Teacher in the Loop

AI Tutoring Platform Built From Special Education First

A revolutionary tutoring system that delivers IEP accommodations invisibly and automatically, detects emotional states before meltdowns, and keeps teachers empowered - never replaced. Built by someone who lived with an IEP, not just someone who read about them.

Patent Filed: October 17, 2025

Why This Exists: A Personal Mission

I was the kid who wouldn't raise my hand for extra time. Despite having an IEP for ADHD, I'd rather fail than be singled out. The accommodations were there, but using them meant standing out, being different, being "that kid."

Now I watch my son face the same impossible choice: support or dignity. Kids shouldn't have to choose. They shouldn't need courage to access legally mandated support.

Teacher in the Loop exists because accommodations should be automatic, invisible, and dignified. When a student with extended time starts a test, they just get the time. No announcement. No special login. No stigma. The teacher knows (transparency matters), but the student? They just get what they need to succeed.

This isn't built by educators theorizing about special education. It's built by someone who lived it, who knows the difference between what IEPs promise and what actually happens in the classroom.

Our Commitment: Teachers First, Always

When it comes to technology in the classroom, especially AI, the first concern is always: "What happens to teachers? Will they be replaced?"

The reality is complex. EdTech companies sometimes allow promises to slip through the cracks. Districts implement new tools, budgets shift, and suddenly teaching positions disappear. It's not always intentional, but the outcome is the same: educators lose their jobs.

This Is Our Binding Contractual Obligation:

📃

Employment Record Access Required

Any school district deploying Teacher in the Loop must provide access to employment records for teachers and aides in relevant departments.

⚠

Immediate Termination Clause

If teachers or aides are let go after deploying this product, the contract is immediately terminated and the school loses access to the tool. No exceptions.

"I will not be a party to teachers and aides losing their jobs."

The Problem We're Solving

Many

Won't Use Accommodations

Educators widely report that many students with IEP accommodations avoid using them due to stigma. We're failing kids who are too afraid to access support they're legally entitled to.

Hidden

Emotional Struggles

Kids with ADHD, autism, or anxiety often can't articulate when they're struggling. By the time teachers notice, it's often too late to prevent a meltdown or shutdown.

Buried

Teachers Drowning

Special education teachers spend more time on paperwork than teaching. IEP compliance tracking, progress monitoring, and documentation bury the actual education.

⚠️ Development Notice

The scope and method of implementation of each feature is subject to change during development. Feature videos represent current design concepts and may evolve based on technical feasibility, user feedback, and educational research findings.

Revolutionary Features for Special Education

🎯 Core Innovations

Opt-Out Available

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Invisible IEP Accommodation Delivery

Accommodations activate automatically based on IEP data. No student action required. Extended time, text-to-speech, simplified instructions - all delivered silently. Support without stigma.

Zero Stigma Automatic

Privacy Notice: Automatic IEP Integration

What This Involves

Automatically implements your child's IEP accommodations without manual activation. The system reads IEP requirements and delivers them invisibly whenever your child logs in - extended time, text-to-speech, behavioral supports, etc.

What You Control

Full opt-out (return to manual teacher implementation)
Partial opt-out (choose which accommodations are automated)
Real-time dashboard showing which accommodations are active
Control over IEP data transfer if your child changes schools

How Data Is Protected

Only special education team sees full IEP details
Regular teachers see accommodation requirements, not diagnostic information
Platform executes accommodations without storing disability labels
All IEP data deleted within 48 hours if student transfers without consent

Read Complete Privacy Notice →

Explicit Opt-In Required

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Real-Time Emotional State Detection

Uses webcam, typing patterns, and response timing to detect confusion, frustration, and cognitive overload. Teachers get alerts BEFORE meltdowns happen.

Early Warning Privacy-First

Privacy Notice: Multi-Modal Emotional State Detection

What This Involves

Analyzes your child's emotional state in real-time by monitoring facial expressions (if webcam enabled), typing rhythm, mouse movements, engagement indicators, and interaction behaviors. The system detects anxiety, confusion, boredom, frustration, and engagement levels, then alerts teachers when intervention might help. This involves continuous behavioral monitoring that requires explicit parental consent.

What You Control

Full opt-out (no emotional state monitoring at all)
Behavioral monitoring only (no webcam analysis)
Webcam control (separate permission, student can disable anytime)
Temporary disable for private moments or standardized tests
Customizable alert thresholds (only severe distress vs. mild confusion)

How Data Is Protected

No video recordings or screenshots stored - ever
Facial analysis processed on your device in real-time, images immediately discarded
Emotional state patterns kept for only 90 days, then deleted
Teachers receive alerts only, not access to webcam or raw behavioral data

Read Complete Privacy Notice →

👁️

Complete Transparency & Control

Teachers see every AI decision and can accept, modify, or override. This is augmentation, not automation. The teacher stays in the loop - always.

