AI Literacy
What Is AI Literacy?
The knowledge and skills needed to understand, use, evaluate, and critically engage with AI technologies in everyday life and professional settings. As AI becomes ubiquitous in education, AI literacy is increasingly recognized as an essential competency for both students and educators.
Learn More
Related Terms in Policy & Ethics
AI Ethics
The study and application of moral principles to the design, development, and deployment of AI systems to ensure they are fair, transparent, and beneficial. In education, AI ethics encompasses concerns about bias in algorithms, student data privacy, equitable access, and the responsible use of AI in teaching and assessment.
Algorithmic Bias
Systematic and unfair discrimination that occurs when an AI system produces results that are prejudiced due to flawed assumptions in the training data or model design. In education, algorithmic bias can lead to inequitable outcomes in automated grading, student risk predictions, and content recommendation systems.
Data Privacy
The right of individuals to control how their personal information is collected, used, stored, and shared by organizations and technology systems. Data privacy is a critical concern in educational AI, as student data including learning behaviors, performance metrics, and personal information must be protected under regulations like FERPA and COPPA.
FERPA
The Family Educational Rights and Privacy Act, a U.S. federal law that protects the privacy of student education records and gives parents certain rights regarding their children's educational information. FERPA compliance is a key consideration when schools adopt AI tools that collect or process student data.
COPPA
The Children's Online Privacy Protection Act, a U.S. federal law that regulates the collection of personal information from children under 13 by online services and websites. EdTech companies and AI tool providers must comply with COPPA when their products are used in K-12 settings with young learners.
Academic Integrity
The ethical commitment to honesty, trust, fairness, respect, responsibility, and courage in academic work and scholarship. The rise of generative AI has prompted educators and institutions to rethink academic integrity policies, balancing the productive use of AI tools with the prevention of dishonest practices.