Analyzing Arabic Syntax in Waṣāyā al-Ābā' lil-Abnā: A Comparative Study between Traditional Nahwu and ChatGPT Approaches

Abstract

This study conducted a comparative analysis of the syntactic structure (tarkīb) in the classical Arabic text Waṣāyā al-Ābā’ lil-Abnā’ by evaluating two distinct methodologies: the Traditional Nahwu Approach (rooted in classical grammar) and the Artificial Intelligence (AI)-based Analysis via ChatGPT. Employing a qualitative descriptive-comparative method, the research described and contrasted the tarkīb outputs from both sources, aiming to (1) detail the tarkīb analysis according to traditional nahwu, (2) describe the analysis of the same text using ChatGPT, and (3) determine the comparative advantages and limitations of each approach. The findings reveal that the Traditional Nahwu Approach provides highly detailed, contextually rich, and terminologically accurate descriptions of all grammatical elements (fi‘l, fā‘il, maf‘ūl bih, jār–majrūr, iḍāfah), including precise identification of positional rules (e.g., fā‘il mu’akhkhar). In contrast, ChatGPT successfully recognized basic syntactic patterns and assigned general functions, but demonstrated limitations in achieving the required classical precision, often failing to identify complex structures (such as the explicit and implied fā‘il or detailed iḍāfah forms) and positional variations. In conclusion, while traditional nahwu excels in depth and interpretive accuracy, ChatGPT offers a quick, accessible, and practical tool for preliminary syntactic mapping. The integration of these two approaches—termed AI-Assisted Nahwu Analysis (AINA)—demonstrates significant potential to enrich the study of Arabic syntax by bridging classical scholarship with modern AI technology.