将多模式多任务联合基金会模式引入教育领域:前景和挑战
Kasra Borazjani1, Naji Khosravan2, Rajeev Sahay3
1Department of Electrical Engineering, University at Buffalo-SUNY, Buffalo, NY, United States.
Frontiers in artificial intelligence
|December 1, 2025
概括
我们介绍了多模多任务联合基础模型 (M3T FedFMs),用于教育中的隐私保护人工智能. 这种方法可以实现跨机构的协作培训,增强个性化和公平性,同时解决数据孤岛问题.
科学领域:
- 教育中的人工智能
- 机器学习 机器学习
- 联邦学习学习 (Federated Learning) 是一种学习方式.
背景情况:
- 多模多任务 (M3T) 基础模型 (FMs) 显示出AI在教育中的前景.
- 部署受到隐私问题,数据孤岛和稀缺的域特定数据的限制.
研究的目的:
- 介绍教育的M3T联合基金模型 (FedFMs).
- 探索M3T FedFMs的潜力和未来研究方向.
- 在智能教育系统中解决隐私,个性化和公平问题.
主要方法:
- 整合联合学习 (FL) 与M3T FMs.
- 在分散的机构中实现协作,保护隐私的培训.
- 适应各种数据模式和教育任务.
主要成果:
- 通过将学生数据保持在本地,M3T FedFM 增强了隐私.
- 模块化架构为学生,教师和机构提供了个性化的模型.
- 通过允许资源有限的实体参与,促进公平和包容性.
结论:
- M3T FedFMs为下一代智能教育提供了一个有希望的方法.
- 关键的研究挑战包括数据异质性,失学,持续学习和可解释性.
- 解决这些挑战对于教育环境中的实际部署至关重要.
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