Hosts: Courtney Acosta & Mario Acosta Bios: https://www.theedleadershippair.com/about-us

Podcast: The EdLeadership Pair – Unfiltered Conversations for Today’s School Leaders

🔗 Connect With Us
📸 Instagram: @edleadership_pair
▶️ YouTube: The EdLeadership Pair
🌐 Website & Newsletter: www.theedleadershippair.com

Join our growing community of school leaders navigating today’s challenges together. Let us know what topics you want us to tackle next.

Episode Overview

AI policy isn’t about controlling technology — it’s about protecting learning.

In this episode, Courtney and Mario continue their conversation on AI in schools by shifting the focus from classroom use to system-level policy decisions. Using recent research, real-world district examples, and legal risk scenarios, they explore why many school systems are dangerously underprepared for AI and what leaders must do now to protect students, staff, and instructional integrity.

The conversation unpacks emerging research from MIT and other scholars on cognitive load, learning depth, and memory recall when students over-rely on AI tools. Courtney and Mario then connect that research directly to policy implications.

This episode makes the case for clear, values-driven, research-informed AI policies that guide teachers, students, and administrators toward responsible, learning-centered use of AI.

Big Ideas from the Conversation

AI policy exists to protect learning, not control behavior.
Unreliable AI detection tools create serious legal and ethical risks.
Over-reliance on AI leads to shallow processing and weaker long-term learning.
Human thinking must begin and end every AI interaction.
Policy cannot be created in isolation from parents, students, and teachers.
Data privacy violations are one of the biggest unseen AI risks in schools.
Most students and teachers currently have no clear guidance on AI use.
One-size-fits-all AI policies fail at the classroom level.
Professional learning around AI must be ongoing, embedded, and differentiated.

Leadership Actions Recommended in This Episode

1. Define the problem your AI policy is solving - Before writing policy, clarify your purpose: academic integrity, instructional quality, data privacy, staff efficiency, student preparedness, or all of the above. Policy without clarity creates confusion and risk.

2. Establish clear use categories - Explicitly define what AI use is allowed, limited, conditional, or prohibited, differentiated by role (students, teachers, administrators) and by grade band (elementary, middle, high school).

3. Do not rely on AI detection tools for discipline - AI detectors are inconsistent and unreliable. Using them as the sole evidence for academic misconduct exposes schools to lawsuits and long-term student harm.

4. Protect student data aggressively - Set strict guardrails around what data can never be entered into AI tools. Train staff on FERPA-aligned practices before encouraging AI use in PLCs or instructional planning.

5. Learn from districts already leading - Study existing models from districts like Chicago Public Schools, Dallas ISD, and state-level guidance such as Washington’s H–AI–H framework (Human → AI → Human).

6. Involve your community early - Parents, students, and teachers must be part of AI policy conversations. Surprising communities with AI policies invites backlash and erodes trust.

7. Commit to ongoing professional learning - AI training cannot be a one-time module. Leaders must plan for continuous, differentiated professional development that meets educators where they are.

8. Leverage AI to help build the policy itself - Use AI as a starting tool to draft frameworks, prompts, and guiding questions — then apply human judgment, values, and reflection before implementation.