# Designing a Generalist Education AI Framework for Multimodal Learning and Ethical Data Governance
## 4 Results
### Use Case 2: AI-Assisted Secure Assessments and Cheating Prevention  
**Educational Challenge:** In digital learning environments, traditional assessment methods face escalating challenges, including AI-assisted plagiarism (e.g., ChatGPT misuse), identity fraud in remote testing, algorithmic bias in grading, and data security risks from cloud-based storage. These issues undermine academic integrity and fairness while amplifying systemic inequalities in assessment outcomes [55; 58]. Addressing these issues requires secure, explainable, and ethically governed AI solutions.  
**GEAI-Enabled Solution:** The GEAI framework proposes a Trusted Domain infrastructure that leverages MM Devices and modular AI components to ensure secure, equitable, and tamper-proof assessments. The system aligns with regulatory frameworks (e.g., GDPR, FERPA) and educational standards for fairness, explainability, and data integrity [22].  
**Workflow and Capabilities:**  
* AI-Powered Identity Verification and Authentication: MM Devices support real-time biometric authentication using pre-exam facial recognition (e.g., 95% accuracy per NIST standards, NIST, 2023), continuous face tracking (up to 1080p video at 30 fps), voice verification (up to 16 kHz), and keystroke dynamics to validate identity. Behavioral anomaly detection models track eye movement (e.g., sustained gaze aversion > 5 s) and sudden keystroke pattern shifts to flag suspicious behavior.  
Outcome: Secures assessment participation and eliminates identity fraud.
* AI-Driven Exam Integrity Monitoring and Proctoring: Real-time audio and screen analysis detects unauthorized behaviors such as whispering, conversation cues, or tab switching. Deepfake detection algorithms--based on CNN architectures--distinguish live video from pre-recorded footage, ensuring authenticity [58].  
Outcome: Preserves assessment credibility and reduces academic dishonesty.
* Automated Grading with Fairness Controls: GEAI incorporates OCR and a BERT-based NLP engine to interpret handwritten and typed responses against structured rubrics (e.g., \"Score 8/10 for explanation with missing term\"). An explainable AI (XAI) layer enables transparency in automated scoring (e.g., \"Score reduced due to incomplete diagram\"), while fairness adjustments are informed by teacher review [22]. Grading outputs are recorded immutably using blockchain ledgers (e.g., Hyperledger Fabric), ensuring tamper-proof verification [35].  
Outcome: Enables verifiable, bias-mitigated grading aligned with educational fairness principles.  
* Secure Data Processing and Compliance Assurance: Assessment data are processed locally on MM Devices and within institutional Trusted Domains using AES-256 encryption, avoiding third-party risks. Data control resides with the institution, ensuring full alignment with GDPR/FERPA mandates and preventing unauthorized access.  
Outcome: Guarantees privacy and institutional sovereignty in assessment workflows.  
**Educational Impact:** GEAI strengthens academic integrity by integrating biometric authentication, behavioral monitoring, explainable AI grading, and blockchain-secured outcomes. These measures reduce cheating, mitigate grading bias, and uphold data privacy, directly supporting equitable and high-quality learning outcomes [22; 58].  
Beyond technical features, the system reflects a principled approach to AI governance in education. As highlighted by [PERSON] and [PERSON] [59], responsible AIED frameworks should be built on five pillars: (1) provenance transparency for AI models; (2) legal legitim of training datasets; (3) institutionalized ethical review procedures; (4) accountability of deploying entities; and (5) prioritization of learner benefit.  
GEAI's Trusted Domain design operationalizes these principles, establishing alignment with emerging global standards in ethical educational AI.