ChatGPT-based generative AI scaffolding, experiential learning styles, and technical writing skills among industrial technology students

This study examined the relationships among ChatGPT-based generative AI scaffolding, experiential learning styles, and technical writing skills of Industrial Technology students at the University of Camarines Norte, providing the basis for a proposed English language program. A quantitative, descriptive-correlational design was employed, with a sample of 450 respondents determined using the Raosoft Sample Size Calculator at a 95% confidence level and a 5% margin of error, allocated through proportionate stratified random sampling across the College of Engineering (CoEng), College of Computing and Multimedia Studies (CCMS), and College of Technology (CoTT). Findings revealed that most respondents were male students enrolled in the College of Engineering and were almost evenly distributed across the four-year levels. Students agreed that they use ChatGPT-based generative AI as a scaffolding tool, with familiarity rated highest and frequency of use rated lowest. However, they strongly agreed on their experiential learning styles, with the reflector learning style rated highest and the activist learning style rated lowest, and agreed on their technical writing skills, with objectivity rated highest and functionality rated lowest. Meanwhile, ChatGPT-based generative AI scaffolding varied significantly by sex, college, and year level; experiential learning styles varied only by sex and college; and technical writing skills varied only by year level. The three major variables were highly and positively correlated, with experiential learning styles most closely associated with technical writing skills, while ChatGPT-based generative AI scaffolding was associated with better technical writing skills as a supporting mechanism. These findings formed the basis for a proposed program, E-CRAFT (Experiential and ChatGPT-Responsive Approach to Functional Technical Writing), that addresses underutilized ChatGPT scaffolding, underdeveloped active and practical learning-style experiences, and identified gaps in functionality, clarity, and precision in technical writing.