Multimodal Generative AI for Intelligent Education: Personalized Content Creation on Edge Devices
Keywords:
multimodal generative AI; intelligent education; edge computing; personalized learning; system architecture; governance; privacy; sustainabilityAbstract
The convergence of multimodal generative artificial intelligence and edge computing is opening a new phase in intelligent education, in which personalized learning materials can be produced directly on student and classroom devices. This paper presents a system-level investigation of that shift. It examines architectural foundations, including transformer-based multimodal encoders, diffusion-based image generation, and compressed on-device inference, alongside edge infrastructure requirements. The analysis moves beyond single-model performance to consider end-to-end trade-offs in latency, energy consumption, privacy preservation, personalization quality, robustness, and equity. It argues that edge-native generative education systems must be designed as socio-technical infrastructures rather than as isolated model deployments. Governance, curriculum alignment, data minimization, model auditing, and sustainable operation are as important as compression ratios and inference speed. Through cross-domain comparisons with cloud-centric generative services, the paper identifies structural tensions between personalization and student autonomy, between adaptive content generation and pedagogical oversight, and between local inference benefits and heterogeneous device capabilities. It further discusses fairness implications across under-resourced schools and regulatory considerations for minors. Future directions include modular teacher-in-the-loop generation, federated adaptation without raw data transfer, verifiable educational quality guarantees, and energy-aware scheduling. The paper contributes a framework for evaluating edge-native multimodal generative systems in education that integrates technical performance, governance, and long-term institutional sustainability.
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