Integrating Artificial Intelligence into the Arabic Language Teaching Plan at Higher Education

\nThe integration of Artificial Intelligence (AI) in language education has shown significant promise in enhancing teaching effectiveness and student engagement. This study aims to explore the strategies for integrating AI into the Arabic language teaching plan at Universitas Islam Riau. The primary objective is to identify effective methods and tools that can be employed to enhance the learning experience and outcomes for Arabic language students. The research employs a qualitative approach using case study methodology to gather in-depth insights from educators and students. Data collection methods include interviews, surveys, and analysis of existing teaching materials and AI tools. The research instruments consist of structured interview guides, survey questionnaires, and evaluation rubrics for AI tools. The findings indicate that the integration of AI can significantly enhance personalized learning, provide real-time feedback, and support interactive and engaging learning experiences. However, challenges such as the need for technical training for educators and the adaptation of existing curricula to incorporate AI tools were also identified. The study concludes that while AI integration in Arabic language teaching at Universitas Islam Riau holds great potential, it requires strategic planning, continuous support, and collaboration among stakeholders to address the challenges and maximize the benefits. These insights can serve as a guide for other institutions seeking to incorporate AI into their language teaching programs.\n

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2024-11-14
DOI
https://doi.org/10.1051/shsconf/202420206010/pdf
Primary Topic
Arabic Language Education Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Integrating Artificial Intelligence into the Arabic Language Teaching Plan at Higher Education

Springer Link (Chiba Institute of Technology)
Arabic Language Education Studies
article

Integrating Artificial Intelligence into the Arabic Language Teaching Plan at Higher Education

article en

Abstract

\nThe integration of Artificial Intelligence (AI) in language education has shown significant promise in enhancing teaching effectiveness and student engagement. This study aims to explore the strategies for integrating AI into the Arabic language teaching plan at Universitas Islam Riau. The primary objective is to identify effective methods and tools that can be employed to enhance the learning experience and outcomes for Arabic language students. The research employs a qualitative approach using case study methodology to gather in-depth insights from educators and students. Data collection methods include interviews, surveys, and analysis of existing teaching materials and AI tools. The research instruments consist of structured interview guides, survey questionnaires, and evaluation rubrics for AI tools. The findings indicate that the integration of AI can significantly enhance personalized learning, provide real-time feedback, and support interactive and engaging learning experiences. However, challenges such as the need for technical training for educators and the adaptation of existing curricula to incorporate AI tools were also identified. The study concludes that while AI integration in Arabic language teaching at Universitas Islam Riau holds great potential, it requires strategic planning, continuous support, and collaboration among stakeholders to address the challenges and maximize the benefits. These insights can serve as a guide for other institutions seeking to incorporate AI into their language teaching programs.\n

Springer Link (Chiba Institute of Technology)
Universitas Islam Riau, International Islamic University Malaysia
Quality Education
Openalex Percentile: Top 47%
Arabic Language Education Studies
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Integrating Artificial Intelligence into the Arabic Language Teaching Plan at Higher Education · Springer Link (Chiba Institute of Technology) (2024) | TGRS Research Map | TGRS