How to Proofread Arabic Text With AI: A Two-Pass Method and a Test You Can Run

No single tool fixes Arabic text well on its own. The workflow that works is two passes: first, a dedicated Arabic checker like Qalam to catch spelling, hamzas, and core grammar; second, an advanced chatbot (ChatGPT, Claude, or Gemini) instructed to list corrections with reasons in a table instead of rewriting; finally, a short human check targeting the errors machines miss. The big English-first checkers will not save you here: as shown in our Grammarly vs QuillBot comparison, Grammarly does not support Arabic at all. Meanwhile, LanguageTool offers only thin community coverage. Its documentation for version 6.6 (dated 2025-03-27 at dev.languagetool.org/languages) lists just 450 XML rules and 16 Java rules for Arabic, compared to 6,074 rules for English. LanguageTool itself calls rule counts “a very rough indication” of support, but the gap is wide, and its website has no Arabic checker page. Treat it as a free extra pass, nothing more.

Why Arabic proofreading is harder for machines

Natural language processing models struggle with Arabic because its morphology, orthography, and syntax differ fundamentally from European languages. Eight specific linguistic challenges routinely mislead automated checkers:

• Hamzat al-qat’ vs. hamzat al-wasl: Writers often get wrong whether a word-initial alif carries a written hamza (as in irsal, “sending”) or a connecting alif that takes none (as in istilam, “receipt”). Getting it right depends on the word’s morphological pattern, which simple checkers do not parse.
• Taa marbuta vs. haa: Dropping the two dots from a final taa marbuta turns madrasa (school) into a spelling that reads as a different word, mudarrisuh (“his teacher”). The slip breaks noun-adjective agreement, and simple checkers often let it through.
• Alif maqsura vs. yaa: Dotting the final letter turns the preposition ‘ala (on) into the name Ali. Both are real words, so a dictionary check passes it.
• Inna vs. anna: Arabic distinguishes inna (at the start of a clause or after qala, “he said”) from anna (after verbs such as akkada, “he confirmed”). A spell checker sees two valid words and stays silent.
• Number-noun agreement: Numerals from 3 to 10 take the opposite gender of the counted noun: a masculine noun such as taqrir (report) needs thalathat, a feminine one such as safha (page) needs thalath. From 11 to 99, the counted noun becomes a singular accusative. Software regularly misses gender mismatches.
• Case endings: In duals and sound masculine plurals the ending itself changes with case. After inna, writing al-muwazzafun instead of the accusative al-muwazzafin is a plain error that spell checkers miss, because both forms are in the dictionary.
• Colloquial words in formal Arabic: Dialect words such as Egyptian and Levantine ‘ashan (“because, so that”) slip into fusha unnoticed by tools that have no dialect-to-fusha dictionary.
• Missing tashkeel: Arabic is normally written without short-vowel marks, so the same three letters k-t-b can be read as kataba (“he wrote”), kutiba (“it was written”) or kutub (“books”). Without the marks, a tool has to guess from context.

Step 1 – Prepare the text

Effective proofreading begins before opening an automated tool. First, paste your copy as unformatted plain text. Rich formatting and word-processor metadata often disrupt right-to-left bidirectional markers, confusing automated checkers and misplacing punctuation marks.

Second, decide whether your text requires strict formal fusha for legal contracts and official correspondence, or accessible modern fusha for blogs and social content. If you are reviewing translated drafts, our document translation guide highlights recurring syntax errors to resolve beforehand. Finally, split long documents into sections of no more than about 1,000 words, so neither tool loses the thread.

Step 2 – First pass with Qalam

Run your prepared text through Qalam for initial mechanical correction. Qalam checks both Arabic and English spelling and grammar, providing one-click corrections. It operates as a web application, browser extensions (Chrome, Firefox, Safari, Edge), and add-ins for Microsoft Word, Outlook, Google Docs, and Google Slides.

