The terms below provide you definitions of common terms and acronyms used in the context of AI.
|
Term |
Definition or examples |
|---|---|
| Artificial Intelligence (AI) | Technology that allows machines to mimic human intelligence—like understanding language, recognizing images, or making decisions. |
| Agentic AI | AI systems that can operate autonomously by setting goals, planning multi-step actions, and executing with minimal human intervention. |
| Algorithm | A set of instructions or rules that a computer follows to solve a problem or perform a task. |
| Chatbot | A program that simulates human conversation, often used to answer questions or provide support. ChatGPT is one example. |
| Deep Learning | A type of machine learning that uses multi-layered neural networks to automatically learn complex patterns from large amounts of data. |
| Ethical AI | The practice of designing and using AI in ways that are fair, transparent, and respect privacy, equity, and human rights. |
| Generative AI | AI that can create new and original content, such as, text, images, audio, or videos, rather than just analyzing and classifying existing data. |
| GPT (Generative Pre-trained Transformer) | A family of AI models based on the Transformer architecture, designed to generate human-like text. "Pre-trained" means the model is first training on large text datasets before being used. |
| Large Language Model (LLM) | A type of AI trained on massive text datasets to understand and generate human-like language. |
| Language Model | A system that understands and generates text based on patterns it has learned from language data. |
| Learning Analytics | The use of AI to track, analyze, and support student learning based on data from online platforms or course tools. |
| Machine Learning | A subset of AI where computers learn from data and improve their performance over time without being explicitly programmed. |
| Model | In AI, a model is the trained system that captures patterns from data and can perform tasks such as translating languages, recognizing images, or generating text. |
| Model training | The process of teaching a machine learning model to make accurate predictions by feeding it large amounts of data, helping it learn from its errors and adjusting it so it can better recognize patterns and make more accurate predictions on new data. |
| Prompt | The input or question you give to an AI like ChatGPT to get a response. |
| Prompt Engineering | The skill of crafting effective prompts to get better or more accurate responses from AI tools. |
| Responsible AI use | Using AI in ways that align with university policies, uphold academic values, and support student learning and equity. |
| Training data | Information used to teach an AI model on how to perform its tasks. |
| Transparency | A principle of ethical AI that emphasizes making the workings of AI systems understandable—such as what data was used, how decisions are made, and what the limitations are. |
| Use case | Describes how a user interacts with a system to accomplish a particular task. Example, two use cases for using ChatGPT would be to “summarize articles” and “help with brainstorming”. |
