Synthetic Users in More Industries
Synthetic Student Applications: Strategic Plan 2025-2027
By Sherry Jones (September 2025)
Online education is poised to benefit enormously from synthetic students – AI-driven agents that emulate real learner behavior as a strategic tool for improving educational outcomes and operations.
Executive Summary
Digital Twin Students
AI personas calibrated to behave like real learners for personalized tutoring and 24/7 support
Agentic Testing
Synthetic users for LMS platform testing, content evaluation, and quality assurance
Synthetic Data
Privacy-preserving datasets for analytics, modeling, and intervention simulation
Unlike static scripted bots, modern synthetic students use advanced AI to engage in realistic, context-aware activities. They eliminate FERPA compliance concerns while enabling extensive experimentation without risking real students or data privacy.
The Privacy Advantage
FERPA-Compliant Innovation
Synthetic students eliminate concerns about student privacy, consent, or sensitive information leakage. Since these agents are entirely artificial, we can test new technologies, curricula, and support strategies rapidly and safely.
This privacy-first approach positions synthetic users as the ideal solution for educational experimentation and quality assurance without regulatory constraints.
Target Outcomes
15%
DFW Rate Reduction
Expected decrease in course failures through AI tutoring support
75%
Student Engagement
Usage rate achieved by AI tutors vs. 14% for traditional chatbots
99.9%
Platform Uptime
Target reliability through synthetic monitoring and testing
These initiatives directly target our priority outcomes: student retention, course completion rates, time-to-degree, equity gaps, advising capacity, and operational efficiency.
Digital Twin Students: Personalized AI Support
Digital twin students serve as AI personas calibrated to behave like real learners, providing personalized tutoring and always-available academic support. These AI "twins" can answer student questions, provide feedback, and guide learning in the style of human instructors.
At Clemson and Alabama State, 75% of students engaged with their professor's AI twin, and some classes saw average grades improve by a full letter grade after adoption.
24/7 AI Tutoring Success Stories
Praxis AI Implementation
Creates AI "professor twins" using Anthropic's Claude model, integrated into Canvas LMS. Professors train twins on their syllabus, materials, and teaching style.
Proven Results
75% student engagement rate vs. 14% for generic chatbots. Some classes improved from C to B average grades after AI tutor deployment.
Faculty Benefits
Offloads routine Q&A from faculty, enabling focus on high-value teaching and substantive student conversations.
Student Success Monitoring
Learning Fitness Tracker
James Cook University is developing AI "twins" for each student that act like learning fitness trackers, monitoring engagement and sending personalized nudges to keep learners on track.
This approach merges data from all systems to visualize each student's learning journey and provide real-time support, drastically reducing dropout rates.
Synthetic Collaborative Learning Partners
Introducing AI-driven collaborative learning partners: sophisticated synthetic students designed to seamlessly integrate into group projects and online learning environments, such as classroom simulations.
These AI agents act as fully-fledged collaborators, ensuring dynamic and productive group interactions. They are equipped with:
  • Realistic cognitive abilities
  • Diverse motivational patterns
  • Emulated nuanced affective responses and learning resistances (e.g., reluctance to share notes or participate actively in discussions)
Addressing Absent or Disengaged Students
A key application of synthetic partners is to address the challenge of absent or disengaged students in collaborative online classrooms. By stepping in as reliable collaborators, they maintain group momentum and learning continuity, preventing projects from stalling due to missing team members and upholding the integrity of the learning process.
Manifold Benefits
The benefits of integrating synthetic collaborative learning partners are significant:
  • Ensures all students gain valuable collaborative experience
  • Provides diverse perspectives to enrich discussions
  • Helps maintain a consistent learning pace for the entire group
This innovative approach fosters robust collaborative learning experiences, even in scenarios where peer participation might otherwise falter, ensuring equitable access to group work opportunities.
Program-Specific Applications
Business Programs
AI tutors run financial simulations and role-play ethical dilemmas. Queen Mary University used AI copilots to help business students generate SWOT analyses and analyze simulation data.
Education Programs
Digital twin student cohorts let teaching candidates practice differentiating instruction with AI "classes" of varied learners that react to lessons.
Health Programs
Digital twins combined with virtual patients create realistic clinical training scenarios. MedVR provides AI patient avatars for diagnosis practice with instant feedback.
IT Programs
AI student bots simulate coding assignments, generating synthetic student code submissions that mirror real student errors for testing auto-graders.
Agentic Testing for Platform Quality
Synthetic student agents behave like students en masse, allowing stress-testing and QA of new systems before real users are involved. These AI agents simulate thousands of students navigating our LMS and portals.
Major tech firms like Google and Meta already use AI user simulations to test new interfaces, accelerating release cycles while preserving student privacy.
