我们信任的人工智能吗? 探索可解释的基因AI和专业知识在教育中的作用
Camille Safarov1, Gregory Gadzinski1, Stephan Schlögl2
1International University of Monaco - Omnes Education, Monaco.
Human factors
|December 3, 2025
概括
人工智能信任错位发生在学生过度信任人工智能时,特别是那些偏爱精致解释而不是准确性的非专家. 专家从详细的AI理性中受益,但仍然不依赖于正确的AI协助.
科学领域:
- 人与计算机的交互
- 人工智能伦理学 人工智能伦理学
- 教育心理学教育心理学
背景情况:
- 人工智能可解释性和用户信任之间的关系是研究不足的.
- 在教育中整合人工智能需要理解学生对人工智能输出的信任.
- 人与人工智能之间的关系通常是在教育阶段形成的.
研究的目的:
- 为了检查大学生之间的AI信任误校.
- 评估解释长度和学生专业知识如何影响AI建议对齐.
- 了解塑造学生AI输出评估的因素.
主要方法:
- 对248名大学生进行了课堂实验.
- 参与者解决了GMAT问题,并获得了AI建议,解释深度各不相同.
- 通过分析学生是否将他们的最终答案与AI建议结合起来来衡量信任.
主要成果:
- 解释的复杂性增加了信任,但其影响因用户的专业知识和AI准确性而异.
- 以前正确答案的学生表现出较少的尊重,特别是对不正确的AI.
- 预先达成协议和AI的一致性增强了信任,促进了批判性参与或不批判的接受.
结论:
- "人工智能知道得更好"的启发式影响非专家,导致不批判地接受不正确的AI建议.
- 当AI准确时,专家从详细的解释中受益,但可能不太依赖正确的AI.
- 信任校准取决于调整学生的表现,AI可靠性和解释设计.
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