DEVELOPING AI-BASED SELF-ASSESSMENT METHOD IN THE PROCESS OF OPTIMIZING EFL INCLUSIVE LEARNING ACHIEVEMENT - Azmi, Ulul; Arta, Berli (Universitas Muhammadiyah Metro, 2025)
This research aims to develop AI-based self-assessment method to optimize EFL inclusive learning. This study employed a Research and Development (R&D) approach using ADDIE model consisting of analysis, design, development, implementation, and evaluation. AI based self-assessment module was created to allow students to reflect on their own learning and receive simple feedback that fits their needs. The sample of this study involved 20 university students. AI-based self-assessment module was designed with user-friendly explanations, self-check questions, reflection activities, and AI-generated feedback. The data were validated by material experts with an average score of 3.5, categorized as very good. Therefore, it can be concluded that developing AI-based self-assessment method is appropriate to be implemented in inclusive learning. The result shows that the module is helpful and suitable to be used in EFL inclusive classrooms. It made learning more flexible, helped students to understand the material better, and supported them to learn more independently. This implies that lecturers can start using AI-based self-assessment modules as practical tools to support every student’s learning needs. By giving students simple feedback and space to reflect on their progress, this approach can help them become more confident, independent, and actively involved in their own learning.