LMS Load Testing Benefits
1
Continuous Monitoring
Bot students execute flows: log in, access courses, watch videos, submit assignments, post in forums
2
Performance Validation
Catch bottlenecks and bugs before rollout. Test hundreds of quiz submissions in minutes
3
Privacy Protection
No production accounts used, accelerated testing cycles without student data exposure
Portal and SIS Workflow Testing
Comprehensive Testing
Synthetic agents test course registration, graduation applications, and complex workflows like transfer credit processing or international student visa updates.
Diverse personas represent different student types (first-gen, part-time, international) to uncover usability issues affecting specific groups.
End-to-End System Integration
Synthetic users test integration points across our technology ecosystem. When a student registers in SIS and enrolls in courses through LMS, agents verify data flows correctly between systems.
01
Application Submission
Agent poses as prospective student, fills out application in CRM/Slate
02
Enrollment Processing
Verify data movement from CRM to SIS for enrolled synthetic student
03
Course Access
Confirm LMS shows correct courses on student dashboard
04
Data Integration
Validate data lake/warehouse and Neo4j relationship mapping updates
Content Quality Assurance
Synthetic student agents evaluate digital learning content including e-books, online courseware, and assessments. They act as virtual test learners to ensure materials are effective and accessible before real students use them.
E-Book Readability Testing
Content Comprehension
AI agents "read" through textbooks, attempt embedded quizzes, and highlight confusing sections that typical students might struggle with
Question Generation
Agents generate questions students might ask after each chapter, identifying unclear passages or overly complex concepts
Early Problem Detection
Catch content issues before deployment, similar to having a focus group test materials virtually
Interactive Content Evaluation
Simulation Testing
Synthetic agents test virtual labs, patient scenarios, and adaptive learning tools. They progress through IT networking labs or health course simulations step-by-step.
Millions of "Learnoid" synthetic students have been used to simulate progress under different curricula, stress-testing curriculum changes at scale.
Bias and Accessibility Auditing
Synthetic personas representing diverse demographics help identify content bias or differential impact. Different AI "students" with varied profiles test content accessibility and engagement.
Time-Constrained Learner
Working full-time with limited study hours, tests content pacing
ESL Student
Tests for idioms and complex language that may need clarification
Accessibility Needs
Visually impaired profile tests screen-reader compatibility and navigation
Synthetic Data Generation Foundation
Using tools like Synthetic Data Vault (SDV) with CTGAN models, we can generate artificial datasets that statistically mirror real students while ensuring complete privacy protection.
MIT researchers demonstrated that models trained on synthetic data showed "no significant difference" in predictive power compared to real data models.
Creating Synthetic Student Records
1
SIS Data Generation
Academic histories, course outcomes, progression paths using real data patterns
2
CRM Dataset Creation
Applicant and enrollment information for recruitment strategy modeling
3
Analytics Sandbox
Populate data lake for retention/DFW model building without FERPA concerns
LMS Clickstream Synthesis
Behavioral Calibration
Generate event logs of student clicks, submissions, forum posts matching statistical patterns of real usage. Unizin consortium built synthetic Canvas LMS data for safe analytics.
Digital twin agents behave more realistically when informed by actual student behavior probabilities derived from real interaction patterns.
Graph Data and Relationships
Generate synthetic networks of course prerequisites, co-enrollments, and social connections using Neo4j/Neptune graph databases.
Course Networks
Prerequisites and co-enrollment patterns
Study Groups
Student social connections and peer networks
Recommendations
Algorithm testing for course and study partner suggestions
Influence Analysis
How student dropout affects peer networks
Intervention Simulation
Create 1000 synthetic students with various risk profiles and simulate interventions like extra tutoring sessions or grading policy changes to predict outcomes.
These "what-if" simulations on synthetic populations guide decision-making by comparing outcomes under different policies or curricula without ethical concerns.
Technical Architecture Overview
Key Integration Points
1
Open edX (OEX) LMS
AI tutors via APIs/LTI modules, synthetic user scripts through REST API, sandbox instances for testing
2
SIS Integration
Synthetic student records in test databases, secure API access for digital twin advisors
3
CRM (Slate)
Synthetic applications via API, prospective student data modeling for recruitment strategies
4
Data Lake/Warehouse
SDV framework integration, utility assessment, privacy metrics reporting
5
Graph DB (Neo4j)
Relational structures, knowledge graphs for AI tutors, social network analysis
6
AI/LLM Infrastructure
Cloud LLM services with privacy-proxy layer, middleware for guardrails and context control
Privacy Safeguards
Rigorous Protection
No PII exposure to AI systems, differential privacy in training, contractual data isolation. Praxis AI keeps professor data in secure "vaults" never shared with LLM providers.
Synthetic datasets evaluated for re-identification risk with strict thresholds before deployment.
