Exploring STEM Career Competencies with the Assistance of Generative AI

This paper presents preliminary findings of an analysis of STEM career competencies and skills using generative AI. According to PRISMA methodology, 65 research papers and 38 reports dealing with STEM career competencies and skills that met inclusion and exclusion criteria were analyzed with Claude AI, one of Large Language Models. Because of Claude AI's restrictions, some research papers and reports were categorized by their specific focus, such as K-12 studies, studies regarding diverse STEM careers, and fab-lab and maker-space studies, and then they were analyzed separately. Through a combination of the skills and competencies that emerged from each step of the analysis, a final analysis was carried out. Being aware of limitations of LLMs, the authors verified the themes based on the literature and further verification is in process with topic modeling analysis with LDA in R. The collective findings indicated six key STEM skills and competencies: technical skills, problem solving and critical thinking, research skills, soft skills, lifelong learning and adaptability, and other competencies. Researchers found some specific themes in the analysis, including creativity and innovation, communication and collaboration from fab-lab and makerspace research, mathematical and analytical skills, ethical and social responsibility, passion, and dedication from research into diverse STEM careers, self-efficacy from K-12 STEM research, and more social emotional learning (SEL) skills from STEM and employability reports. The findings indicated the importance of foundational technical skills as well as SEL skills for STEM career identity.