Text simplification aims to improve the readability of a text while maintaining its original meaning. Despite significant advancements in Automatic Text Simplification, particularly in English, other languages like Italian have received less attention due to limited high-quality data. Moreover, most Automatic Text Simplification systems produce a unique output, overlooking the potential benefits of customizing text to meet specific cognitive and linguistic requirements. These challenges hinder the integration of current Automatic Text Simplification systems into Computer-Assisted Language Learning environments or classrooms. This article presents a multifaceted output that highlights the potential of Automatic Text Simplification for Computer-Assisted Language Learning. First, we curated an enriched corpus of parallel complex-simple sentences in Italian. Second, we fine-tuned a transformer-based encoder-decoder model for sentences simplification. Third, we parameterized grammatical text features to facilitate adaptive simplifications tailored to specific target populations, achieving state-of-the-art results, with a SARI score of 60.12. Lastly, we conducted automatic and manual qualitative and quantitative evaluations to compare the performance of ChatGPT-3.5, and our fine-tuned transformer model. By demonstrating enhanced adaptability and performance through tailored simplifications in Italian, our findings underscore the pivotal role of ATS in Computer-Assisted Language Learning methodologies.