E2 is a critical competency for educators to acquire before introducing AI tools to students, such as experimenting Classroom features with students.

To achieve E1. Responsible Operation, educators must not only know how to use AI, but also ensure every use aligns with ethical norms, institutional policies, and legal requirements,  including but not limited to: protecting students’ and others’ privacy, upholding trust, and ensuring transparency.

1. Understand and Follow Institutional and Legal Guidelines

• Know the rules: Be familiar with district, school, and platform policies on AI use, including approved tools and prohibited practices.
• Comply with laws: Follow regulations like FERPA, COPPA, and applicable data protection laws when handling student information.
• Stay updated: Monitor changes in AI-related policies and compliance requirements.

2. Protect Data Privacy and Security

• Minimize personal data use: Avoid inputting identifiable student, family, or coworker information into AI tools unless explicitly approved.
• Use secure platforms: Operate only within institution-approved AI environments with proper security safeguards.
• Verify storage and sharing practices: Understands where data is stored and who has access to it for the AI tools in use.

3. Maintain Transparency

• Disclose AI use: Let students, parents, and colleagues know when AI is being used to generate, adapt, or assess content.
• Acknowledge AI contributions: Attribute AI outputs where appropriate, especially in shared or published materials.
• Be clear about AI limitations: Communicate that AI outputs may require verification and human judgment.

4. Ensure Accessibility

• Check for bias: Inspect AI-generated materials for stereotypes, exclusionary language, or cultural insensitivity.
• Promote accessibility: Adapt AI content to be usable by all students, including those with disabilities or language barriers.
• Use AI to close—not widen—gaps: Prioritize AI applications that improve access to learning for underrepresented or disadvantaged students.

To achieve E2. Ethical Modeling, educators must treat their own AI use as a visible example for students, showing what responsible, reflective, and value-driven digital behavior looks like in AI-supported environments. This goes beyond compliance (E1) into mentorship, helping students develop their own ethical reasoning skills.

1. Reflect on Personal AI Practices

• Self-assess regularly: Review how, when, and why you use AI, considering accuracy, fairness, and student impact.
• Acknowledge mistakes and corrections: Share instances where you revised your AI use based on ethical concerns.
• Identify improvement areas: Set personal goals for more transparent and safe AI use.

2. Make Ethical Decisions Visible

• Think aloud during use: When appropriate, verbalize your decision-making process as you prompt, select, and adapt AI content.
• Highlight responsible choices: Point out why you reject biased outputs or fact-check AI responses.
• Discuss trade-offs: Explain why certain AI tools and features may not be used due to privacy or accessibility concerns.

3. Guide Students in Ethical Reasoning

• Connect to digital citizenship: Link AI use to broader concepts like source credibility, online safety, and intellectual property.
• Foster critical inquiry: Encourage students to question how AI generated an output and what assumptions might be embedded.
• Use case-based learning: Analyze real or simulated AI scenarios where ethical choices must be made.

4. Model Ethical AI Use for Students

• Teach digital responsibility: Demonstrate safe, ethical AI use and explain why these practices matter.
• Encourage critical evaluation: Guide students to question and verify AI outputs rather than accept them blindly.
• Foster agency: Help students see AI as a tool they control, not a replacement for their own thinking.

5. Indicators of Mastery

• Students can articulate ethical considerations in AI use because they have seen them modeled in class.
• Students adopt reflective habits, checking their own AI use for fairness, accuracy, and respect.
• Ethical reasoning becomes part of classroom culture, not just a one-time lesson.

To achieve E3. Critical Inspection, educators must be able to systematically review AI outputs for accuracy, bias, and reliability, and then actively correct or mitigate any issues before using them with students or in professional work.

1. Accuracy Verification

• Cross-check facts: Validate information against trusted academic sources, curriculum documents, and verified data sets.
• Spot logical errors: Identify flawed reasoning, unsupported claims, or mismatched examples in AI-generated responses.
• Confirm citations and references: Ensure that any provided sources are real, credible, and relevant.

2. Bias Detection

• Identify stereotypes and exclusion: Look for language, examples, or framing that perpetuates cultural, gender, or other biases.
• Check representation: Ensure that diverse perspectives and voices are reflected in the content.
• Evaluate framing: Notice if the AI presents a single perspective as universal or omits key viewpoints.

3. Misinformation Correction

• Edit content directly: Revise inaccuracies, misleading statements, or biased framing in the AI output.
• Use AI iteratively: Prompt the AI to regenerate or reframe content based on identified issues (“Reflect on the content and revise this to remove bias and verify facts”).
• Supplement with human-sourced content: Add teacher-created or authoritative resources to balance or replace flawed sections.

4. Transparency in Corrections

• Document changes: Keep a record of identified errors and the revisions made.
• Explain adjustments to students: Share age-appropriate explanations for why content was modified, modeling responsible information use.
• Build student awareness: Help students learn to spot and question bias and misinformation themselves.

5. Indicators of Mastery

• • Rarely uses AI-generated content “as is” without verification.
  • Can clearly explain what was changed and why.
  • Actively teaches bias and misinformation detection as part of instruction.