Integrating generative AI in data science programming: Group differences in hint requests

Tenzin Doleck, Pedram Agand, Dylan Pirrotta
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Abstract

Generative AI applications have increasingly gained visibility in recent educational literature. Yet less is known about how access to generative tools, such as ChatGPT, influences help-seeking during complex problem-solving. In this paper, we aim to advance the understanding of learners' use of a support strategy (hints) when solving data science programming tasks in an online AI-enabled learning environment. The study compared two conditions: students solving problems in DaTu with AI assistance (N = 45) and those without AI assistance (N = 44). Findings reveal no difference in hint-seeking behavior between the two groups, suggesting that the integration of AI assistance has minimal impact on how individuals seek help. The findings also suggest that the availability of AI assistance does not necessarily reduce learners’ reliance on support strategies (such as hints). The current study advances data science education and research by exploring the influence of AI assistance during complex data science problem-solving. We discuss implications and identify paths for future research.

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在数据科学编程中整合生成式人工智能:提示请求的群体差异
在最近的教育文献中,生成式人工智能应用日益受到关注。然而,人们对使用生成工具(如 ChatGPT)如何影响复杂问题解决过程中的求助行为知之甚少。在本文中,我们旨在进一步了解学习者在人工智能在线学习环境中解决数据科学编程任务时使用辅助策略(提示)的情况。研究比较了两种情况:学生在有人工智能辅助的 DaTu 中解决问题(45 人)和没有人工智能辅助的学生(44 人)。研究结果表明,两组学生在寻求提示行为上没有差异,这表明集成人工智能辅助对个人如何寻求帮助的影响微乎其微。研究结果还表明,提供人工智能辅助并不一定会减少学习者对辅助策略(如提示)的依赖。本研究通过探索人工智能辅助在复杂数据科学问题解决过程中的影响,推动了数据科学教育和研究的发展。我们讨论了研究的意义,并确定了未来研究的方向。
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