# Designing a Generalist Education AI Framework for Multimodal Learning and Ethical Data Governance
## 3 Materials and Methods
### GEAI Model  
#### 3.1.1 MM Devices for Secure Educational Data Collection  
The integration of AI technologies in education faces major challenges in data availability, privacy protection, and regulatory compliance. High-quality real-world data for instructional analysis remains scarce due to the high cost and limited scalability of expert classroom observations [5], while audiovisual recordings are often restricted by legal and ethical concerns. To address these constraints, the GEAI framework introduces Multimedia Registration Devices (MM Devices)--specialized hardware terminals designed to operate within a Trusted Domain and securely collect multimodal classroom data, providing a privacy-preserving foundation for educational analytics. These devices are currently at the conceptual stage, pending prototype development and validation.  
MM Devices are intended for deployment in both physical and virtual classrooms, capable of capturing diverse data modalities such as video, audio, and handwriting for potential applications in facial expression analysis, speech recognition, and response evaluation. Initial configurations propose low-resolution sensors (e.g., 720p cameras, 8 kHz microphones) as cost-effective baselines, scalable to higher specifications (e.g., 1080p, 16 kHz) as needed. Drawing on best practices from the payment industry, each device will integrate a Hardware Security Module (HSM)--adapted from Point-of-Sale (POS) systems--to manage cryptographic keys and ensure secure content encryption in compliance with PCI P2 PE guidelines [32; 33].  
To support local AI processing and reduce dependency on cloud infrastructure, MM Devices are envisioned to incorporate lightweight edge AI models, such as MobileNet for video processing and Whisper-tiny for speech transcription. The projected unit cost is approximately USD 500, based on market trends in embedded AI devices (e.g., NVIDIA Jetson Nano) and HSM-integrated platforms [34]. Final costs and performance benchmarks will be established during prototyping.  
To mitigate physical and cybersecurity threats, MM Devices will adopt a layered security architecture, including secure execution environments, app-level access controls, and HSM-based identity verification. The hardware design emphasizes portability, with battery support (5-10 h), modular sensor slots, and operational LED indicators. Software components--such as tutoring agents, behavior classifiers, or encoders--will operate in sandboxed environments, subject to formal auditing, approval, and version control. Data transmission will be limited to registered institutional GEAI platforms over secure channels, avoiding exposure to unsecured networks (e.g., public Wi-Fi), and employing encryption protocols aligned with PCI P2 PE.  
To enhance system accountability, the design also envisions blockchain-based logging, implemented through a lightweight private blockchain (e.g., Proof of Authority) that records local, tamper-proof logs of device operations and compliance status. These logs will be periodically synchronized to the GEAI platform's distributed ledger, enabling real-time auditing and traceability [35; 36]. The feasibility of this approach depends on optimizing energy consumption and computing overhead during prototype validation. A summary of these layered security strategies is presented in Table 1. Ultimately, MM Devices aim to provide a scalable infrastructure for secure, privacy-compliant, and personalized learning, with benefits such as improved student engagement and adaptive support to be evaluated in future trials [37].