Instructional Design for Impactful Arabic Speaking Instruction Through AI-Assisted Learning and a Deep Learning Approach
DOI:
https://doi.org/10.55909/jpbs.v5i5.1785Keywords:
instructional design, Arabic, speaking, AI-assisted, deep learningAbstract
This study aimed to develop an instructional design for impactful Arabic speaking instruction through AI-Assisted Learning and a Deep Learning approach. The study was conducted at the Arabic Language Education Study Program, Faculty of Languages and Arts, Universitas Negeri Jakarta, during the 2025/2026 academic year. The population consisted of first-year students enrolled in the Kalam lil Mubtadi' course, with a sample of 50 students selected for the needs analysis. This study employed the Design and Development Research (DDR) model, focusing on the analysis and design phases. Data were collected using questionnaires, classroom observations, semi-structured interviews, and document analysis. Quantitative data from the questionnaires were analyzed using descriptive statistics in the form of frequencies and percentages, while qualitative data from observations, interviews, and document analysis were analyzed through data reduction, categorization, and interpretation to identify instructional requirements. The results produced an instructional design framework that systematically integrates Course Learning Outcomes (CPMK), the Deep Learning phases of Understanding, Applying, and Reflecting, AI-Assisted Learning as a pedagogical learning partner, Story-Based Learning as a contextual learning environment, and authentic assessment into a coherent instructional system. The framework was implemented through an interactive digital module that provides contextual learning experiences, AI-assisted speaking practice, reflective learning activities, and authentic speaking projects to support impactful Arabic speaking instruction.
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Copyright (c) 2026 Rifda Haniefa, Siti Masyitoh, Samsi Setiadi, Muhammad Kamal, Puti Zulharby
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