Get Started with Aeion LMS
Set up your learning management system in 5 steps. AI teaching assistants, grade prediction, standards heatmaps, LMS analytics, accessibility, and parent notifications—all in under 20 minutes.
Configure LMS Integrations
| Provider | Roster Sync | Grade Passback | Content Import |
|---|---|---|---|
| Google Classroom | ✅ | ✅ | ✅ |
| Canvas | ✅ | ✅ | ✅ |
| Blackboard | ✅ | ✅ | ❌ |
| Moodle | ✅ | ✅ | ❌ |
| Schoology | ✅ | ❌ | ❌ |
| Clever | ✅ | ❌ | ❌ |
| PowerSchool | ✅ | ❌ | ❌ |
Set Up Gradebook
Go to LMS → Gradebook → Configure.
Create Grade Categories:
bash
POST /lms/gradebook/categories
{
"courseId": "course-uuid",
"categories": [
{ "name": "Tests", "weight": 40, "dropLowest": 0 },
{ "name": "Quizzes", "weight": 15, "dropLowest": 2 },
{ "name": "Homework", "weight": 20, "dropLowest": 3 },
{ "name": "Projects", "weight": 25, "dropLowest": 0 }
]
}
Grading Scale Configuration:
typescript
{
"scale": [
{ "letter": "A+", "minPercentage": 97, "gpaPoints": 4.0 },
{ "letter": "A", "minPercentage": 93, "gpaPoints": 4.0 },
{ "letter": "A-", "minPercentage": 90, "gpaPoints": 3.7 },
{ "letter": "B+", "minPercentage": 87, "gpaPoints": 3.3 },
{ "letter": "B", "minPercentage": 83, "gpaPoints": 3.0 },
{ "letter": "B-", "minPercentage": 80, "gpaPoints": 2.7 },
{ "letter": "C+", "minPercentage": 77, "gpaPoints": 2.3 },
{ "letter": "C", "minPercentage": 73, "gpaPoints": 2.0 },
{ "letter": "C-", "minPercentage": 70, "gpaPoints": 1.7 },
{ "letter": "D+", "minPercentage": 67, "gpaPoints": 1.3 },
{ "letter": "D", "minPercentage": 63, "gpaPoints": 1.0 },
{ "letter": "F", "minPercentage": 0, "gpaPoints": 0.0 }
],
"gpaScale": 4.0
}
Grade an Assignment:
bash
POST /lms/gradebook/grades
{
"assignmentId": "assignment-uuid",
"studentId": "student-uuid",
"score": 85,
"maxScore": 100,
"feedback": "Great work on the analysis section!"
}
Get Student Grade Status:
bash
GET /lms/gradebook/students/{studentId}/status?courseId=course-uuid
Response:
typescript
{
"enrollmentId": "enrollment-uuid",
"currentGrade": 88.5,
"letterGrade": "B+",
"totalPoints": 1770,
"maxPossiblePoints": 2000,
"percentage": 88.5,
"categories": [
{
"name": "Tests",
"weight": 0.4,
"currentScore": 360,
"maxScore": 400,
"percentage": 90
}
]
}
Configure Accessibility
| Mode | Description | CSS Filter |
|---|---|---|
| none | Standard | — |
| deuteranopia | Red-green (protan) | greyscale + swap |
| protanopia | Red-green (deutan) | greyscale + swap |
| tritanopia | Blue-yellow | greyscale + swap |
| achromatopsia | Complete | greyscale |
Set Up Standards Mastery Heatmaps
Go to LMS → Standards → Heatmaps.
