Generative AI Framework for Digital Motif Design in Vocational Education: Integrating Pedagogy and Ethics

Abstract

Conventional digital motif design instruction in higher vocational education often prioritizes complex software technicalities over conceptual design thinking. Generative AI (GenAI) can bypass manual drawing barriers, yet structured frameworks integrating prompt pedagogy and AI ethics remain lacking. This study conceptualizes and evaluates an ADDIE-based instructional e-module, enhanced by Borg & Gall protocols, that incorporates Midjourney prompt engineering, ethical AI frameworks, and interactive video tutorials within the Fashion Design program at Universitas Negeri Makassar. Expert validation confirmed exceptional content (92.56%) and media (91.27%) validity. Practicality testing demonstrated high user acceptance across individual (85.76%), small-group (89.77%), and large-group (84.17%) trials. A quasi-experimental evaluation (Experimental n=35 vs. Control n=22) revealed significant post-test learning gains in the experimental group (M=84.03, SD=6.42) compared to the control group (M=71.95, SD=5.88), confirmed by an Independent Samples t-test (t(55)=7.12, p<0.001) with a large effect size (Cohen's d=1.95). By shifting student cognitive load from manual illustration mechanics toward prompt syntax optimization and ethical human-AI co-creativity, this validated e-module bridges technical execution gaps, offering a scalable model for AI-driven vocational curricula.