The intersection of AI and education—from personalized learning algorithms to data privacy regulations—brings both exciting opportunities and complex challenges. This glossary is designed to help educators, students, administrators, and families navigate the evolving landscape of AI in education. Whether you're new to the topic or looking to deepen your understanding, this glossary includes key concepts, technologies, and terms that are shaping the future of teaching and learning.
Algorithmic Bias: “Systematic, unwanted unfairness in how a computer detects patterns or automates decisions,” often based on characteristics and identities such as age, class, culture, disability experience, ethnicity, gender, location, nationality, political affiliation, race, religious background and practices, and/or sexuality.
Artificial Intelligence (AI): Machine-based systems designed around human-defined objectives to perform tasks that would otherwise require human or animal intelligence.
AI Literacy: Understanding what it means to learn with and about AI while gaining specific knowledge about how artificial intelligence works, the skills necessary to master AI tools, and how to critically navigate the benefits and risks of this technology.
Data Governance: A set of practices and policies to formally manage and safeguard data assets throughout a system/enterprise; roles, responsibilities, and processed are defined therein to ensure accountability for and ownership of data assets.
Deepfake: An AI-generated image, video, or audio file that convincingly replaces one person’s likeness and/or voice with another person’s.
Educators: People employed by an institution dedicated to pre-K–12 or higher education.
Generative AI: Artificial intelligence tools that generate text, images, videos, or other content based on existing data patterns and structures.
Machine Learning: A branch of artificial intelligence that uses algorithms to enable computers to learn and make predictions by identifying patterns in data without being explicitly programmed.
Natural Language: Language that has developed through human or animal interaction rather than being constructed, such as with computer code; AI systems that use natural language processing are able to understand this type of language.
Ransomware: When cybercriminals block access to an institution’s computer system until a ransom is paid.
Transparency: Open disclosure of how AI systems work, including how they reach decisions and the data used to do so.
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