Algorithm
A set of step-by-step instructions that a computer follows to complete a task.
Algorithmic Bias
This refers to the potential for AI systems to perpetuate or amplify existing biases due to biased training data or flawed algorithms, resulting in unfair or discriminatory outcomes.
Artificial intelligence (AI)
The ability of a computer or machine to mimic human intelligence (e.g., learn, reason, solve problems).
AI Ethics
Guidelines for developing and using AI in a responsible and ethical way, ensuring fairness, safety, and respect for everyone.
AI Literacy
Involves the knowledge, skills, and attitudes necessary to interact with AI in a safe and effective way. This includes understanding how AI works, recognizing its potential benefits and risks, evaluating AI systems for bias and fairness, and developing the skills to use AI tools effectively and critically in teaching and learning responsibly.
AI Model
A computer program trained on a dataset to recognize patterns and perform specific tasks.
AI Safety
Measures taken to ensure AI systems are used in ways that prevent harm to individuals or society. This can encompass data privacy, bias mitigation, and responsible development.
AI Tool
AI-powered software that can automate or assist users with a variety of tasks (e.g., AI-powered writing assistants, tutoring programs, or assessment tools).
Data
Information, such as facts, numbers, and text, that is used to train AI systems.
Dataset
A large collection of organized information (like text, images, or numbers) used to train an AI model.
Data Privacy
Protecting the personally identifiable information (PII) of students, teachers, and families when using digital tools, including AI tools.
Digital Citizenship
Responsible and ethical use of technology, encompassing online safety, privacy, critical thinking, and respectful interactions in the digital world.
Hallucination
Any inaccurate or misleading output from an AI tool. These can be presented as facts by the AI tool, further elevating the need to properly vet the outputs before using them more broadly.
Human-in-the-Loop
An understanding that humans should always be involved in the AI process, providing guidance, feedback, or making final decisions to ensure the AI is used responsibly and effectively.