一个能看图识文的教育智能助手，一模型搞定多种学习任务。
UniEDU: A Unified Language and Vision Assistant for Education Applications
- 统一语言与视觉输入，单模型处理多类教育任务。
- 效率提升300%，计算开销大幅降低，性能几乎不下降。
- 适合实际教学场景，可部署于各类学习系统中。
面向K-12教育材料常包含文本与图像等多模态信息，传统模型难以全面理解其中的细微内容。本文提出统一语言与视觉助手UniEDU，适用于知识推荐、知识追踪、时间成本预测和用户答案预测等多种教育应用，且所有任务均在单一模型中完成。相比专用模型，UniEDU具备更强泛化能力，适应真实多样化的学习环境。此外，通过优化设计，其计算开销显著降低，实现约300%的效率提升，性能仅比全微调模型略有下降，具备工业级部署潜力。本工作为构建满足教育多样化需求的通用AI系统迈出关键一步。
原文摘要 · Abstract (English)
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.
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