Enhancing mathematics teachers’ contextualization of mathematics content in welding and fabrication courses using ChatGPT and Meta AI

Abstract Mathematics underpins all technical and vocational education and training (TVET) trade areas, particularly welding and fabrication. However, contextualising mathematical concepts within welding and fabrication remains challenging. To address this gap, this study investigated the potential of ChatGPT and Meta AI to assist mathematics teachers in generating contextualised mathematical examples and tasks for welding and fabrication courses. An exploratory case study design was employed. The relevant mathematical concepts were selected from Fabrication Mathematics I and II of the American Welding Society and the Mathematics Curriculum for Welders of Northeast Wisconsin Technical College. Two mathematics teachers used ChatGPT and Meta AI to generate examples and questions relating to various mathematical concepts taught to welding and fabrication students in second-cycle TVET institutions. Experts in welding and fabrication (WF) assessed the generated outputs, and the data were examined using content and thematic analyses. The findings revealed that both ChatGPT and Meta AI generated high-quality, well-structured and meaningful questions that demonstrated an understanding of how to contextualise mathematical content within WF courses. The generated questions were generally appropriate for the students’ academic levels and cognitive demands and were considered suitable for classroom instruction. The findings further indicated that the questions were relevant to WF courses, supported the application and transfer of mathematical knowledge, and addressed the mathematical competencies required in the WF trade area. Thus, when used appropriately and with expert validation, these AI tools may help teachers connect theoretical mathematics with practical vocational applications, thereby making mathematics instruction more relevant to students’ future careers.