人工智能驱动的可穿戴设备和学生福祉的大型语言模型:初步研究
Arfan Ahmed1, Sarah Aziz1, Alaa Abd-Alrazaq1
1AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
Studies in health technology and informatics
|April 9, 2025
概括
大型语言模型 (LLM) 和可穿戴技术可以个性化学生支持. 这种整合提供了比传统方法更深入地了解幸福感和学业成绩.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 学生福利 学生福利
背景情况:
- 评估学生福祉和学业成绩的传统方法往往是有限的.
- 越来越需要个性化干预措施来有效支持学生.
- 可穿戴技术产生丰富的数据流,可以为学生提供支持.
研究的目的:
- 探索大型语言模型 (LLM) 与可穿戴技术数据的整合.
- 为提高学生福祉和学业绩提供个性化的建议.
- 评估LLMs在分析学生数据以获得可操作的见解方面的有效性.
主要方法:
- 收集了各种学生数据,包括可穿戴设备指标和学术报告反.
- 使用大型语言模型 (LLM) 来分析学生定性数据的情绪.
- 分析学生数据档案,以确定与情绪状态和参与相关的模式.
主要成果:
- LLM 在处理和分析学术报告中的文本数据方面表现出有效性.
- 情感分析为学生的情绪状态和参与度提供了洞察力.
- 综合方法提供了对学生个体需求的更细致的理解.
结论:
- 与可穿戴技术相结合的LLM显示了个性化学生支持的巨大潜力.
- 这种方法可以通过数据驱动的见解来提高学生的福祉和学业表现.
- 基于LLM的分析为传统的学生评估方法提供了更复杂的替代方案.
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