arXiv:2607.19699cs.CYcs.AI2026-07被引 6

比较加韩学生对生成式AI编程使用的伦理看法,发现文化差异影响判断。

Did Alice Do Wrong? Cross-Cultural Differences in Student Perceptions of Generative AI Use in University Computing Education

  • 基于情景问卷调查,对比加韩大学生对AI辅助编码的伦理评价。
  • 加拿大学生更认为使用AI编程不道德且违规,即使政策相同。
  • 文化因素如权力距离、个人主义显著影响学生对AI使用的道德判断。

生成式AI在高等教育中的兴起引发了关于学术诚信与伦理使用的紧迫讨论。本研究比较了加拿大与韩国大学生对生成式AI使用的态度,通过2024年秋季的情景式问卷调查,分析学生对AI辅助编程行为的伦理性和规则符合性判断。结果显示,尽管制度政策功能上一致,加拿大学生普遍比韩国学生更倾向于认为使用生成式AI既不道德也违反校规。统计分析(包括曼-惠特尼U检验和相关系数)表明,几乎所有情景下均存在显著差异。进一步分析显示,作业中所包含的AI生成代码比例是影响伦理判断的最强因素。研究基于霍夫斯泰德文化维度理论进行解读,指出权力距离、个人主义与不确定性规避等文化特征显著塑造学生对生成式AI的道德推理。研究结果强调,教育中公平整合AI需具备文化敏感性,应制定兼顾本地文化背景与学术诚信原则的细致使用规范。建议持续开展跨文化研究,以支持全球高等教育中负责任的生成式AI应用。

原文摘要 · Abstract (English)

The rise of generative AI (GenAI) in higher education has prompted urgent debates surrounding academic integrity and ethical use. This study examines cross-cultural differences in student perceptions of GenAI use, comparing responses from students at Canadian and South Korean universities. Using a scenario-based survey administered in Fall 2024, we analyzed how students judged the ethicality and rule compliance of AI-assisted coding practices. Results reveal that Canadian students were consistently more likely to perceive the use of GenAI as both unethical and against institutional policies compared to Korean students, despite functionally identical institutional policies. Statistical analysis, including Mann-Whitney U tests and correlation coefficients, demonstrated significant differences across nearly all scenarios. Analysis of the factors used in generating scenarios indicated that the amount of AI-generated code incorporated into assignments most strongly influenced ethical judgments. Findings were interpreted through Hofstede's cultural dimensions framework, suggesting that cultural factors such as power distance, individualism, and uncertainty avoidance significantly shape students' ethical reasoning regarding GenAI. Our results contribute to the growing body of evidence emphasizing that equitable AI integration in education must be culturally responsive, taking into account diverse conceptions of academic integrity. We advocate for the development of nuanced AI-use guidelines that are sensitive to local cultural contexts while upholding fundamental principles of academic honesty. This study highlights the need for ongoing cross-cultural research to inform ethical AI policies and support responsible GenAI use in global higher education settings.

生成式AI教育伦理跨文化研究

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