Adaptive learning in vocational accounting education: Designing and validating AI-driven personalization for professional English mastery

Lely Indah Kurnia; Ahmad Mustofa; Subekhi Hadi Purnomo; Bimo Setyawan; Mohammad Ridho’i; Kunti Eliyen; Yanuarius Seran

doi:10.18860/jetle.v8i1.45866

Adaptive learning in vocational accounting education: Designing and validating AI-driven personalization for professional English mastery

Lely Indah Kurnia, Ahmad Mustofa, Subekhi Hadi Purnomo, Bimo Setyawan, Mohammad Ridho’i, Kunti Eliyen, Yanuarius Seran

Abstract

English for Specific Purposes (ESP) instruction within higher vocational accounting education often struggles with substantial baseline proficiency disparities and rigid, one-size-fits-all curriculum delivery that may increase cognitive demands. This study aimed to design, develop, and evaluate the Adaptive Professional English Learning System (APELS), a web-based intelligent learning platform integrating rule-based pedagogical routing and Natural Language Processing (NLP) to personalize ESP learning trajectories for accounting undergraduates. Employing a Design and Development Research (DDR) paradigm structured within the five-phase ADDIE framework, the prototype was engineered with smart diagnostic placement, dynamic skill mapping, automated formative feedback, and pedagogical tracking dashboards. System evaluation involved 37 vocational accounting undergraduates and three multidisciplinary expert validators (in ESP pedagogy, accounting education, and educational technology). Evaluation procedures comprised black-box functional validation, System Usability Scale (SUS) administration across all 40 participants, and empirical pre-test/post-test learning efficacy measurements administered to the 37 accounting undergraduates. The results demonstrated a 100% functional pass rate across all branching tracks, an exceptional usability rating with an average SUS score of 82.75 (Grade A / "Excellent"), and a significant increase in domain competency from a pre-test mean of 48.20 to a post-test mean of 84.60. The normalized gain index (g = 0.703) confirmed high instructional efficacy while reducing unproductive learning time by 34%. In conclusion, the integration of rule-based logic and domain-specific NLP feedback effectively accommodates learner heterogeneity, scaffolds professional writing and lexical acquisition, and enhances autonomous ESP competencies in vocational higher education. These findings offer critical pedagogical implications for vocational polytechnics seeking to scale adaptive, AI-augmented language environments that bridge foundational classroom instruction with workplace accounting standards.

Keywords

adaptive learning; artificial intelligence; english for specific purposes; natural language processing; vocational education

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DOI: https://doi.org/10.18860/jetle.v8i1.45866