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AI terms + education: A glossary of what you need to know

Audrey Campbell
Audrey Campbell






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With all this talk about Artificial Intelligence (AI), let's make sure we understand the terms being used in education and mainstream media. Additionally, let’s agree that many of these phrases are changing in real-time as the technology itself matures, which means what we define today as one thing may very well be another in six months.

Here at Turnitin, we are working closely with students, educators, and innovators to comprehend the impact of AI-generated text on learning environments. Very quickly, we’re seeing how AI writing tools are being utilized meaningfully in the classroom, as well as situations in which they may be misused. AI paraphrasing tools, for example, are a subset of AI writing tools that impact learning because they are a shortcut solution that prevent students from learning the valuable skill of paraphrasing.

And while AI is a constantly evolving space, it is crucial that we prioritize conversation and understanding on this journey. The more we can talk about these elements as they relate to the education landscape, the greater students and educators can understand the benefits, challenges, and promise of this moment.

The following is a list of important terms that you need to know. And while it is certainly an incomplete list, it is an essential first step in establishing awareness and knowhow.

Artificial Intelligence (AI): Any intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. These machines use mathematical models that identify and encode patterns in data sets, which can then perform predictions on new situations which they haven’t encountered before.

AI-assisted writing: When artificial intelligence software utilizes existing content to predict, modify or create text based on input that a user supplies it. Certain tools may create novel bodies of text, while others may reword existing text in the case of AI paraphrasing tools.

AI-generated text: Text created by artificial intelligence based on vast amounts of data of existing content from the internet.

AI paraphrasing: AI paraphrasing refers to the use of AI techniques to rephrase or rewrite a given piece of text in a way that preserves the original meaning of the text while using different words and phrases.

Algorithm: A set of instructions or computations that a machine follows in order to learn how to do a particular task.

Autonomous: When something–in this case, a machine–can perform a task without human interference or intervention.

Chatbots (or “bots”): A program designed to help human users with simple tasks and communicate via voice or text commands in order to feel like a human-to-human conversation.

ChatGPT: ChatGPT (short for Chat Generative Pre-trained Transformer) is a chatbot launched by OpenAI in November 2022. It is a Large Language Model with both supervised and reinforcement learning techniques. ChatGPT can produce a body of unique text from a user’s specific input based on existing content from the internet.

Cognitive science: The broader form of study that includes AI in addition to philosophy, linguistics, psychology, neuroscience, and anthropology. All of these together seek to understand how the mind functions and, when applied to AI, how machines can simulate human thought and action.

DALL-E 1: Launched by OpenAI in January 2021, it’s an AI that generates images based on text and belongs to a family of AI called Diffusion Models. In 2022, it was superseded by DALL-E 2.

Dataset: Related data points in a collection, usually with tags (labels) and a uniform order.

Deep Learning: A family of Artificial Intelligence architectures that uses neural networks to encode information, resulting in state-of-the-art performance across a wide array of tasks. Generative AI models are typically examples of Deep Learning.

Foundation model: Models that are trained on a broad set of unlabeled data that can be used for different tasks, with minimal fine-tuning.

General AI: Sometimes referred to as “strong AI” (although not entirely equivalent), general AI is artificial intelligence that could successfully do any intellectual task that can be done by a human being.

Generative AI: Generative AI is a broad label that's used to describe any type of artificial intelligence that uses learning algorithms to create new digital images, video, audio, text, or code.

Large Language Model (LLM): Artificial intelligence that has been trained on massive quantities of text data to produce human-like responses to natural language inputs.

Machine intelligence: An overarching term for different types of learning algorithms, including machine learning and deep learning.

Machine learning: A subset of AI that is specifically focused on developing algorithms that will help machines to learn and change in response to new data, without the help of a human being.

Natural Language Processing (NLP): Technology that allows machines to determine–via text or by voice–what humans are saying.

Open AI: An Artificial General Intelligence (AGI) research and deployment company based in San Francisco, California (USA), seeking to develop and build AGI that benefits all of humanity. Their most recent releases include ChatGPT and DALL-E 2.

Reinforcement learning: A machine learning training method based on rewarding desired behaviors and/or punishing undesired ones.

Strong AI: This field of research is focused on developing AI that is equal to the human mind when it comes to ability. General AI is a similar term often used interchangeably.

Supervised learning: An approach to creating artificial intelligence where a computer algorithm is trained on input data that has been labeled for a particular output.

Text spinners: Also known as AI paraphrasing tools, these are software tools that are used to automatically rewrite or rephrase a given piece of text in order to generate multiple variations of the original text.

Turing Test: A test that examines a machine’ s ability to pass for a human, specifically in language and behavior. A machine can pass this test if, after being graded by a human, its performance is indistinguishable from that of human participants. This test is named after Alan Turing (1912-1954), a notable English mathematician, computer scientist, and logician.

Unsupervised learning: The use of AI algorithms to identify patterns in data sets containing data points that are neither classified nor labeled.

There are a variety of resources available to students and educators as they learn to navigate AI in the classroom, including this collection of educator-developed Turnitin assets for approaching academic integrity in the age of AI. Additionally, keeping up with innovation in the news cycle, staying on top of AI developments in education, and diving into how organizations, including Turnitin, are leveraging AI technology to support student learning outcomes, are all helpful ways to stay connected and informed during this rapidly-shifting time.