Follow these concrete steps during this pass:

• Sign up and trial: New accounts receive a trial of paid features to correct 2,500 words free. Plan prices are not shown on Qalam’s public pricing page without logging in and choosing a currency, so we cannot quote them. For unlimited words, organizations can inquire about the Business plan. Qalam lists offices in Riyadh, Saudi Arabia, and Amman, Jordan.
• Accept corrections one by one: Review suggestions individually rather than applying automated bulk fixes.
• Standardize vocabulary: Use Qalam’s fusha suggestions to replace colloquial or foreign expressions with formal literary equivalents.
• Apply selective tashkeel: Instead of vocalizing every letter, select the word endings option or vocalize only ambiguous words. Qalam also supports full tashkeel, selected marks (tanween al-fath and shadda), and removing tashkeel.
• Use tafqeet for amounts: In contracts and invoices, use Qalam’s tafqeet feature to convert numbers into written Arabic words to prevent financial ambiguities.

Step 3 – Second pass with a chatbot

For your second pass, submit the cleaned copy to an advanced model like ChatGPT, Claude, or Gemini. While these models have strong contextual grasp, they tend to rewrite and alter style unless tightly constrained. Use this ready prompt:

You are an expert Arabic proofreader. Correct spelling and grammar only. Keep the original meaning and style intact, and do not rewrite sentences. If you are unsure about any correction, flag it clearly. Return your output strictly as a table with three columns: Original | Correction | Reason. Here is the text: (your text goes here)

Demanding a table is essential because chatbots instinctively try to “improve” text, changing tone and meaning. Reviewing a three-column table is much faster than comparing rewritten blocks, letting you evaluate justifications and accept or reject edits quickly.

Step 4 – The human check

No automated tool catches everything. Use this checklist to verify elements machines frequently miss:

• Names and transliterations: Check foreign proper names and brand names that software misinterprets as typos.
• Numbers and calculations: Cross-check numerals against written values for consistency.
• Quranic verses: Verify all ayahs against the standard Mushaf. Qalam provides a dedicated Quranic check that converts normal script to Uthmani script matching the Mushaf, fixes typos, and identifies the surah and ayah. Do not rely on a chatbot for Quranic text.
• Direct quotes: Ensure quotations and legal clauses remain verbatim.
• Ambiguous homographs: Check words where missing tashkeel could alter the intended technical meaning.
• Colloquial residue: Confirm that subtle regional idioms are converted into formal fusha.
• Terminology consistency: Verify that specialized terms remain consistent throughout the draft.

Test any tool yourself in two minutes

The best way to judge any checker is a planted-error test. The Arabic version of this article carries a three-sentence paragraph about a company announcement with exactly six deliberate errors. Copy it from there into any tool, then count how many of these it flags:

1. Taa marbuta: sharikah (company) written with a final haa instead of a dotted taa marbuta.
2. Hamzat al-wasl: istikhdam (use) written with a hamza under its first alif. As the verbal noun of a six-letter verb, it takes a connecting alif with no hamza.
3. Case ending of a number: khamsin (fifty) used as the subject of the verb “attended”. As a subject it must be nominative, khamsun.
4. Case after inna: al-muhandisun (the engineers) after inna. The noun of inna is accusative, so it must be al-muhandisin.
5. Number-noun gender: arba’at muwazzafat (four female employees). Because the noun is feminine, the number drops its taa marbuta: arba’ muwazzafat.
6. Alif maqsura: the preposition ‘ala written with a dotted yaa, which turns it into the name Ali.

Which tool for which job

Different proofreading tasks call for different tools. Select your tool based on these verified capabilities:

Privacy note

Never paste confidential contracts or personal data into any tool without reading its privacy terms. Organizations with large or sensitive volumes can ask Qalam about its Business plan, and should ask directly where texts are stored and for how long before signing anything.

No single tool handles Arabic proofreading well on its own. Generic checkers fail on complex grammar, while chatbots alter meaning if allowed to rewrite freely. The reliable workflow is a two-pass system: run Qalam first for mechanical spelling and grammar, use a tightly prompted chatbot table for stylistic auditing, and finish with a human check of names, numbers, and quotes. This structured approach delivers error-free Arabic while keeping you in full control.