Education materials for K-12 students often consist of multiple modalities, such as text and images, posing challenges for models to fully understand nuanced information in these materials. In this paper, we propose a unified language and vision assistant UniEDU designed for various educational applications, including knowledge recommendation, knowledge tracing, time cost prediction, and user answer prediction, all within a single model. Unlike conventional task-specific models, UniEDU offers a unified solution that excels across multiple educational tasks while maintaining strong generalization capabilities. Its adaptability makes it well-suited for real-world deployment in diverse learning environments. Furthermore, UniEDU is optimized for industry-scale deployment by significantly reducing computational overhead-achieving approximately a 300\% increase in efficiency-while maintaining competitive performance with minimal degradation compared to fully fine-tuned models. This work represents a significant step toward creating versatile AI systems tailored to the evolving demands of education.

翻译：K-12 教育材料通常包含文本和图像等多种模态，这给模型充分理解材料中的细微信息带来了挑战。本文提出了一种统一语言与视觉助手 UniEDU，专为多种教育应用而设计，包括知识推荐、知识追踪、时间成本预测和用户答案预测，所有这些功能均集成于单一模型中。与传统的任务专用模型不同，UniEDU 提供了一种统一的解决方案，在多种教育任务上均表现出色，同时保持了强大的泛化能力。其适应性使其非常适合在不同学习环境中进行实际部署。此外，UniEDU 针对工业级部署进行了优化，通过显著降低计算开销——效率提升约 300%——同时保持与完全微调模型相比具有竞争力的性能，且性能下降极小。这项工作代表了为适应教育领域不断变化的需求而创建多功能人工智能系统的重要一步。