Teacher Empowerment No Black Box

🧠 Emotional & Cognitive Support

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Multi-Modal Emotion Detection

Combines facial expression analysis, typing rhythm, mouse movements, and pause patterns. Detects anxiety, confusion, boredom, and engagement without invasive monitoring.

Non-Invasive Multi-Signal

Opt-Out Available

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Dynamic Cognitive Load Adjustment

Automatically adjusts difficulty and pacing based on student's current cognitive state. Prevents both overwhelming and under-challenging students.

Adaptive Personalized

Privacy Notice: Invisible Disability Accommodations

What This Involves

Delivers disability accommodations through interface design rather than visible add-ons. Two students sitting side-by-side see interfaces that look identical but are individually optimized - neither student nor peers notice the difference.

What You Control

Keep invisible (default) - accommodations embedded, peers cannot tell
Make student-visible - student sees what helps them, peers don't
Make fully visible - explicit accommodation notifications
Age-based recommendations (invisible for elementary, student choice for high school)

How Data Is Protected

Uses IEP data from Feature 3 (no additional data collection)
Disability labels never visible to student or peers
Accommodation logs don't reveal diagnostic information
Real-time parent dashboard shows which accommodations are active

Read Complete Privacy Notice →

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Proactive Teacher Alerts

Notifies teachers when students show signs of distress, confusion, or disengagement. Enables intervention before problems escalate.

Prevention Actionable

📋 Compliance & Documentation

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Automatic IEP Compliance Tracking

Every accommodation delivery is logged with timestamps and context. Generate compliance reports with one click. Full IDEA audit trail.

Legal Compliance Automated

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Progress Monitoring Dashboard

Real-time visualization of IEP goal progress. Automatic data collection replaces manual tracking. Teachers see patterns, not just data points.

Data-Driven Visual

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Parent Portal Access

Parents see their child's progress, accommodation usage, and emotional well-being. Transparency without overwhelming detail. Trust through visibility.

Family Engagement Controlled Access

🛡️ Academic Integrity & Safety

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Silent Behavioral Analysis

Detects potential cheating through typing patterns and response timing - not invasive monitoring. Protects academic integrity while respecting privacy.

Privacy-Respecting Multi-Factor

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Provenance Tracking

Every AI-generated suggestion is marked and tracked. Teachers and parents know exactly what help was provided and when.

Attribution Transparency

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Collaboration Detection

Distinguishes between authorized collaboration and potential cheating. Supports group work while maintaining individual accountability.

Flexible Smart Detection

Explicit Opt-In Required

✍️

Process-Based Academic Integrity Verification

Captures the entire writing journey with keystroke patterns, revision history, and thinking time. Cryptographically-signed evidence proves work is authentic without invasive proctoring.

Forensic Evidence Cryptographic

Privacy Notice: Process-Based Academic Integrity Verification

What This Involves

Monitors how your child creates their work (not just the final product) to verify academic integrity. Analyzes writing process, research behavior, problem-solving workflow, and consistency patterns. This is NOT traditional plagiarism detection - we verify the work process itself is authentic to your child. This involves detailed observation of how students work, requiring explicit informed consent.

What You Control

Full opt-out (no process monitoring at all)
Writing process only (monitor documents, not research)
Major assignments only (high-stakes work verification)
Student awareness settings (let them see when monitoring is active)
Temporary disable for specific assignments or brainstorming

How Data Is Protected

We see typing rhythm, NOT keystroke content - no text logging
No screen recordings or screenshots ever captured
Process data limited to work within platform (not cross-application)
30-day retention only - deleted after assignment due date
Never used for AI training or commercial purposes

Read Complete Privacy Notice →

🎓 AI Tutoring & Pedagogy

Explicit Opt-In Required

🧬

Student Digital Twins with Predictive Modeling

Creates a living cognitive model of each student. Runs "what-if" simulations to predict crises 5-15 minutes early. Portable profiles follow students across grade levels and platforms.

Predictive Portable

Privacy Notice: Student Digital Twins

What This Involves

Creates a detailed cognitive profile of your child's learning patterns, including knowledge strengths/gaps, behavioral patterns, cognitive processing characteristics, response to teaching approaches, and social learning preferences. Your child's cognitive profile is more personally identifying than their name, address, or even Social Security Number.

What You Control

Full opt-out (platform works without twin creation)
Partial permissions (academic only, academic + behavioral, or full profile)
Annual renewal requirement (permission expires each school year)
Deletion on demand (executed within 48 hours)
Transfer control (you decide if twin transfers to new schools)

How Data Is Protected

All identifying information stripped at ingestion
Encrypted with school-controlled keys
Teachers access through simulation interface only, never see raw data
Portable export requires explicit permission
Automatically deleted 90 days after graduation

Read Complete Privacy Notice →

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Socratic AI Engine That Never Gives Answers

Guides students through questions instead of providing solutions. Works within zone of proximal development with teacher-configurable scaffolding. Students discover understanding, not copy answers.

True Learning No Shortcuts

Opt-Out Available

👥

AI-Optimized Peer Tutoring Matching

Matches students based on knowledge complementarity and social compatibility. Tracks the Protege Effect where teaching reinforces tutor's own learning.

