AcademicGuard
Executive Summary
Vision Statement
To restore trust in online education by making cheating technically and economically unviable through continuous innovation in detection technology.
Problem Summary
Educators face escalating cheating challenges with AI-powered tools like Evadus, which bypass traditional proctoring systems like Respondus and Turnitin. These tools create invisible overlays, use AI for answer generation, and employ polymorphic code to evade detection[1][4]. Current solutions struggle to address advanced cheating methods, leaving institutions vulnerable to academic dishonesty despite existing monitoring tools.
Proposed Solution
An integrated anti-cheating platform combining real-time monitoring, AI detection, and secure assessment environments. Features include:
- Advanced AI content analysis to detect synthetic text
- Multi-layered detection for overlay tools like Evadus
- Integration with existing LMS platforms
- Compliance support for academic policies[4][5]
Market Analysis
Target Audience
Higher education institutions and online course providers using proctored assessments. Key personas:
- Academic Administrators: Responsible for maintaining institutional integrity
- Proctors: Overburdened with manual monitoring tasks
- Tech-Savvy Students: Seeking ways to bypass existing systems
Niche Validation
Strong validation from Reddit discussion showing growing demand for better solutions. Current tools (Respondus, Turnitin) fail to address advanced methods like Evadus overlays and AI-generated content[1][5]. Market expansion driven by shift to online learning.
Google Trends Keywords
Market Size Estimation
Institutions using proctored assessments (~30% of TAM)
Early adopters in tech-forward universities (5-10% of SAM)
Global e-learning market projected at $325B by 2025[5]
Competitive Landscape
Key competitors include Turnitin (content checking), Respondus (proctoring), and emerging solutions like GPTZero. AcademicGuard differentiates through multi-layered detection combining AI analysis with behavioral monitoring[1][5].
Product Requirements
User Stories
As a proctor, I want real-time alerts when suspicious behavior is detected during exams so I can intervene promptly
As an administrator, I need automated reports of cheating incidents to support academic policy enforcement
MVP Feature Set
AI-powered text analysis for synthetic content detection
Overlay detection system for tools like Evadus
Integration with Respondus lockdown browser
Compliance reporting engine
Non-Functional Requirements
Low-latency monitoring (<1s response time)
GDPR/CCPA compliance for student data
Scalability to support 10k+ concurrent users
Key Performance Indicators
Detection rate of known cheating tools
Adoption rate among target institutions
Reduction in academic dishonesty reports
Data Visualizations
Visual Analysis Summary
The following charts illustrate market trends, competitor comparison, and potential adoption rates.
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Go-to-Market Strategy
Core Marketing Message
Stop Cheating Before It Starts: AcademicGuard combines AI detection and behavioral analysis to protect your institution's academic integrity.
Initial Launch Channels
- Academic Conferences: Sponsor sessions at EDUCAUSE and similar events
- Proctoring Partnerships: Integrate with Respondus/Turnitin ecosystems
- AI Education Communities: Engage on r/MachineLearning and AI ethics forums
Strategic Metrics
Problem Urgency
Critical
Solution Complexity
High
Defensibility Moat
Proprietary AI models trained on cheating patterns + continuous updates against new threats
Source Post Metrics
Business Strategy
Monetization Strategy
Tiered SaaS pricing:
- Basic: $5/student/year (content checking)
- Pro: $10/student/year (full monitoring)
- Enterprise: Custom pricing with API access
Financial Projections
Target 1,000 institutions in first 3 years → $5M-$10M MRR
Tech Stack
Python with FastAPI for AI processing and Node.js for WebSocket-based monitoring
PostgreSQL for user data + Redis for real-time session tracking
Next.js for responsive dashboards and real-time monitoring UIs
OpenAI API (content analysis), AWS Rekognition (visual monitoring), LTI integration with Canvas/Moodle
Risk Assessment
Identified Risks
- Evolving Cheating Methods: New tools emerge faster than detection capabilities
- Privacy Concerns: Real-time monitoring raises compliance issues
Mitigation Strategy
- Continuous R&D: Dedicated team monitoring dark web for new threats
- Transparent Policies: Clear data usage agreements and anonymization features