Transforming Vocational Pedagogy: A Quasi-Experimental Study on AI-Powered Job Sheets for Enhancing Critical Inquiry and Collaborative Dynamics in Metalwork Practice

Abstract

Traditional instructional media in higher vocational education, particularly in metalwork and forging practice, often fosters passive learning, directive task execution, limited critical thinking development, and insufficient collaborative interaction. To address these gaps and align vocational training with current industry competency demands, this study designed and validated an AI-powered job sheet integrated into the practical engineering curriculum. This research evaluates the effectiveness of the AI-enhanced job sheet in improving students’ Higher-Order Thinking Skills (HOTS) and collaborative competencies, compared to conventional instructional materials. A quasi-experimental non-equivalent control group pre-test-post-test design was employed, with participants assigned to an experimental group using the AI job sheet and a control group using traditional versions. Data were collected via validated assessment rubrics, then analyzed through normality and homogeneity tests, Normalized Gain (N-Gain) calculation, independent sample t-tests, and Cohen’s d effect size measurement. Results show the experimental group achieved significantly higher gains across all measured competencies. For HOTS, the experimental group attained high-category N-Gain while the control group remained in the moderate category; the same pattern was observed for collaborative skills. The intervention demonstrates a statistically significant positive influence with an exceptionally large effect size. Integrating AI-powered job sheets transforms traditionally instructive workshop sessions into active, inquiry-oriented, and highly collaborative learning environments, offering a scalable, evidence-based framework for engineering and vocational educators.