligence (or narrow) applies machine learning and natural language processing techniques and algorithms to perform very specific, automated tasks; they have limited capacity beyond the specified range of tasks.
It focuses on defined tasks, such as pattern identification and image recognition. It can be found in various applications like chatbots, autonomous vehicles, and others.
Artificial intelligence is an emerging and volatile field, so some of the terms may undergo modifications over time. However, this current useful guide of the key terminology will allow teachers to identify and understand more about this technology in education.
Translated by Daniel Wetta
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This article from Observatory of the Institute for the Future of Education may be shared under the terms of the license CC BY-NC-SA 4.0 
