[论文解读] Generative AI in Education: A Study of Educators' Awareness, Sentiments, and Influencing Factors

Aashish Ghimire, James Prather+1 more|arXiv (Cornell University)|Mar 22, 2024

Diverse Approaches in Healthcare and Education Studies被引用 14

一句话总结

tldr: 该研究调查高校教师，以评估对生成式AI与大语言模型在高等教育中的认知、情感及态度驱动因素，揭示总体积极态度，但学科与熟悉度存在差异。计算机科学教育者显示出更高的技术理解和情感，但在内容检测自信度方面与非CS同行相似。

ABSTRACT

The rapid advancement of artificial intelligence (AI) and the expanding integration of large language models (LLMs) have ignited a debate about their application in education. This study delves into university instructors' experiences and attitudes toward AI language models, filling a gap in the literature by analyzing educators' perspectives on AI's role in the classroom and its potential impacts on teaching and learning. The objective of this research is to investigate the level of awareness, overall sentiment towardsadoption, and the factors influencing these attitudes for LLMs and generative AI-based tools in higher education. Data was collected through a survey using a Likert scale, which was complemented by follow-up interviews to gain a more nuanced understanding of the instructors' viewpoints. The collected data was processed using statistical and thematic analysis techniques. Our findings reveal that educators are increasingly aware of and generally positive towards these tools. We find no correlation between teaching style and attitude toward generative AI. Finally, while CS educators show far more confidence in their technical understanding of generative AI tools and more positivity towards them than educators in other fields, they show no more confidence in their ability to detect AI-generated work.

研究动机与目标

• 评估教育者在各学科对生成式AI工具的认知度。
• 评估教育者对在教育中采用AI工具的情感态度。
• 识别影响教师对生成式AI态度差异的因素。
• 比较计算机科学与非计算机科学教育者的态度差异。
• 突出教育工作者就AI在课堂中的机会与关切。

提出的方法

• 采用 Likert量表的混合方法设计，可选的后续访谈。
• 使用描述性统计、推断检验和回归分析的定量分析。
• 对定性访谈进行扎根理论编码，同行评审一致性>85%。
• 将定性与定量结果整合，以交叉验证教育者的态度。

实验结果

研究问题

• RQ1 RQ1 不同学科的教育者对生成式AI工具的认知程度如何？
• RQ2 RQ2 教育者对这些AI工具的看法与情感态度为何？
• RQ3 RQ3 哪些因素促成教育者对生成式AI工具态度的差异？
• RQ4 RQ4 CS教育者的态度与其他学科教育者有何不同？
• RQ5 RQ5 教育者在课堂中指出的最大机会与关切是什么？

主要发现

• 大多数教育者听说过或尝试过生成式AI工具，超过40%定期或经常使用。
• 对AI工具的总体态度为积极（均值3.99；中位数4.5；第三四分位数5）。
• CS教育者展现出更高的技术理解（83% 对比 非CS 10%）和更高的熟悉度（M=4, SD=.71 对比 M=3.15, SD=1.08）。
• CS讲师对学生使用工具更有信心（M=4.22）高于非CS讲师（M=3.23）。
• 影响情感态度的主要因素：收益超过风险、感知教育质量提升、以及易于整合正向影响态度；对创造力丧失和作弊的担忧负向影响态度。
• 回归分析（线性、随机森林、梯度提升、XGBoost）在基于识别特征预测情感方面的均方误差在0.4到0.5之间。