Bias and Fairness Controls
01
Diverse Synthetic Data
Generate demographically representative synthetic students to avoid majority-group bias
02
Output Testing
Test AI tutors with questions from different backgrounds, verify consistent help quality
03
Curated Content
Feed AI only professor materials and trusted sources, avoiding biased internet data
04
Human Oversight
Log all AI responses for instructor review and correction capabilities
Accuracy and Hallucination Prevention
Multi-layered guardrails reduce AI errors through closed-domain models, uncertainty responses, and verification tools.
Closed-Domain Training
Fine-tune on academic content only, prevent straying beyond known material
Uncertainty Handling
Enable "I don't know" responses when AI is uncertain about answers
Verification Tools
Double-check AI answers against source databases when possible
Security and Access Control
Secure Deployment
AI tutors run in authenticated LMS contexts, synthetic testers use limited-permission accounts, rate limiting prevents system overload.
All interactions logged with timestamps for auditability, no real student data exposure to AI agents.
Human-in-the-Loop Approach
Gradual rollout with human oversight ensures quality and builds trust. Synthetic students complement, not replace, human processes.
Pilot Testing
Start with opt-in beta courses, faculty monitoring, intervention capability for incorrect answers
Performance Validation
Require 95% accuracy threshold before scaling, domain expert review of synthetic data models
Scaled Deployment
Expand only after proven consistent performance, maintain human decision authority
Impact on Student Retention
AI tutor twins offer academic help on demand, preventing frustration and withdrawal. Clemson's pilot showed 75% student engagement with AI support, correlating with improved retention.
Student digital twin analytics identify at-risk students weeks before human advisors might notice warning signs, enabling proactive interventions.
DFW Rate Reduction Strategy
1
24/7 Support
AI tutors clarify misconceptions before they snowball into failure
2
Content Optimization
Synthetic testing catches confusing materials early
3
Targeted Interventions
Predictive models identify students likely to get D/F grades
Classes with AI tutor access saw average grades improve from C to B, implying significant DFW reduction potential.
Time-to-Degree Improvements
Pathway Optimization
Better course pass rates mean fewer repeats and faster progress. AI advisors help students make optimal course choices and maintain momentum.
Synthetic simulations identify curriculum inefficiencies that cause delays, enabling proactive pathway improvements.
Closing Equity Gaps
AI support is scalable and available to all, particularly helping those without equal access to human help. Working adult learners studying at night benefit from always-available AI tutors.
75%
Higher Engagement
AI tutor usage vs. traditional tools, including underserved students
24/7
Availability
Support access regardless of student schedule or location
100%
Equal Access
All students receive same quality AI assistance
Enhanced Advising Capacity
AI assistance allows advising teams to handle more students effectively. Routine questions answered by AI advisors free human advisors for complex mentoring.
If AI answers 50% of common queries, advisors gain significant time for high-value interactions and improved student satisfaction.
Platform Quality and Uptime
24/7 Monitoring
Synthetic users catch issues before they affect real students
Proactive QA
Prevent outages during peak periods like registration
UX Enhancement
Identify and fix user experience issues early
Target >99.9% uptime with reduced helpdesk tickets for technical issues.
Cost-to-Serve Optimization
Operational Efficiency
Automation of testing and support reduces labor-intensive tasks. Synthetic QA saves development costs by preventing post-release fixes.
AI tutors and advisors enable scaling support without linear payroll increases, especially valuable for off-hours student needs and enrollment growth.
Pilot Implementation Roadmap
Five strategic pilot projects over 6-18 months will validate synthetic student concepts across different applications and build institutional confidence.
1
Q4 2025
Pilots 2 & 4: Synthetic QA Testing and Data Warehouse Implementation begin
2
Q1 2026
Pilots 1 & 3: AI Tutor Twin and E-Book Evaluation launch
3
Q2 2026
Pilot 5: Student Success Digital Twin & Nudge System begins
Pilot 1: AI Tutor Twin for Business Ethics
Implementation Details
Timeline: Q1-Q2 2026 (1-term pilot)
Scope: Deploy AI digital twin of Business Ethics instructor as 24/7 course assistant, integrated into OEX LMS
Success Metrics: Student engagement rates, grade improvements, satisfaction scores
Pilot 2: Synthetic Collaborative Learning Partners
Implementation Details
Timeline: Q4 2025 - Q1 2026
Scope: Deploy AI students as stand-in collaborators in project-based courses and collaborative learning environments.
01
AI Collaborators
Deploy synthetic agents designed with realistic cognitive abilities, motivational patterns, and learning resistances.
02
Team Integration
Integrate AI students to replace missing team members, ensuring learning continuity in online collaborative classrooms.
03
Success Metrics
Measure improvements in group dynamics, learning continuity, and team engagement when synthetic partners are utilized.
