Integrating Generative AI and Data Science into General Chemistry Laboratories

The emergence of generative AI tools such as ChatGPT, Claude, and Gemini is reshaping research, education, and industry. Recent workforce data indicate a widening gap between declining junior-level opportunities and rising demand for senior-level expertise, underscoring the urgent need to adapt undergraduate chemistry curricula to prepare students for an AI-enabled workforce. To address this challenge, we designed a series of Python-based laboratory experiments for General Chemistry that integrate generative AI with instructor guidance to enhance student learning. The modules focus on (1) using AI tools to support conceptual understanding, (2) applying AI-assisted coding for data visualization and analysis, and (3) introducing machine learning workflows to solve real-world chemistry problems. All activities are implemented in Google Colaboratory, a cloud-based programming platform now integrated with Gemini, which helps students, particularly those without prior programming experience, complete coding tasks with step-by-step instructor guidance. We compare student attitudes toward the use of AI and programming at the beginning and end of the semesters, observing a more positive attitude toward AI use and increased confidence in programming. To demonstrate scalability, three PhD graduate students, two of whom had no prior programming background, successfully led the laboratory sessions using these AI-supported materials, confirming that instructors across a wide range of computational skill levels can teach the experiments. This work demonstrates how generative AI can create more prosperous and equitable learning experiences, equipping students with transferable skills essential for research and industry careers, as well as the evolving STEM workforce.