AI is no longer theoretical for K‑12 schools - it's in the tools students use, the platforms teachers interact with, and the analytics administrators rely on. The question has shifted from "should we engage with AI?" to "how do we engage with it responsibly?"

The practical framework: AI in schools falls into three categories. AI students use (ChatGPT, writing assistants, research tools). AI teachers use (lesson planning assistants, grading tools, content generators). AI the school uses (predictive analytics, early warning systems, operational automation). Each category requires different policies and different conversations.

The most valuable AI in an SIS isn't generative - it's analytical. AI-powered analytics can:

• Identify students at risk of disengagement before teachers notice
• Surface attendance and grade patterns that predict outcomes
• Flag data anomalies that suggest errors or missing information
• Recommend interventions based on what has worked for similar students

This is fundamentally different from generative AI (writing content, answering questions). SIS-based AI works with the school's own data to surface insights that would take hours to find manually. It's augmenting human judgment, not replacing it.

Yes. Schools without an AI policy are making AI decisions by default rather than by design. A practical school AI policy should address:

• Which AI tools students are permitted to use for academic work, and how AI assistance must be disclosed
• Which AI tools teachers are permitted to use for instruction, grading, and communication
• How AI-generated content is treated in academic integrity contexts
• What student data is permissible to share with AI tools (FERPA and privacy implications)
• How the school evaluates new AI tools before adopting them

The policy doesn't need to be long. It needs to be clear, communicated to every stakeholder, and reviewed annually as the technology evolves.

The primary concern is that AI tools - particularly generative AI services - may process, store, or train on student data in ways that violate FERPA. Schools need to verify:

• Does the AI tool store student data? For how long?
• Is student data used to train the AI model?
• Does the tool meet FERPA, COPPA, and state-level student privacy requirements?
• Where is the data processed and stored?

AI analytics within an SIS are typically lower risk because the data stays within the school's existing system and the vendor's existing data processing agreement. AI tools that students access directly carry higher risk because the data flows to a third party.

When an SIS vendor says "AI-powered," ask specifically:

• What data does the AI use? (Should be the school's own attendance, academic, and behavioral data)
• What does the AI produce? (Should be actionable insights - flagged students, trend analysis - not generic content)
• Where is the AI processing happening? (Should be within the vendor's infrastructure, not shipped to a third-party AI service)
• How is accuracy measured? (The vendor should be able to describe false positive rates and how the model improves)

AI features that are vague about their methodology or can't explain what data they use should be treated with skepticism. The best AI in an SIS is specific, transparent, and built on the school's own data.

Alma's AI - BeaconAI - is an analytical engine that works with your school's attendance, academic, and behavioral data to surface students who need attention. It identifies at-risk students based on pattern recognition across multiple data signals, flags trends that a single teacher or counselor might miss, and provides the data foundation for intervention decisions.

BeaconAI does not generate content, write communications, or make decisions on behalf of educators. It surfaces information so humans can make better decisions faster. That's a deliberate design choice.