Ethics, Governance, and Policy

This section covers responsible AI use, critical for administrators and policy creators.

The moral principles and guidelines specifically governing AI use in educational settings. This includes protecting student privacy, ensuring equitable access, preventing academic dishonesty, maintaining human relationships in teaching, and prioritizing genuine learning over metrics.

Developing and deploying AI ethically with consideration for societal impact. In schools, responsible AI means protecting student privacy, ensuring equitable access, preventing misuse, and maintaining focus on genuine learning outcomes.

Systematic errors or unfair preferences in AI systems resulting from biased training data or flawed algorithms. In education, bias can affect grading, recommendations, and learning opportunities, making it critical to ensure AI treats all students equitably.

Measures ensuring AI systems don't cause harm to users or society. For schools, AI safety includes content filtering, age-appropriate responses, prevention of harmful advice, protection from manipulation, and maintaining student wellbeing.

Protection of personal educational information from unauthorized access or misuse. With AI systems handling sensitive data, schools must ensure compliance with FERPA, COPPA, and other regulations while maintaining trust with families.

Clear communication about what data is collected, how it's used, and who has access. Schools must maintain data transparency to build trust with students and parents while complying with privacy regulations.

The framework of policies, procedures, and oversight mechanisms for managing AI use in organizations. School districts need AI governance to ensure responsible deployment, protect student data, maintain educational quality, and comply with regulations.

Formal rules and regulations governing AI development and use. School AI policies address student data privacy, acceptable use, academic integrity, equity considerations, and teacher autonomy while ensuring compliance with educational regulations.

Documented best practices and rules for using AI in specific contexts. Educational AI guidelines typically cover appropriate use cases, data handling procedures, academic integrity standards, and ethical considerations for students and teachers.

A systematic tool for assessing AI systems before implementation in schools. These checklists typically cover data privacy, educational effectiveness, bias detection, cost-benefit analysis, accessibility features, and alignment with curriculum standards.