Content
Introduction
This Applied Use Guide provides guidelines and examples for using AI responsibly in K-12 education, helping teachers personalize lessons, identify student needs, and enrich learning while protecting minors’ privacy and well-being.
Reason Why
AI can be a game-changer in creating adaptive learning paths, offering instant feedback, and catering to diverse skill levels. Nevertheless, educators must ensure these tools do not undermine equity (e.g., penalizing certain backgrounds) or infringe on student data rights. Ethical AI fosters an inclusive environment that benefits all students, rather than exacerbating existing disparities.
Key Principles
- Transparency: Communicate to students and parents how AI tools are used, what data they collect, and how that data is safeguarded.
- Fairness: Ensure algorithms don’t label or track students in a way that perpetuates stereotypes or disadvantages any group.
- Privacy: Safeguard student data to comply with child protection regulations (like COPPA) and local privacy laws.
- Inclusivity: Develop AI tools that account for varied learning styles, linguistic backgrounds, and accessibility needs.
- Human-Centric: Teachers and counselors remain essential. AI insights should guide—not replace—human care and judgement.
Best Practices
- Disclose AI Usage: Inform parents, guardians, and students about which AI systems are in use and the purposes they serve.
- Protect Student Data: Implement anonymization or restricted access to sensitive student data, and follow local regulations regarding children’s information.
- Monitor and Mitigate Bias: Conduct regular checks to ensure the AI doesn’t disproportionately mark certain student demographics as ‘high-risk’ or ‘low-achieving.’
- Enhance Human-AI Collaboration: Teachers should interpret AI-driven analytics, tailoring lesson plans with empathy and personal knowledge of each student’s context.
- Offer Alternatives: In instances where families are uncomfortable with AI-based tools or students struggle with the digital interface, provide traditional resources or manual interventions.
Specific Techniques
Technique 1: Adaptive Learning Paths
Default Prompt: Create personalized learning paths for students in the math class.
Updated Prompt: Create adaptive math learning paths for students, focusing on fairness and inclusivity. Explain what data is gathered to customize lessons, how you ensure no group is overlooked, and indicate how teachers can review or override AI-suggested difficulty levels.
Technique 2: Student Progress Tracking
Default Prompt: Track student progress in reading comprehension.
Updated Prompt: Track reading comprehension progress while respecting individual privacy. Highlight how the system flags improvement areas and what safeguards are in place to prevent labeling students unfairly. Outline how teachers can manually adjust or add context to these AI-derived insights.
Technique 3: Tailored Educational Resources
Default Prompt: Recommend educational resources for science projects.
Updated Prompt: Recommend science resources for different learning styles and age groups. Clarify how the recommendations consider diverse backgrounds, language needs, or special education requirements, and how AI ensures compliance with parental consent for younger students.
Technique 4: Predictive Analytics for Student Support
Default Prompt: Predict which students might need additional support in upcoming exams.
Updated Prompt: Predict which students might need extra support in upcoming exams, explaining the data points used. Emphasize that these predictions are provisional, encouraging teachers and counselors to investigate further and provide tailored interventions without stigma.
