A state university system with 45,000 students had a first-year failure rate of 22% in gateway STEM courses. Instructors teaching 200+ student lectures couldn't identify struggling students until after midterm exams, by which point recovery was difficult.

Tutoring center capacity served only 8% of students who needed help. Advanced students spent class time on material they'd already mastered. The learning management system delivered identical content sequences to every student regardless of preparation level or learning style.

The university needed adaptive learning technology that could personalize at scale without increasing faculty workload.

The Solution

Agents that assess, plan, tutor, track, and intervene for every student individually

Vijan.AI deployed 5 agents integrated with the LMS. The Assessment agent evaluates knowledge gaps through adaptive pre-tests and ongoing formative assessments. The Curriculum Planner selects and sequences content from 10,000+ resources based on each student's gaps, pace, and learning style. The Tutor agent provides on-demand explanations, worked examples, and practice problems using LLM trained on course materials. The Progress Tracker monitors mastery of each learning objective in real-time. The Intervention agent alerts instructors when students show signs of disengagement or falling behind, with specific recommendations for support.

Autonomous Agents

How each agent reasons, decides, and acts

Step 1 · Content

Student Content Agent

Adaptive Content Personalization

Analyzes individual learning styles, pace, and knowledge gaps to curate personalized content from vast libraries, autonomously adjusting difficulty levels and modalities while routing struggling students through diamond pattern.

Input

Student interaction data with quiz scores, time-on-task, and engagement metrics

Output

Personalized learning playlists with adaptive content recommendations

Calls LMS API to retrieve student performance history, preferred learning modalities (video, text, interactive)
Queries content library to match knowledge gaps with remedial materials and enrichment resources
Autonomous decision: recommend foundational review, advance to next concept, or provide alternative explanation
Routes at-risk patterns to Course Guidance and Student Segmentation for multi-angle intervention via diamond merge

Step 2 · Guidance

Course Guidance Agent

Intelligent Course Pathway Guidance

Recommends optimal course sequences based on career goals, prerequisite mastery, and historical success rates, autonomously adjusting pathways as student progress evolves and feeding insights to success hub.

Input

Student transcripts with completed courses, grades, and declared majors

Output

Personalized degree plans with course recommendations and timeline

Queries curriculum database to validate prerequisite chains and identify optimal course sequences
Invokes pathway engine using collaborative filtering to recommend electives based on similar successful students
Autonomous decision: suggest accelerated track, remedial support courses, or alternative major exploration
Contributes course performance predictions to Student Success Hub for holistic intervention planning

Step 3 · Segmentation

Student Segmentation Agent

Predictive Student Cohort Analysis

Segments students into cohorts using machine learning clustering on engagement, performance, and demographic attributes, autonomously identifying at-risk groups and merging insights for targeted support.

Input

Multi-dimensional student data including attendance, grades, and socioeconomic indicators

Output

Student cohort assignments with risk scores and intervention recommendations

Calls analytics platform to compute feature vectors from academic, behavioral, and demographic data
Executes ML clustering algorithm to identify cohorts such as high-achievers, at-risk, re-engaged learners
Autonomous decision: assign cohort-specific resources, flag for counselor review, or enroll in success programs
Merges cohort risk profiles with content and guidance data at Student Success Hub for comprehensive action

Step 4 · Support

Student Helpdesk Agent

24/7 Intelligent Student Support

Provides instant answers to student inquiries using AI chatbot with knowledge base search, autonomously resolving common questions and escalating complex issues to intervention specialists at the merge point.

Input

Student questions via chat, email, or mobile app

Output

Resolved inquiries with answers, resources, or escalation tickets

Invokes chatbot NLU engine to interpret student intent and match to FAQ database
Calls knowledge base search to retrieve relevant articles, video tutorials, and policy documents
Autonomous decision: provide self-service answer, connect to live support, or create case for advisor
Escalates unresolved academic concerns to Student Success Hub for triage alongside risk and content data

Step 5 · Triage

Escalation Triage Agent

Proactive Student Intervention

Triages merged insights from content, guidance, segmentation, and support to prioritize high-impact interventions, autonomously assigning advisors and tracking outcomes for continuous learning analytics feedback.

Input

Consolidated student profiles from all upstream agents at success hub merge

Output

Prioritized intervention plans with advisor assignments and success tracking

Calls risk scoring model to rank students by urgency combining academic, engagement, and support signals
Queries CRM system to assign appropriate advisor based on caseload, expertise, and student needs
Autonomous decision: schedule one-on-one meeting, enroll in tutoring, or refer to counseling services
Feeds intervention outcomes back to all agents via learning analytics to refine personalization algorithms

Results

Measurable impact within 90 days of deployment

35%

Better Outcomes

Course pass rates in gateway STEM courses improved from 78% to 91%. Average grades increased by one letter grade.

60%

Fewer Failures

First-year failure rate in STEM reduced from 22% to 8.8%. At-risk students identified within first 2 weeks.

24/7

Tutoring Access

AI tutor available around the clock. Average student interaction: 45 minutes/week, replacing unmet tutoring demand.

4.3/5

Student Satisfaction

Student satisfaction with learning experience improved from 3.1 to 4.3 out of 5.

Implementation

From pilot to production in 12 weeks

Week 1-4

Agent Design & Tool Integration

Defined agent capabilities, connected ML model, rules engine, graph DB, and chargeback API tools. Configured orchestrator routing logic.

Week 5-8

Shadow Mode & Autonomous Tuning

Agents ran in shadow mode on 10% of transactions. Tuned decision thresholds, tool call parameters, and feedback loop retraining frequency.

Week 9-12

Full Autonomous Deployment

Production rollout across all channels. Agents operating fully autonomously with human-in-the-loop for critical escalations only.

Ready to deploy autonomous agents for your use case?

Let's design an agentic AI solution tailored to your organization's workflows.

Get in touch Browse All Agents