Can LLMs Control Readability? A Multi-Dimensional Evaluation Framework for CEFR-Controlled Arabic Generation
Year: 2026
Type: conference-paper
Abstract: While Large Language Models (LLMs) can generate fluent Arabic text, their ability to reliably control readability levels remains unclear.We propose a multi-dimensional evaluation framework for Common European Framework of Reference for Language (CEFR)-controlled Arabic text generation, assessing whether instruction-following LLMs can serve as reliable generators for adaptive language learning.Our framework integrates controlled prompting, automatic readability prediction using a validated Taha-19 model, lexical constraint validation, and syntactic complexity profiling.Results show that structured prompting substantially improves CEFR alignment.In particular, CEFR-guided prompting with lexical constraints achieves the highest conformity to reference linguistic profiles (0.91 cosine similarity) and near-perfect agreement with predicted readability levels (0.99), while unconstrained prompting exhibits weak control.These findings establish an empirical foundation for integrating readability-aware Arabic text generation into adaptive educational systems.
Authors: Nour Rabih, Chatrine Qwaider, Ted Briscoe
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7166508647