Designing a Generalist Education AI Framework for Multimodal Learning and Ethical Data Governance

作者：Yuyang Yan, Hui Liu, Baiyu Zhang, Toby Chau, Jiahui Li · 发表于：Applied Sciences · 年份：2025 · DOI：10.3390/app15147758 · 被引用次数：12 · 研究领域：Online Learning and Analytics、Big Data and Digital Economy、Innovative Teaching and Learning Methods

The integration of artificial intelligence (AI) into education requires frameworks that are not only technically robust but also ethically and pedagogically grounded. This paper proposes the Generalist Education Artificial Intelligence (GEAI) framework—a conceptual blueprint designed to enable privacy-preserving, personalized, and multimodal AI-supported learning in educational contexts. GEAI features a Trusted Domain architecture that supports secure, voluntary multimodal data collection via multimedia registration devices (MM Devices), edge-based AI inference, and institutional data sovereignty. Drawing on principles from constructivist pedagogy and regulatory standards such as GDPR and FERPA, GEAI supports adaptive feedback, engagement monitoring, and learner-centered interaction while addressing key challenges in ethical data governance, transparency, and accountability. To bridge theory and application, we outline a staged validation roadmap informed by technical feasibility assessments and stakeholder input. This roadmap lays the foundation for future prototyping and responsible deployment in real-world educational settings, positioning GEAI as a forward-looking contribution to both AI system design and education policy alignment.