Pilot 3: E-Book Content Evaluation
Focus: Business Ethics e-text and case study module evaluation
AI student persona will "read" materials, attempt quizzes, and provide feedback on unclear content. Instructional designers use findings to improve content clarity and accessibility.
Timeline: Q1-Q2 2026 (prior to next course offering)
Pilot 4: Synthetic Data Warehouse Implementation
Data Generation
Build synthetic student records, clickstream logs, survey responses using SDV/CTGAN
Model Development
Create predictive model for course dropout risk as proof-of-concept
Validation
Compare synthetic vs. real data model performance
Timeline: Q4 2025 - Q2 2026
Pilot 5: Student Success Digital Twin & Nudges
Personalized Interventions
System aggregates student data into digital twin profiles and sends automated personalized nudges based on engagement patterns.
Example: Low LMS activity triggers study reminders; poor quiz scores suggest tutoring
Timeline: Q2 2026 - Q1 2027
Pilot Success Metrics
75%
Target Engagement
Student usage rate for AI tutors based on proven results
10-15%
DFW Reduction
Expected decrease in failure rates in pilot courses
95%
Accuracy Threshold
Required AI performance before scaling deployment
100
Pilot Students
Initial cohort size for nudge system testing
Implementation Team Structure
IT Leadership
Technical architecture, system integration, security implementation
Institutional Research
Data analysis, synthetic data validation, outcome measurement
Academic Affairs
Curriculum integration, faculty training, pedagogical oversight
Instructional Design
Content evaluation, AI tutor training, learning experience optimization
Quality Assurance
Testing protocols, synthetic user scripting, platform monitoring
Technology Stack Decisions
Core Components
  • SDV/CTGAN for synthetic data generation
  • Cloud LLM services with privacy-proxy layer
  • Neo4j/Neptune for graph relationships
  • Open edX LTI integration for AI tutors
Faculty and Staff Training
Comprehensive training ensures successful adoption and addresses concerns about AI integration in education.
1
AI Capabilities Workshop
Understanding synthetic student technology, limitations, and appropriate use cases
2
Tutor Customization
Training professors to configure AI twins with course materials and teaching style
3
Monitoring and Correction
Using dashboards to review AI responses and make adjustments
4
Ethics and Transparency
Ensuring students know they're interacting with AI, maintaining academic integrity
Student Communication Strategy
Clear communication builds trust and ensures appropriate use of AI tools. Students must understand they're interacting with AI assistants, not human faculty.
Usage policies will guide appropriate interactions: "AI tutors provide guidance, not direct assignment answers" to maintain academic integrity.
Risk Mitigation Timeline
Pre-Pilot
Legal/IRB review, privacy assessment, bias testing protocols
Pilot Phase
Continuous monitoring, human oversight, performance validation
Scale Decision
Board review of pilot results, risk assessment, expansion approval
Expected ROI and Benefits
50%
Query Automation
Routine questions handled by AI, freeing advisor time
30%
Testing Efficiency
Faster QA cycles through synthetic user automation
25%
Content Development
Accelerated course material optimization
Beyond cost savings, improved student outcomes justify investment through higher retention and satisfaction.
Next Steps and Board Approval
Immediate Actions
Upon Board approval, assemble cross-functional implementation team, finalize technical platform selections, and develop detailed project plans for each pilot.
Begin stakeholder training and communication planning to ensure faculty and student buy-in for these innovative AI-assisted learning tools.
The Future of Online Education
Synthetic students represent a transformative opportunity to create smarter, more responsive learning environments where every interaction contributes to continuous improvement.
1
2
3
4
5
1
Innovation Leadership
2
Enhanced Student Success
3
Operational Excellence
4
Privacy-First Analytics
5
Ethical AI Foundation
With Board approval, we embark on an innovative journey toward AI-enhanced learning companions, robust tested systems, and data-driven decision making that elevates outcomes across Business, Education, Health, and IT programs.
References
Unizin (2024). Leveraging the Unizin Data Platform to Support Student Learning – on synthetic LMS data for safe analytics[28][45].
EdScoop – Wood, C. (2025). Professors’ AI twins loosen schedules, boost grades – on Praxis AI digital tutor results and guardrails[7][66].
JCU Australia (2025). Digital Twin Student: Personalising the student learning experience – on holistic student twin and nudges[18][19].
Nielsen Norman Group (2024). Synthetic Users: If, When, and How to Use AI-Generated Research – on using synthetic personas wisely[41][68].
MIT Sloan (2023). What is synthetic data – and how can it help you? – on SDV and no accuracy loss with synthetic data[43][44].
Qualz.ai (2025). Impact of Using Synthetic Users on Research – on synthetic users as dynamic agents[3][2].
[The full reference list from the attached document and additional sources is available upon request, ensuring all claims are substantiated by current research and industry examples.]
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