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

• URL: http://arxiv.org/abs/2607.19699v1
• Date: Wed, 22 Jul 2026 03:02:06 GMT
• Title: Did Alice Do Wrong? Cross-Cultural Differences in Student Perceptions of Generative AI Use in University Computing Education
• Abstract summary: This study examines cross-cultural differences in student perceptions of generative AI (GenAI) use.<n>Canadian students were consistently more likely to perceive the use of GenAI as both unethical and against institutional policies.<n>Cultural factors such as power distance, individualism, and uncertainty avoidance significantly shape students' ethical reasoning regarding GenAI.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Is Solving Better Than Evaluating GenAI Solutions? [45.714140455691485]
  This study compares evaluating GenAI-generated solutions with traditional problem solving in a junior-level algorithms course.<n>We found no statistically significant differences between groups in midterm scores, final exam scores, overall course grades, or exam problems structurally aligned with the homework interventions.<n>Students received significantly higher homework scores when evaluating GenAI-generated solutions, but this localized advantage did not translate into downstream gains.
  arXiv  Detail & Related papers  (2026-07-30T02:08:29Z)
• Beyond Detection: Redesigning Assessment and Governande of Generative AI at the Universidad Politécnica de Madrid (UPM) [39.92037610044293]
  Most institutions frame the debate around AI detection, plagiarism, academic integrity and a presumed drop in student effort as defensive.<n>Other group of pioneering universities is pursuing deeper adoption, and assuming that any policy built on prevention or sanction will not hold.<n>What universities need instead is a coordinated effort to set clear, course-by-course rules for GenAI use.<n>UPM is developing a strategic and sustainable AI policy and adoption framework structured around six dimensions.
  arXiv  Detail & Related papers  (2026-06-04T18:04:50Z)
• Exploring Student Perception on Gen AI Adoption in Higher Education: A Descriptive Study [1.978884131103313]
  This study examines how students perceive, use, and evaluate Generative Artificial Intelligence (GenAI)<n>Data were collected through a questionnaire administered to 436 postgraduate Computer Science students at the University of Hertfordshire.
  arXiv  Detail & Related papers  (2026-03-29T17:19:39Z)
• A Human-Centered Approach to Ethical AI Education in Underresourced Secondary Schools [0.0]
  We developed a college credit--bearing course on Responsible and Ethical AI for students attending Title I and Title I--eligible high schools.<n>The study examines academic agency, confidence with college-level expectations, critical engagement with responsible AI, and perceived post-secondary trajectory expansion.
  arXiv  Detail & Related papers  (2026-03-27T01:44:07Z)
• AI Literacy, Safety Awareness, and STEM Career Aspirations of Australian Secondary Students: Evaluating the Impact of Workshop Interventions [38.350232667249095]
  Deepfakes and other forms of synthetic media pose growing safety risks for adolescents.<n>This study evaluates the impact of Day of AI Australia's workshop-based intervention on Australian secondary students.
  arXiv  Detail & Related papers  (2026-01-30T02:55:53Z)
• Beyond the Hype: Critical Analysis of Student Motivations and Ethical Boundaries in Educational AI Use in Higher Education [0.0]
  We find that 92% of students use AI tools primarily to save time and improve work quality.<n>We argue that institutions must adopt comprehensive AI literacy programs that integrate technical skills and ethical reasoning.
  arXiv  Detail & Related papers  (2025-11-14T14:49:29Z)
• Cultural Dimensions of Artificial Intelligence Adoption: Empirical Insights for Wave 1 from a Multinational Longitudinal Pilot Study [0.0]
  The swift diffusion of artificial intelligence (AI) raises critical questions about how cultural contexts shape adoption patterns and their consequences for human daily life.<n>This study investigates the cultural dimensions of AI adoption and their influence on cognitive strategies across nine national contexts in Europe, Africa, Asia, and South America.<n>Results reveal two key findings: First, cultural factors, particularly language and age, significantly affect AI adoption and perceptions of reliability with older participants reporting higher engagement with AI for educational purposes.<n>Second, ethical judgment about AI use varied across domains, with professional contexts normalizing its role as a pragmatic collaborator while academic settings emphasized risks of plagiarism.
  arXiv  Detail & Related papers  (2025-10-22T16:31:28Z)
• The Morality of Probability: How Implicit Moral Biases in LLMs May Shape the Future of Human-AI Symbiosis [33.50773360893016]
  This paper investigates how leading AI systems prioritize moral outcomes.<n>It shows that Care and Virtue values outcomes were rated most moral, while libertarian choices were consistently penalized.<n>It also highlights the need for explainability and cultural awareness as critical design principles to guide AI toward a transparent, aligned, and future symbiotic.
  arXiv  Detail & Related papers  (2025-09-12T14:37:57Z)
• Understanding Student Attitudes and Acceptability of GenAI Tools in Higher Ed: Scale Development and Evaluation [22.35484281040714]
  This study introduces a validated survey instrument designed to assess students' perceptions of generative AI (GenAI)<n>The instrument includes six thematic domains: institutional understanding, fairness and trust, academic and career influence, societal concerns, and GenAI use in writing and coursework.<n>Students from multilingual households perceived greater clarity in institutional policy, while first-generation students reported a stronger belief in GenAI's impact on future careers.
  arXiv  Detail & Related papers  (2025-08-03T21:22:34Z)
• Hey GPT, Can You be More Racist? Analysis from Crowdsourced Attempts to Elicit Biased Content from Generative AI [41.96102438774773]
  This work presents the findings from a university-level competition, which challenged participants to design prompts for eliciting biased outputs from GenAI tools. We quantitatively and qualitatively analyze the competition submissions and identify a diverse set of biases in GenAI and strategies employed by participants to induce bias in GenAI.
  arXiv  Detail & Related papers  (2024-10-20T18:44:45Z)
• How Different Groups Prioritize Ethical Values for Responsible AI [75.40051547428592]
  Private companies, public sector organizations, and academic groups have outlined ethical values they consider important for responsible AI technologies. While their recommendations converge on a set of central values, little is known about the values a more representative public would find important for the AI technologies they interact with and might be affected by. We conducted a survey examining how individuals perceive and prioritize responsible AI values across three groups.
  arXiv  Detail & Related papers  (2022-05-16T14:39:37Z)
• Confucius, Cyberpunk and Mr. Science: Comparing AI ethics between China and the EU [41.19326726478421]
  A major concern is the large homogeneity and presumed consensualism around these principles. We propose to analyse and compare the ethical principles endorsed by the Chinese National New Generation Artificial Intelligence Governance Professional Committee and those elaborated by the European High-level Expert Group on AI (HLEGAI) In our analysis, we wish to highlight that principles that seem similar a priori may actually have different meanings, derived from different approaches and reflect distinct goals.
  arXiv  Detail & Related papers  (2021-11-15T06:38:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.