用机器学习预测大学生用ChatGPT写作业的行为，帮教育者设计更合理的AI考核方式。
Predicting ChatGPT Use in Assignments: Implications for AI-Aware Assessment Design
- 基于388名学生数据，用XGBoost模型分析使用ChatGPT的预测因素。
- 二分类模型准确率达80.1%，能有效识别是否使用AI完成作业。
- 发现频繁用AI学新知识可能影响独立思考，需调整评估方式。
生成式AI工具如ChatGPT的兴起深刻改变了教育生态，引发关于学习效果与学术诚信的讨论。尽管已有研究探讨其机遇与风险，但缺乏对学生完成作业时实际行为的量化分析。本研究通过分析来自俄罗斯及国际学生的388份问卷，采用XGBoost算法建模预测学生在学术作业中使用ChatGPT的倾向。关键预测因子包括学习习惯、学科偏好和对AI的态度。二分类模型在测试集上达到80.1%准确率，敏感度80.2%，特异度79.9%；多分类模型测试准确率64.5%，加权精确率64.6%，召回率64.5%，显示可能存在数据不足问题。研究发现，频繁使用ChatGPT学习新概念与潜在过度依赖相关，可能削弱长期学术独立性。这表明尽管生成式AI可提升知识获取效率，但无约束使用可能损害批判性思维与原创性。研究建议制定学科定制化指导方针与重构评估策略，以在创新与学术严谨间取得平衡，为教育工作者与政策制定者提供伦理化、高效化的AI融合路径。
原文摘要 · Abstract (English)
The rise of generative AI tools like ChatGPT has significantly reshaped education, sparking debates about their impact on learning outcomes and academic integrity. While prior research highlights opportunities and risks, there remains a lack of quantitative analysis of student behavior when completing assignments. Understanding how these tools influence real-world academic practices, particularly assignment preparation, is a pressing and timely research priority. This study addresses this gap by analyzing survey responses from 388 university students, primarily from Russia, including a subset of international participants. Using the XGBoost algorithm, we modeled predictors of ChatGPT usage in academic assignments. Key predictive factors included learning habits, subject preferences, and student attitudes toward AI. Our binary classifier demonstrated strong predictive performance, achieving 80.1\% test accuracy, with 80.2\% sensitivity and 79.9\% specificity. The multiclass classifier achieved 64.5\% test accuracy, 64.6\% weighted precision, and 64.5\% recall, with similar training scores, indicating potential data scarcity challenges. The study reveals that frequent use of ChatGPT for learning new concepts correlates with potential overreliance, raising concerns about long-term academic independence. These findings suggest that while generative AI can enhance access to knowledge, unchecked reliance may erode critical thinking and originality. We propose discipline-specific guidelines and reimagined assessment strategies to balance innovation with academic rigor. These insights can guide educators and policymakers in ethically and effectively integrating AI into education.
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