Development of an integrative learning model based on artificial intelligence and digital literacy for prospective vocational teachers

This study aims to develop an integrative learning model based on Artificial Intelligence (AI) and digital literacy to enhance the competencies of prospective vocational teachers, particularly in Statistics learning. The research employed a Research and Development (R&D) approach adapted from the Lee and Owens model, consisting of five stages: needs assessment, front-end analysis, design, development and implementation, and evaluation. The study involved students of the Electronics Engineering Education Study Program at Universitas Negeri Makassar. Data were collected through tests and non-test instruments, including questionnaires, interviews, and expert validation sheets, and analyzed using qualitative and quantitative descriptive techniques. The findings revealed that students’ initial competencies in Statistics were relatively low, indicating a significant gap between actual and expected learning outcomes. The developed learning model integrates AI-based adaptive features, interactive multimedia, and digital learning resources to support personalized and flexible learning. Validation results from media, instructional design, and subject matter experts showed that the model is categorized as “very good,” with average scores above 89%. Furthermore, empirical testing demonstrated a significant improvement in student learning outcomes, as indicated by the increase in post-test scores compared to pre-test results, as well as highly positive student responses. The study concludes that the AI-based integrative learning model with digital literacy is effective in improving students’ conceptual understanding, engagement, and digital competencies. This model also contributes to preparing prospective vocational teachers to meet the demands of technology-driven education in the era of the Industrial Revolution 4.0 and Society 5.0.