Import Standards:
bash
POST /lms/standards/import
{
"standardSet": "common_core",
"gradeLevel": "9",
"subjects": ["math", "ela"]
}
Supported Standard Sets:
- Common Core State Standards
- NGSS (Next Generation Science Standards)
- State-specific standards
- Custom standards
Get Student Standards Heatmap:
bash
GET /lms/standards/heatmap/students/{studentId}?courseId=course-uuid
Response:
typescript
{
"studentId": "student-uuid",
"studentName": "Jane Smith",
"cells": [
{
"standardCode": "CCSS.MATH.9-12.A.REI.1",
"standardName": "Explain solving equations",
"domain": "Algebra",
"category": "Reasoning with Equations",
"masteryPercent": 85,
"masteryLevel": "mastered",
"color": "#22C55E",
"attemptCount": 5,
"lastAssessedAt": "2026-05-10",
"trend": "improving"
}
],
"summary": {
"totalStandards": 45,
"masteredCount": 22,
"advancedCount": 8,
"atRiskCount": 3,
"notStartedCount": 5,
"overallMastery": 72
}
}
Get Class Standards Heatmap:
bash
GET /lms/standards/heatmap/class?courseId=course-uuid
Response:
typescript
{
"courseId": "course-uuid",
"courseName": "Algebra I - Period 2",
"aggregate": {
"byStandard": {
"CCSS.MATH.9-12.A.REI.1": {
"standardCode": "CCSS.MATH.9-12.A.REI.1",
"averageMastery": 68,
"medianMastery": 72,
"masteredPercent": 45,
"atRiskPercent": 15,
"trend": "stable"
}
},
"overall": {
"classAverage": 71,
"classMedian": 74,
"atRiskStudents": 4
}
}
}
At-Risk Identification:
bash
GET /lms/standards/heatmap/at-risk?courseId=course-uuid
Configure Parent Notifications & AI Features
Go to LMS → Notifications → Configure.
Parent Notification Settings:
typescript
{
"gradeAlerts": {
"enabled": true,
"threshold": 70,
"channels": ["email", "sms", "push"]
},
"attendanceAlerts": {
"enabled": true,
"notifyOnAbsence": true,
"notifyOnTardy": true
},
"assignmentReminders": {
"enabled": true,
"reminderHoursBefore": 24
},
"digestMode": "daily",
"languagePreference": "en"
}
Grade Prediction What-If Scenarios:
bash
POST /lms/grades/whatif
{
"studentId": "student-uuid",
"courseId": "course-uuid",
"scenario": {
"name": "Boost my grade to A-",
"hypotheticalScores": [
{ "assignmentId": "final-exam", "hypotheticalScore": 92 },
{ "assignmentId": "final-project", "hypotheticalScore": 88 }
]
}
}
Response:
typescript
{
"scenarioId": "scenario-uuid",
"projectedGrade": 90.5,
"projectedLetterGrade": "A-",
"gradeChange": 2.0,
"isAchievable": true,
"message": "Scoring 92% on Final Exam and 88% on Final Project will raise your grade from B+ to A-."
}
Required Grade Calculation:
bash
POST /lms/grades/required-grade
{
"studentId": "student-uuid",
"courseId": "course-uuid",
"targetGrade": 90
}
Response:
typescript
{
"targetGrade": 90,
"targetLetterGrade": "A-",
"isAchievable": true,
"minimumGradeNeeded": 85,
"maximumPossibleGrade": 95,
"message": "You need at least 85% on remaining assignments to reach an A-. Maximum possible grade: 95%.",
"requiredScores": [
{ "assignmentId": "final-exam", "requiredScore": 80, "isAchievable": true }
]
}
AI Teaching Assistant - Lesson Plan Generation:
bash
POST /lms/ai/lesson-plan
{
"courseId": "course-uuid",
"standards": ["CCSS.MATH.9-12.A.REI.1", "CCSS.MATH.9-12.A.REI.2"],
"duration": 50,
"difficulty": "intermediate",
"students": ["student-1", "student-2"]
}
Response:
typescript
{
"planId": "plan-uuid",
"title": "Solving Systems of Linear Equations",
"standards": ["CCSS.MATH.9-12.A.REI.1", "CCSS.MATH.9-12.A.REI.2"],
"duration": 50,
"objectives": [
{
"bloomLevel": "apply",
"description": "Students will solve systems of equations using substitution",
"measurableOutcome": "Solve 3 of 4 systems correctly"
}
],
"activities": [
{
"title": "Hook - Real World Problem",
"type": "direct_instruction",
"duration": 10,
"instructions": "Present ticket pricing problem..."
