[论文解读] Analyzing the Impact of AI Tools on Student Study Habits and Academic Performance

Ben Ward, Deepshikha Bhati+2 more|arXiv (Cornell University)|Dec 3, 2024

Education and Learning Interventions被引用 8

一句话总结

该论文对大学生进行调查，评估人工智能工具对学习习惯、时间管理、动机和学业成绩的影响，整体呈现积极结果，但存在一些保留意见。

ABSTRACT

This study explores the effectiveness of AI tools in enhancing student learning, specifically in improving study habits, time management, and feedback mechanisms. The research focuses on how AI tools can support personalized learning, adaptive test adjustments, and provide real-time classroom analysis. Student feedback revealed strong support for these features, and the study found a significant reduction in study hours alongside an increase in GPA, suggesting positive academic outcomes. Despite these benefits, challenges such as over-reliance on AI and difficulties in integrating AI with traditional teaching methods were also identified, emphasizing the need for AI tools to complement conventional educational strategies rather than replace them. Data were collected through a survey with a Likert scale and follow-up interviews, providing both quantitative and qualitative insights. The analysis involved descriptive statistics to summarize demographic data, AI usage patterns, and perceived effectiveness, as well as inferential statistics (T-tests, ANOVA) to examine the impact of demographic factors on AI adoption. Regression analysis identified predictors of AI adoption, and qualitative responses were thematically analyzed to understand students' perspectives on the future of AI in education. This mixed-methods approach provided a comprehensive view of AI's role in education and highlighted the importance of privacy, transparency, and continuous refinement of AI features to maximize their educational benefits.

研究动机与目标

• 评估人工智能工具如何影响学生的学习习惯、时间管理、动机与学业成绩。
• 识别影响大学生在不同人口特征和学科领域中采用AI的因素。
• 评估将AI工具整合到教育中的障碍，并提出负责任使用的框架。

提出的方法

• 在71名大学生中进行混合方法调查（李克特量表问题和后续访谈）。
• 使用描述性统计来汇总人口统计学特征和AI使用模式。
• 应用t检验和ANOVA来检验AI采用在人口统计方面的差异。
• 进行回归分析以识别AI采用的预测因素。
• 对关于未来教育中AI的定性回答进行主题分析。

Figure 1: Gender distribution of survey respondents

实验结果

研究问题

• RQ1 AI驱动工具如何提升学生的学业成果和学习效率？
• RQ2 哪些人口统计和情景因素影响高等教育中AI工具的采用？
• RQ3 哪些障碍阻碍AI工具与传统教学方法与课程的整合？
• RQ4 学生希望AI教育工具具备哪些功能以最大化学习效果？

主要发现

• 多数学生在AI工具的帮助下报告了学业方面的某些提升（83%，包括显著和轻微的提升）。
• 大多数学生定期使用AI工具，78%的人使用频率为‘经常或有时’。
• AI工具被认为对学习例程（平均4.37/5）和动机（平均4.17/5）有积极影响。
• 学生偏好提供自适应学习路径、实时反馈和考试定制的AI工具。
• 受访者中STEM领域占比很高，46.5%在计算机科学及相关领域。
• 国际样本的比例相对较低，表明样本以美国为中心。

Figure 2: Age distribution of survey respondents