<![CDATA[This study systematically evaluates the effectiveness of the Canva AI platform as a diagnostic assessment instrument for Arabic writing proficiency, specifically in the aspects of Al-Imla' (orthography) and Al-Khat (calligraphy), using the Systematic Literature Review (SLR) method. By adapting the PRISMA 2020 guidelines, this research synthesizes empirical evidence and indexed scientific literature from the 2016–2026 period across the Google Scholar, DOAJ, and Dimensions databases. From the 240 identified articles, nine core studies examining the intersection of artificial intelligence, visual-textual feedback, and Arabic linguistic processing were analyzed in depth. The synthesis results indicate that the visual generative features, automatic pattern recognition, and instantaneous feedback of Canva AI significantly accelerate the identification of granular orthographic errors, such as hamzah positioning, alif layyinah placement, and letter-joining discrepancies, and map student weakness profiles more intuitively than manual methods. Structurally, this platform functions as a practical "low-code" intermediary that bridges complex deep learning algorithms with pedagogical needs in the classroom. Although AI spatial analytics are effective in measuring the geometric proportions of calligraphy, the purely aesthetic assessment of Al-Khat still requires human expert validation. This research constructs a conceptual framework that integrates Information Systems Success Theory and Slavin's QAIT pedagogical model and concludes that Canva AI functions optimally as a secondary diagnostic assistant. This Human-AI collaboration paradigm reduces the administrative burden on teachers, thereby facilitating the design of precise and evidence-based remediation plans.]]>
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