SLV Fall Educators Conference 2026

I guarantee every one of you has heard an AI objection from your staff and students. There is a proven way to address them while steering use.
AI Ethics is about much more than water usage, and it is about much more than academic honesty. When it comes to AI, student and teacher objections start with the environmental impact, spiral into privacy concerns, and land on a deeper worry: that relying on AI is making us worse at thinking. These concerns are valid. They deserve more than a policy update.
 
But right now, most district conversations about AI ethics stop at acceptable use. Meanwhile, the people in your buildings, your teachers and your students, have real objections that aren't being addressed. And when concerns go unaddressed, people opt out quietly. That is exactly what happened at EF Education First before they changed their approach.
 
This session walks through exactly how to do that. We start by validating the common concerns rather than dismissing them, because dismissal is why people stop raising their hand. Then we do something practical with them.
 
Almost every AI objection falls into one of three buckets: Privacy, Complacency, or the Environment. Each one requires a different response, and each one has a steering mechanism that empowers people rather than shutting the conversation down.
 
 - **On Privacy** — we go under the hood. Who is actually using your data right now? What is the difference between tools that train on your input and tools that don't? What settings can you turn off today, and how do you teach someone to do that in under two minutes? This is not theoretical. Participants will see exactly where their data goes and how to stop it.
 
 - **On Complacency** — we talk about what "Lazy Brain" actually means. Cognitive offloading is real, and it matters more for adolescents than it does for adults because identity formation is still happening. But the answer is not to ban AI. The answer is to teach students when to use it and when to put it down. We look at what students are actually using AI for, why that is a rational response to how work is assigned, and how to redirect that behavior toward use cases that build capability instead of bypassing it.
 
 - **On the Environment** — we get the scale right in both directions. AI's energy and water footprint at inference versus training are very different numbers, and most of the comparisons people reach for are wrong. We are not here to tell you AI is fine. We are here to teach you how to steer so you can have a proportionate conversation with students, one that avoids both dismissal and alarmism. Conscious use is a teachable habit, and this section shows you what that looks like in practice.
 
The session starts and closes with an open question period. This is where it gets real. Participants bring the specific objections they are hearing in their buildings, and we work through them together using the framework from the session. The goal is not to leave with scripted answers. The goal is to leave with the confidence to have these conversations honestly, because your students and staff can tell when you're dodging the question. If your district is going to ask people to use AI, you owe them an honest conversation about the tradeoffs first. This session gives you the framework and the language to have it.