This is part 5 in a 12-part series on How Primary and Secondary Schools Use AI. The goal is to provide educators with a roadmap for planning AI usage in their schools.
Schools work hard to keep students from slipping through the cracks. In MTSS meetings, educators review attendance patterns, grades, behavior notes, reading data, and survey responses. The hope is to spot the signs that a student may be struggling. But these signals often sit scattered across different systems, making early detection difficult even for experienced teams.
AI is beginning to change that. New early-warning and MTSS-aligned tools can analyze large amounts of academic, attendance, behavioral, and social-emotional data in seconds. They surface patterns that would otherwise be hard to see, giving educators a clearer picture of which students may need check-ins, support, or immediate intervention.
Let’s explore how AI is strengthening MTSS, improving visibility into student well-being, and helping schools deliver proactive care.
A – What It Is
AI in MTSS and early-warning systems refers to tools that collect, organize, and analyze student data to identify emerging needs and guide timely interventions. In schools today, AI use in this area falls mainly into two key categories.
1. Identifying Early Signs of Risk
AI systems pull information from multiple sources such as attendance, grades, assessments, behavior logs, SEL surveys. They look for patterns that may indicate a student is beginning to struggle. These tools highlight early signals such as declining attendance, slipping academic performance, behavior changes, or shifts in social-emotional indicators. Instead of piecing these clues together manually, educators receive a clearer picture of who may need support and why.
2. Providing Actionable Insight for MTSS Teams
AI tools also help schools make sense of the data they review in MTSS meetings. They can summarize key concerns, note trends over time, or flag when a student’s risk level changes. Many systems highlight which students may need immediate Tier II attention, which may benefit from a check-in, and which are showing improvement. These insights help staff prioritize limited time and tailor supports more effectively.
AI-powered early-warning systems help educators see patterns earlier, respond more quickly, and support students before minor concerns grow into major barriers.
B – Why It’s Important
Supporting student well-being is one of the most essential responsibilities of a school, and AI dramatically strengthens that effort. Here are the major reasons it matters:
1. Early Intervention Changes Trajectories
Most academic or behavioral challenges worsen over time. The earlier schools intervene, the more effective the intervention, and the less intensive it needs to be. AI helps schools catch small problems before they become big ones.
2. Reduces Inequities
Students from historically underserved communities often face barriers to support. AI helps ensure no student is “invisible” because of understaffing or fragmented data systems.
3. Helps Counselors Manage Enormous Caseloads
School counselors often have hundreds of students. AI helps them prioritize who needs check-ins, home contact, or deeper support.
4. Improves Accuracy
Human judgment is essential, but it works best when supported by a complete picture. AI finds patterns across data sources that educators can easily miss when time is limited.
5. Strengthens Attendance & Behavior Systems
Chronic absenteeism and escalating behavior incidents rarely appear suddenly. AI identifies slow-building patterns so teams can respond before students disconnect.
6. Supports Whole-Child Well-Being
By incorporating social-emotional survey responses, AI provides a more holistic view of each student’s experience, not just academics.
7. Gives Educators Back Time
Instead of spending MTSS meetings digging through spreadsheets, teams can focus entirely on planning support and implementing it quickly.
Ultimately, AI allows schools to operate the MTSS system they have always wanted but rarely had capacity to run consistently.
C – How It’s Being Used
AI-powered MTSS and early-warning systems are now used at state, district, and school levels. The following case studies showcase real examples and the educators leading this work.
Case Study #1: Kentucky Department of Education – Using Statewide Early-Warning Indicators to Support On-Time Graduation
Focus: Statewide early-warning indicators for dropout prevention
Heroes: KDE data teams, school counselors, MTSS leaders, and Infinite Campus partners
What They Did
The Kentucky Department of Education developed a statewide Early Warning Tool within Infinite Campus to identify students who may be at risk of not graduating. The system uses academic, attendance, behavior, stability, and course-progress data to generate a “GRAD” score and domain-specific indicators based on years of statewide data.
How It Worked
Educators access dashboards showing individual student risk levels and the factors contributing to them. Staff can create watch lists, review domain-specific indicators, and examine trends at the student, school, or district level. The system is designed to help educators understand why a student is at risk and intervene earlier.
What the Results Showed
State documentation notes that the Early Warning system helps schools forecast risk more accurately, understand underlying causes, and design targeted interventions within their MTSS frameworks. Its purpose is to support proactive dropout prevention with clear, data-driven insight.
Case Study #2: New Mexico Districts – Automating Attendance Outreach With AI Messaging Tools
Focus: Reducing chronic absenteeism through automated family communication
Heroes: Attendance clerks, family liaisons, operations teams, and Edia platform developers
What They Did
Four New Mexico districts piloted an AI-driven attendance communication tool that sends personalized, multilingual text messages to families when students are marked absent. The system compiles responses and identifies trends to support schools’ attendance teams.
How It Worked
When a student is marked absent, the AI system immediately contacts families in their home language and logs replies in an attendance profile. District staff reported that this reduced the daily burden of phone calls and manual entry, allowing them to focus on higher-need cases and root-cause problem-solving.
