Developing problem-solving competency is central to Science, Technology, Engineering, and Mathematics (STEM) education, yet translating this priority into effective approaches to problem-solving instruction and assessment remain a significant challenge. The recent proliferation of generative artificial intelligence (genAI) tools like ChatGPT in higher education introduces new considerations about how these tools can help or hinder students' development of STEM problem-solving competency. Our research examines these considerations by studying how and why college students use genAI tools in their STEM coursework, focusing on their problem-solving support. We surveyed 40 STEM college students from diverse U.S. institutions and 28 STEM faculty to understand instructor perspectives on effective genAI tool use and guidance in STEM courses. Our findings reveal high adoption rates and diverse applications of genAI tools among STEM students. The most common use cases include finding explanations, exploring related topics, summarizing readings, and helping with problem-set questions. The primary motivation for using genAI tools was to save time. Moreover, over half of student participants reported simply inputting problems for AI to generate solutions, potentially bypassing their own problem-solving processes. These findings indicate that despite high adoption rates, students' current approaches to utilizing genAI tools often fall short in enhancing their own STEM problem-solving competencies. The study also explored students' and STEM instructors' perceptions of the benefits and risks associated with using genAI tools in STEM education. Our findings provide insights into how to guide students on appropriate genAI use in STEM courses and how to design genAI-based tools to foster students' problem-solving competency.

翻译：培养问题解决能力是科学、技术、工程与数学（STEM）教育的核心目标，然而将这一优先事项转化为有效的问题解决教学与评估方法仍是一项重大挑战。近期以ChatGPT为代表的生成式人工智能（genAI）工具在高等教育中的激增，引发了关于这些工具如何促进或阻碍学生STEM问题解决能力发展的新思考。本研究通过调查大学生在其STEM课程中如何使用及为何使用生成式AI工具（重点关注其问题解决支持功能），来探讨这些问题。我们对来自美国多所高校的40名STEM专业学生及28名STEM教师进行了调查，以了解教师对STEM课程中有效使用生成式AI工具及相关指导的看法。研究结果显示，STEM学生对生成式AI工具的采纳率很高，且应用场景多样。最常见的用例包括：寻找概念解释、探索相关主题、总结阅读材料以及协助完成习题集。使用生成式AI工具的主要动机是节省时间。此外，超过半数的学生参与者报告称，他们直接将问题输入AI以生成解决方案，这可能绕过了自身的问题解决过程。这些发现表明，尽管采纳率很高，学生当前使用生成式AI工具的方式往往未能有效提升其自身的STEM问题解决能力。本研究还探讨了学生和STEM教师对在STEM教育中使用生成式AI工具所带来的益处与风险的认知。我们的研究结果为如何指导学生恰当地在STEM课程中使用生成式AI工具，以及如何设计基于生成式AI的工具以促进学生问题解决能力的发展提供了见解。