}
],
"aiGenerated": true,
"confidence": 0.92
}
AI Grading Suggestions:
bash
POST /lms/ai/grading-suggest
{
"submissionId": "submission-uuid",
"rubricId": "rubric-uuid"
}
Response:
typescript
{
"submissionId": "submission-uuid",
"suggestedScore": 88,
"suggestedFeedback": "Strong thesis with clear evidence. Consider elaborating on the counterargument...",
"confidence": "high",
"rubricAlignment": [
{ "criterion": "Thesis", "level": "Proficient", "justification": "Clear but could be more specific" }
],
"needsHumanReview": false
}
Quick Reference
Gradebook API:
```bash # Grade assignment POST /lms/gradebook/grades
# Get student status GET /lms/gradebook/students/{id}/status
# What-if scenario POST /lms/grades/whatif
# Required grade POST /lms/grades/required-grade ```
Standards API:
```bash # Import standards POST /lms/standards/import
# Student heatmap GET /lms/standards/heatmap/students/{id}
# Class heatmap GET /lms/standards/heatmap/class
# At-risk students GET /lms/standards/heatmap/at-risk ```
AI API:
```bash # Lesson plan POST /lms/ai/lesson-plan
# Grading suggestion POST /lms/ai/grading-suggest
# Learning gap analysis POST /lms/ai/learning-gaps ```
Accessibility API:
```bash # Get settings GET /lms/accessibility/settings/{userId}
# Update settings POST /lms/accessibility/settings ```
Parent Notifications:
```bash # Get notification settings GET /lms/notifications/settings
# Update settings POST /lms/notifications/settings ```
FAQ
Aeion's AI Teaching Assistant analyzes the student submission against the rubric and returns a suggested score, feedback, and confidence tier. High confidence auto-applies; medium presents for teacher review; low or `needs_review` always requires a teacher decision.
Grade Prediction reads the current grade breakdown, substitutes hypothetical assignments with projected scores, recalculates the weighted total, and determines whether the target grade is achievable given remaining work.
Assessment data is aggregated per standard, mastery percentage is calculated per student, mapped to a traffic-light level (not_started → advanced), colored, and combined into class aggregates with trend indicators (improving / declining / stable).
TTS with voice/speed control, dyslexia mode (OpenDyslexic font + adjusted spacing), 5 color-blind filters (none / deuteranopia / protanopia / tritanopia / achromatopsia), font / line-height / word / letter spacing, reduce-motion, screen-reader optimization, auto-captions and transcripts.
Triggers on grade-below-threshold, absence, and upcoming-assignment events. Delivers via email / SMS / push with per-family language preference and optional digest mode (daily summary). Supports emergency broadcast.
Generates the code verifier and state, redirects to the provider, exchanges the authorization code for tokens, and stores the tokens encrypted at rest with per-tenant keys. Tokens auto-refresh.
not_started → gray (#9CA3AF), beginning → red (#EF4444), developing → orange (#F97316), approaching → yellow (#EAB308), mastered → green (#22C55E), advanced → emerald (#6EE7B7).
You provide standards and duration. The AI generates Bloom's-taxonomy objectives, materials, activities differentiated by learner type, and formative assessments — with a confidence score on the output.
Aeion calculates your current grade, the points remaining on outstanding assignments, and the maximum possible grade. From the target letter grade you specify, it computes the minimum score you need on remaining work and flags whether that's achievable.
Excused and unexcused absences are tracked separately. When a student crosses the configurable threshold (default 10%), an alert fires and the absence is included in state reporting exports.
The AI Teaching Assistant analyzes learning gaps and recommends one of six strategies — acceleration, remediation, scaffolding, alternative format, peer support, or one-on-one — mapped to the specific students who would benefit.
Google Classroom, Canvas, Blackboard, Moodle, Schoology, Clever, PowerSchool, Infinite Campus, LTI 1.3, REST API, custom.
Pick from common standard sets (Common Core, NGSS, state-specific) or import a custom standards file. Standards are associated with courses and grade levels; mastery is tracked per assessment.
high → auto-apply, medium → present for review, low → flag for careful review, needs_review → always require teacher decision.
The analytics engine combines attendance, engagement, performance, and behavior indicators into a weighted risk score and returns the risk level (low / medium / high / critical) with recommended actions.