What the Results Showed
According to public reporting, the pilot improved response rates from families, helped districts address chronic absenteeism more efficiently, and reduced the follow-up workload on staff. Officials described the system as a useful support during a period of increased attendance challenges.
Case Study #3: Mesa Public Schools – Using Unified Academic & SEL Dashboards to Strengthen MTSS
Focus: Combining early-warning and SEL analytics for whole-child support
Heroes: MTSS teams, counselors, school psychologists, and Panorama Education partners
What They Did
Mesa Public Schools implemented Panorama Education’s platform to bring academic, behavior, attendance, and social-emotional data into a single dashboard. The system’s early-warning indicators and SEL analytics informed MTSS decision-making across the district.
How It Worked
Teams used dashboards to identify students showing signs of disengagement, attendance decline, or risk of course failure. Panorama’s SEL insights highlighted patterns in belonging, relationships, emotional regulation, and well-being, helping staff plan interventions, counseling groups, advisory lessons, and check-in cycles.
What the Results Showed
Mesa reports stronger MTSS consistency and a clearer ability to see the “whole child.” Educators noted that SEL insights often revealed emerging concerns before academic performance shifted, enabling earlier, more equitable support.
Case Study #4: Los Angeles Unified School District – Monitoring On-Track Status With Early Warning Indicators in MiSiS
Focus: Graduation readiness and dropout prevention
Heroes: Counseling teams, student support staff, and district data analysts
What They Did
Los Angeles Unified School District built Early Warning Indicators (EWI) into its MiSiS student information system. These indicators categorize students as “On Track,” “Off Track,” or “High Risk” based on attendance, grades, credits, behavior, and graduation requirements.
How It Worked
Counselors accessed regularly updated dashboards to identify students whose progress was slipping and to view the specific factors contributing to risk. The data informed credit recovery placement, tutoring, family outreach, and other MTSS supports.
What the Results Showed
District documentation shows that EWI data helps staff intervene earlier with students beginning to fall behind and supports broader graduation-rate improvement efforts. Schools use these indicators throughout ongoing MTSS monitoring cycles to maintain proactive attention.
D – Pro Tips
1. Use AI to Flag Students, but Let Educators Lead
AI should surface early signals, but counselors and MTSS leaders, like those in Kentucky, use their judgment and relationships to determine the right support for each student.
2. Pilot One Area Before Expanding Districtwide
Many early-adopter districts begin with a small pilot, such as attendance or on-track indicators, allowing MTSS teams to refine routines before rolling out AI tools more broadly.
3. Automate Routine Tasks to Focus on High-Need Cases
New Mexico attendance clerks and family liaisons found that AI-powered messaging handled simple communication so staff could focus on students facing deeper barriers like transportation or mental health challenges.
4. Combine Academic, Behavior, Attendance & SEL Data
Mesa Public Schools shows how unified dashboards help MTSS teams spot early dips in belonging or engagement—signals that often appear before grades start to fall.
5. Make Dashboards Part of Weekly MTSS Routines
Counselors in LAUSD check early-warning indicators during every MTSS meeting, ensuring that shifting risk levels are noticed quickly rather than waiting for report cards.
6. Communicate Early and Consistently with Families
Districts in New Mexico saw better family responsiveness when AI tools sent same-day, multilingual messages, preventing minor absences from turning into chronic issues.
7. Keep Data Secure and Centralized
Kentucky and LAUSD demonstrate the importance of using district-managed systems, which protect student information while keeping early-warning data accurate and accessible to the right teams.
References
CRPE. “AI in Education: Projects & Rapid Response Research.”
https://crpe.org/projects/ai-in-education/
Education Commission of the States. “AI Pilot Programs in K–12 Education.”
https://www.ecs.org/ai-artificial-intelligence-pilots-k12-schools/
Center on Reinventing Public Education (CRPE). “AI Early Adopter Districts: The Promises and Challenges of Using AI to Transform Education.”
https://eric.ed.gov/?id=ED674608
Farmington Municipal Schools. “Edia Launches AI Platform to Reduce Chronic Absenteeism in Schools.”
https://www.farmingtonschools.us/article/1850431
GovTech. “New Mexico Schools Use AI to Track Student Absences and Support Educators.”
https://www.govtech.com/education/k-12/new-mexico-schools-use-ai-to-track-student-absences
Kentucky Department of Education. “Early Warning, Insights and Persistence to Graduation Data Tools.”
https://education.ky.gov/educational/int/Pages/EarlyWarningAndPersistenceToGraduation.aspx
Kentucky Department of Education. “Early Warning Tool Overview.”
https://education.ky.gov/educational/int/Documents/Early%20Warning%20Tool%20Overview.pdf
Los Angeles Unified School District. “Early Warning Indicators (EWI).”
https://achieve.lausd.net/Page/10665
LAUSD MiSiS Documentation. “Graduation Progress and EWI Overview.”
https://achieve.lausd.net/misis
Panorama Education. “Mesa Public Schools: Using Whole Child Data to Strengthen MTSS.”
https://www.panoramaed.com/case-studies/mesa-public-schools
Panorama Education. “Early Warning System for K–12.”
https://www.panoramaed.com/early-warning-system
