AI Policy Audit Tool

Why it matters: AI should augment human capabilities rather than replace human judgment in educational settings.

Implementation tip: Ensure policies explicitly state that AI tools are decision-support systems, not decision-makers. Include language that requires human oversight for all AI-generated content or recommendations.

Why it matters: Student and staff data must be protected according to local laws, and institutions should maintain control over how this data is used.

Implementation tip: Create a data registry that tracks where all AI tools store data and ensure compliance with local regulations like GDPR, FERPA, or regional data sovereignty laws. Consider prioritizing tools that offer local data processing options.

Why it matters: AI systems can perpetuate or amplify existing biases, potentially disadvantaging certain student groups.

Implementation tip: Establish a regular audit schedule for AI tools that includes testing with diverse sample data. Create a diverse committee to review AI outputs for potential bias. Request transparency reports from vendors about how they address bias in their systems.

Why it matters: Understanding how AI reaches conclusions is essential for trust and for identifying potential errors or biases.

Implementation tip: Require that AI tools used in education provide clear explanations of their reasoning. For example, automated grading systems should explain which criteria influenced the grade and how. Avoid "black box" AI systems where decisions cannot be explained.

Why it matters: Not all AI tools are designed with educational contexts in mind, and some may have inappropriate features or inadequate safeguards.

Implementation tip: Create a formal review process for AI tools before they're approved for classroom use. Develop a rubric that evaluates tools based on privacy, accessibility, bias mitigation, and pedagogical value. Maintain and regularly update an approved tools list that teachers can reference.

Why it matters: Different age groups have different cognitive abilities, digital literacy levels, and developmental needs that should be considered when implementing AI tools.

Implementation tip: Create age-specific guidelines for AI tool usage. For younger students, focus on tools that have strong content filters and simplified interfaces. Consider restricting certain AI capabilities (like unrestricted text generation) for younger students while providing more access as students develop critical thinking skills.

Why it matters: Teachers need more than just technical training; they need to understand the ethical implications and pedagogical approaches for effectively integrating AI into education.

Implementation tip: Develop a comprehensive professional development program that includes modules on AI ethics, recognizing AI limitations, designing AI-enhanced lessons, and fostering critical thinking about AI. Allocate specific budget for this training and make it part of regular professional development cycles.

Why it matters: Students need to develop critical thinking skills about AI to become informed digital citizens who can evaluate AI outputs and understand how algorithms influence information.

Implementation tip: Integrate age-appropriate AI literacy lessons across subject areas. For younger students, focus on understanding that AI tools are created by humans and have limitations. For older students, include lessons on how algorithms work, how to evaluate AI-generated content, and the social implications of AI systems.

Why it matters: Large AI models, especially those generating video or complex images, can have significant carbon footprints due to their computational requirements.

Implementation tip: Create guidelines for when resource-intensive AI tools are appropriate to use. Consider the environmental impact in AI procurement decisions, favoring vendors with carbon-neutral commitments. Educate staff and students about the environmental costs of different AI applications to encourage mindful usage.

Why it matters: AI systems can make errors, and stakeholders need clear pathways to challenge decisions that affect educational outcomes.

Implementation tip: Establish a formal appeals process for any significant decision influenced by AI. Ensure the process is transparent, accessible, and timely. Designate specific staff members responsible for reviewing appeals and provide them with the authority to override AI-driven decisions when appropriate.