Harnessing Generative AI for ESP: A Cross-Disciplinary Vocational Education Framework with Predictive Modeling Evidence from Indonesia - Pranoto, Dani Chandra Yudho; Rufii, Rufii; Sabariah, Sabariah; Bandono, Adi (Universitas Dharma Wacana, 2025)
This study examines how Generative AI tools, specifically ChatGPT and Gemini, can enhance English for Specific Purposes (ESP) learning and education. Drawing on the UTAUT2 model of technology acceptance and recent discussions on AI-mediated learning, we examine the roles of baseline ability, perceived usefulness, and satisfaction as mediating factors in ESP classrooms. Data were collected from 50 vocational students across five departments using pre- and post-tests, AI usage logs, and Likert-scale surveys. Statistical analyses included descriptive statistics, paired t-tests, ANOVA with Tukey adjustment, correlation, reliability tests, and predictive modeling (OLS and LASSO) in SAS Studio. Results show a mean learning gain of 24.42 points, with Nursing and IT students benefiting most. AI usage hours strongly correlate with post-test scores but not directly with learning gain, suggesting that perceived usefulness and satisfaction (both rated 4.4/5 with ? = 1.00) mediate the outcomes. Baseline competence remains the strongest predictor, highlighting persistent disparities in skill distribution across vocational fields. These findings suggest that the effective integration of Generative AI in ESP requires scaffolding and domain-specific alignment, rather than simple exposure. The study offers a novel framework for AI-supported ESP instruction, providing practical guidance for educators and policymakers in Indonesia and similar contexts.