利用人工智能探索学术界教师在金融素养方面的性别模式
A Ruban Christopher1, A R Nithya1
1School of Management, Hindustan Institute of Technology and Science, Chennai, India.
Frontiers in artificial intelligence
|October 10, 2025
概括
这项研究使用人工智能来分析学术界金融素养中的性别差距. 研究结果显示,男性拥有更高的投资知识和信心,差距受纪律和社会经济因素的影响.
科学领域:
- 经济学 经济学 经济学
- 社会学 社会学 社会学
- 计算机科学 计算机科学
背景情况:
- 金融素养对于经济稳定至关重要.
- 金融知识的性别差异在各个职业中仍然存在,包括学术界.
- 人工智能 (AI) 为分析这些差异提供了新的方法.
研究的目的:
- 探索人工智能的应用,以识别和分析高等教育教师在金融素养的基于性别的模式.
- 在学术界量化金融知识,态度和行为中的性别差异.
- 调查交叉因素 (学科,社会经济地位) 对这些差异的影响.
主要方法:
- 一种混合方法的方法,结合了调查和人工智能驱动的分析.
- 调查数据从300名学术专业人士收集了有关金融知识,态度,行为和人口统计的调查数据.
- 自然语言处理 (NLP) 和机器学习 (ML) 技术用于检测基于性别的语言和行为差异.
主要成果:
- 在财务素养方面观察到有统计学意义的性别差距,男性在投资知识和信心方面得分更高.
- 交叉分析显示,与人文学科相比,STEM领域的女性差距较小,受工资和照顾因素的影响.
- 人工智能驱动的情绪和集群分析强调了性别,学科和社会经济背景对金融素养的复合影响.
结论:
- 将人工智能与传统方法相结合,提高了学术界对性别和金融素养的研究.
- 建议采取情境敏感的干预措施,包括针对性别的培训和人工智能指导.
- 需要进行制度和政策改革,以解决所发现的差异,得到大学和政府机构的支持.
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