# Designing a Generalist Education AI Framework for Multimodal Learning and Ethical Data Governance
## 3 Materials and Methods  
In response to the challenges of transparency, data fragmentation, and regulatory compliance in current educational AI systems, we propose the Generalist Education AI (GEAI) framework as a conceptual blueprint. Grounded in a Trusted Domain architecture, this framework is designed to enable legally compliant, context-sensitive, and scalable AI deployment in real-world educational environments. It is important to note that the GEAI framework, as proposed in this study, is a conceptual architecture rather than a validated system. While it draws upon the design principles of emerging generalist AI models--such as their capacity to operate across modalities and tasks--it does not claim to deliver full generalist functionality at this stage. Instead, GEAI serves as a blueprint for guiding future prototyping and implementation in educational contexts, particularly in settings where multimodal AI capabilities and ethical data infrastructures are under development.  
As illustrated in Figure 1, GEAI consists of three tightly integrated domains that form a closed-loop pipeline:  
* (A) MM Device Domain: Handles secure and privacy-preserving multimodal data collection from teaching environments;
* (B) GEAI Resource Domain: Aggregates and preprocesses data to create structured learning resources and prepare training inputs for foundation models;
* (C) GEAI Model and Interaction Domain: Facilitates model training, personalization, and the delivery of intelligent educational services.  
The MM Device Domain (Domain A) is responsible for data acquisition within authentic educational scenarios. Multimedia registration devices (e.g., AI cameras, wearable sensors, tablets) are deployed in physical or virtual classrooms to capture multimodal inputs, including video, audio, and user interactions. These devices are embedded with tamper-resistant modules and local encryption capabilities. Device operation is managed through a secure key injection protocol, ensuring compliance with data protection regulations. All data collected in this domain is encrypted and securely transmitted to Domain B.  
The GEAI Resource Domain (Domain B) functions as the central data management and preprocessing layer. It consolidates heterogeneous educational data--such as performance logs, behavioral traces, and instructional content--and organizes them into a multimodal knowledge graph. This domain also enforces data legitimacy and diversity, preparing high-quality and ethically sourced training datasets for subsequent AI model development.  
The Model and Interaction Domain (Domain C) supports the pretraining, fine-tuning, and deployment of AI models tailored to educational tasks. These models provide automated tutoring, personalized feedback, and analytics. Processed outputs are delivered back to applications such as adaptive learning platforms or virtual assistants, while their usage data can optionally be looped back into the system via the MM Devices. This cyclical flow forms a continuous learning loop, enabling dynamic adaptation and ongoing performance optimization.  
Figure 1: Overview of the GEAI Framework. The figure depicts the architecture of the proposed GEAI framework, comprising three interconnected domains that together form a secure, adaptive, and auditable AI infrastructure for education. This closed-loop pipeline supports multimodal data acquisition, compliant processing, and personalized service delivery in classroom and online settings.