Scaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education
Karen D. Wang, Zhilu Wu, L'Nard Tufts, Carl Wieman, Shima Salehi, Nick Haber
Abstract
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 has been a significant challenge. The recent proliferation of generative artificial intelligence (genAI) tools like ChatGPT in higher education introduces new considerations: how to define problem-solving competency in a genAI era, and how these tools can help or hinder students' development of STEM problem-solving competency. Our research takes steps in examining these considerations by studying how and why college students are currently using genAI tools in their STEM coursework, with a specific focus on how they employ these tools to support their problem-solving. We conducted an online survey of 40 STEM college students from diverse institutions across the US. In addition, we surveyed 28 STEM faculty to understand instructor views on effective and ineffective genAI tool use in STEM courses and their guidance for students. Our findings reveal high adoption rates and diverse applications of genAI tools among STEM students. The most common use cases of genAI tools in STEM coursework include finding explanations, exploring related topics, summarizing readings, and helping with problem-set questions. The primary motivation for using genAI tools in STEM coursework was to save time. Moreover, we found that over half of the student participants reported simply inputting a problem 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.