判断
生成语法
系统回顾
计算机科学
工作(物理)
内容分析
生成模型
心理学
管理科学
工程伦理学
教学方法
数学教育
内容(测量理论)
高等教育
选择(遗传算法)
人工智能
写作评估
学术写作
作者
Jining Han,Yuying Yang,Geping Liu
摘要
ABSTRACT The rapid emergence of generative artificial intelligence (GenAI) in academic settings has led to growing concerns about its impact on writing and assessment practices. This paper reviews the latest literature on detecting GenAI‐generated content and explores the challenges and potential solutions faced by educators. This study identifies various GenAI detection tools and analyses their strengths, weaknesses, and effectiveness across different writing contexts. It also discusses the growing complexity of GenAI outputs, including modifications by humans or other AI systems, which complicate detection efforts. The findings highlight the importance of adopting multifaceted approaches to evaluation, combining detection tools with expert human judgement to ensure academic integrity. Additionally, the results of this study suggest that pedagogical models need to evolve to accommodate the use of GenAI, advocating for a shift toward promoting critical thinking, creativity, and real‐world problem solving. This review provides insights into how educators can adapt their teaching strategies and assessment methods in response to the increasing prevalence of GenAI tools in education.
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