使用模糊集定性比较分析,对个性特征如何影响GAI学术不当行为行为的配置性探索
Haiying Liang1, Michael J Reiss2
1School of Foreign Languages, Peking University, Beijing, China.
BMC psychology
|February 20, 2026
概括
个性特征的组合,而不是单一的个性特征,预测了人工智能 (GAI) 在教育中的滥用. 低诚实 - 谦卑和良心,高黑暗三合一的特征,与学术不当行为相关.
科学领域:
- 心理学 心理学 心理学
- 教育技术的教育技术
- 伦理学 伦理学 伦理学
背景情况:
- 在高等教育中采用生成人工智能 (GAI) 提出了新的学术不当行为挑战.
- 之前的研究将人格与不道德行为联系起来,但GAI滥用组合的研究不足.
研究的目的:
- 检查个性特征配置如何影响学生涉及GAI的学术不当行为.
- 整合HEXACO模型和黑暗三位一体框架,用于分析GAI滥用.
主要方法:
- 利用了 864 名大学生数据的模糊集定性比较分析 (fsQCA).
- 研究了人格特征对GAI相关学术不当行为的配置效应.
主要成果:
- 没有一个个性特征能够预测GAI的不当行为.
- 高水平的不当行为与低水平的诚实 - 谦卑 / 良心和高水平的马基雅维利主义 / 精神病相关.
- 低的不当行为与高的诚实 - 谦卑 / 良心 / 可接受性和低的黑暗三合一特征相关.
结论:
- 个性特征协同相互作用,塑造道德的人工智能参与.
- 道德诚信和自我监管是防止不道德使用技术的关键保障.
- 调查结果为防止GAI学术不当行为提供了人格知情的伦理教育.
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