性别,知识和对人工智能的信任:一个基于课堂的随机实验
Antonio Dávila1, Ismail El Fassi2, Daniel Oyon1
1University of Lausanne, Lausanne, Switzerland.
Scientific reports
|November 20, 2025
概括
学生对人工智能 (AI) 的信任度低于同龄人,尤其是男性和知识博的人. 这种信任模式不管建议的准确性如何,都会持续存在,这突显了人工智能工具对用户感知的挑战.
科学领域:
- 教育技术的教育技术.
- 人与计算机的交互
- 行为经济学是一种行为经济学.
背景情况:
- 人工智能 (AI),特别是生成性AI,越来越多地用于教育和工作场所的实时协助.
- 由于AI幻觉等问题,用户对AI工具的信任是不确定的.
- 了解影响对人工智能与人类建议信任的因素至关重要.
研究的目的:
- 调查学生特征如何影响对人工智能产生的建议的信任,与同行建议相比.
- 用随机现场实验在教育环境中分析信任模式.
主要方法:
- 在一个本科课程中进行了一项随机现场实验.
- 学生们收到的建议被标记为由人工智能产生或来自人类同行.
- 在四周的时间里,对象的特征和对建议的信任得到了分析.
主要成果:
- 虽然观察到"算法欣赏"效应,但它是由参与者特征调节的.
- 男学生和具有较高学科知识的学生对人工智能建议的信任度低于同行建议.
- 无论建议的准确性如何,信任模式保持一致,并在绩效反后持续存在.
结论:
- 对人工智能建议的信任不统一,受用户人口统计和知识水平的影响.
- 人工智能工具的可信度可能落后于它们日益融入教育和专业环境.
- 需要进一步的研究来理解和增强用户对人工智能系统的信任.
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