AI 中文总结
本文通过叙事综述与概念综合，明确了影响高等教育GenAI使用学术诚信的心理因素，提出整合性概念模型，主张结合多维度措施应对GenAI相关诚信问题。
AI 中文摘要
本文研究了影响高等教育中生成式人工智能（GenAI）使用的学术诚信与不诚信行为的心理决定因素。该研究并未将学术不端视为纯粹的技术问题，而是将学术诚信概念化为一种心理中介决策过程，受道德推理、感知社会规范、政策清晰度、学术自我效能、AI素养、学业压力以及作者身份信念的影响。在方法上，本文采用聚焦叙事综述与概念综合设计，通过组合关键词“生成式AI”“学术诚信”“学术不端”“道德脱离”“AI素养”及“高等教育”进行定向检索，构建了由16篇核心文献组成的目的样本，包括2022年至2026年3月间发表的同行评审研究及政策导向文本。综述文献表明，学生并非将所有形式的AI辅助都视为作弊；当机构指导模糊、同伴使用呈常态化、学业压力较高且AI工具被视为困难认知劳动的合法替代品时，诚信风险会上升。相比之下，作业层面的指导、明确的披露规范、伦理导向的教学以及真实性评估设计，似乎比仅以检测为中心的应对措施更能有效降低诚信风险。基于这些发现，本文提出了一个整合性概念模型，其中机构情境塑造心理评估，而心理评估进而影响已披露、边缘或不诚信的GenAI使用。本文结论指出，应对GenAI相关诚信问题的有效措施应结合政策清晰度、教学法、AI素养及学生支持，而非仅依赖禁止或基于软件的监控。
英文摘要
This paper examines the psychological determinants that shape academically honest and dishonest uses of generative artificial intelligence (GenAI) in higher education. Rather than treating academic misconduct as a purely technological problem, the study conceptualizes academic integrity as a psychologically mediated decision process influenced by moral reasoning, perceived social norms, policy clarity, academic self-efficacy, AI literacy, performance pressure, and beliefs about authorship. Methodologically, the paper adopts a focused narrative review and conceptual synthesis design. A purposive corpus of 16 core publications, including peer-reviewed studies and policy-oriented texts published between 2022 and March 2026, was assembled through targeted searches using combinations of the keywords generative AI, academic integrity, academic misconduct, moral disengagement, AI literacy, and higher education. The reviewed literature suggests that students do not interpret all forms of AI assistance as cheating. Integrity risk increases when institutional guidance is vague, peer use appears normalized, academic pressure is high, and AI tools are perceived as legitimate substitutes for difficult cognitive labor. By contrast, assignment-level guidance, explicit disclosure norms, ethics-oriented instruction, and authentic assessment design appear to reduce integrity risk more effectively than detection-centered responses alone. Based on these findings, the paper proposes an integrative conceptual model in which institutional context shapes psychological appraisal, and psychological appraisal in turn influences disclosed, borderline, or dishonest GenAI use. The paper concludes that effective responses to GenAI-related integrity problems should combine policy clarity, pedagogy, AI literacy, and student support rather than relying only on prohibition or software-based surveillance.
Comments10 pages, 1 figure. Presented at the 11th International Academic Studies Congress, Tarsus, Mersin, Türkiye, April 28-30, 2026; published in the Book of Full Texts, pp. 750-759
Journal ref11th International Academic Studies Congress, Book of Full Texts, Medyator Publishing (2026) 750-759