Peer Learning Social Matching

Privacy Notice: AI-Optimized Peer Tutoring Matching

What This Involves

Uses AI to match students for peer tutoring based on knowledge complementarity, teaching effectiveness prediction, social compatibility, and mutual benefit optimization. Creates highly effective peer learning partnerships instead of random pairings.

What You Control

Full opt-out (your child not included in peer matching)
Parent-approved pairings only (review before activation)
Academic-only matching (ignore social factors)
Tutor role only or tutee role only options
Partner preferences (specify students to avoid or prefer)

How Data Is Protected

Disability diagnoses or IEP status NEVER shared with peers
Partners see subject areas for collaboration, not diagnostic reasons
Behavioral screening prevents students with bullying history from tutoring
System ensures reciprocal pairings (everyone both teaches and learns)

Read Complete Privacy Notice →

📊 Advanced Learning Analytics

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Multi-Modal Comprehension Analysis in Real-Time

Provides 0.0-1.0 comprehension scores with disability-aware adjustments. Triggers interventions on understanding, not correctness. Sees struggle before students even know they're lost.

Understanding-Based Disability-Aware

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Productive Struggle Detection Algorithm

Distinguishes beneficial challenge from harmful frustration. Only intervenes when struggle becomes unproductive. Protects the learning that happens when students work through difficulty.

Growth Mindset Research-Based

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Bayesian Knowledge Tracing with Forgetting Curves

Models probabilistic knowledge state for each concept. Calculates mastery probability and tracks knowledge decay over time. Knows when review is needed before students forget.

Predictive Spaced Repetition

Opt-Out Available

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Early Warning System with Multi-Factor Risk Detection

Analyzes academic, behavioral, and social-emotional indicators. Detects at-risk students weeks before crisis. Combines attendance, engagement, grades, and emotional patterns into actionable alerts.

Multi-Factor Weeks-Early Detection

Privacy Notice: Multi-Factor Early Warning System

What This Involves

Analyzes multiple data streams to detect students at risk of academic or behavioral crisis weeks before it happens. Combines academic performance, behavioral indicators, social-emotional signals, and temporal patterns to provide early alerts to teachers when concerning patterns align.

What You Control

Full opt-out (no predictive analytics about your child)
Customized sensitivity (high, moderate, or crisis-only alerts)
Data source restrictions (academic only, behavioral only, or custom)
Alert preferences (parent notification for all alerts or high-severity only)

How Data Is Protected

Alerts temporary (30-day retention), never in permanent record
Not included in transcripts or shared outside school
No discrimination factors (race, gender, socioeconomic status never used)
Deleted when student leaves system
Regular bias auditing to prevent disproportionate alerts

Read Complete Privacy Notice →

🚀 Intelligent Intervention

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Intelligent Intervention Routing System

Classifies struggle types and routes to appropriate support. Three-tier escalation: AI guidance, peer tutoring, then teacher intervention. Right help at the right time from the right source.

Smart Routing Tiered Support

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Explainable AI with Pedagogical Reasoning

Every AI decision includes educational reasoning. Explanations tailored for students, teachers, and parents. Retroactive explanations for past decisions. No black box, just transparent logic.

Transparent Multi-Audience

🔒 System Safety & Privacy

🛡️

Universal Context Checkpoint Protocol (UCCP)

Prevents AI hallucination cascades through knowledge cutoff awareness and reality drift prevention. Complete audit trail ensures AI never invents facts or forgets what's real versus planned.

Anti-Hallucination Audit Trail

🎭

Privacy-Preserving Synthetic Student Generation

Creates artificial students for system testing and rare edge cases. Differential privacy protects real student data. Test against realistic scenarios without exposing actual children.

Differential Privacy Safe Testing

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Automated COPPA/FERPA Compliance Workflow

Real-time permission enforcement at system level. Auto-generates consent forms with audit trail. Impossible to accidentally violate privacy laws when system enforces compliance architecturally.

Legal Compliance Architectural

👨‍🏫 Teacher Support Tools

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Teacher AI Co-Pilot with Research-Backed Recommendations

Assists with differentiated instruction, resource curation, and lesson planning. Every suggestion backed by educational research. Learns individual teacher preferences and classroom context.

Research-Backed Personalized

The Impact We're Building

Zero Student actions required for accommodation delivery

100% IEP compliance audit trail

Real-Time Emotional state alerts to teachers

Invisible Support delivery to peers

Support This Innovation

Teacher in the Loop represents a fundamental shift in special education technology - from stigmatizing to dignifying, from reactive to proactive, from replacing teachers to empowering them.

Currently in development. We're seeking partnerships and funding to bring this vision to reality. While not yet ready for deployment, schools interested in future pilot programs can join our early access list.

If you're an educator or administrator who loves this concept, send us an email letting us know how this could help you. Building interest builds funding. Building funding makes this happen faster.

Partner With Us Join Early Access List Share Your Interest