In the past decade, we have observed a growing interest in using technologies
such as artificial intelligence (AI), machine learning, and chatbots to provide
assistance to language learners, especially in second language learning. By
using AI and natural language processing (NLP) and chatbots, we can create an
intelligent self-learning environment that goes beyond multiple-choice
questions and/or fill in the blank exercises. In addition, NLP allows for
learning to be adaptive in that it offers more than an indication that an error
has occurred. It also provides a description of the error, uses linguistic
analysis to isolate the source of the error, and then suggests additional
drills to achieve optimal individualized learning outcomes. In this paper, we
present our approach for developing an Artificial Intelligence-based Arabic
Language and Speech Tutor (AI-ALST) for teaching the Moroccan Arabic dialect.
The AI-ALST system is an intelligent tutor that provides analysis and
assessment of students learning the Moroccan dialect at University of Arizona
(UA). The AI-ALST provides a self-learned environment to practice each lesson
for pronunciation training. In this paper, we present our initial experimental
evaluation of the AI-ALST that is based on MFCC (Mel frequency cepstrum
coefficient) feature extraction, bidirectional LSTM (Long Short-Term Memory),
attention mechanism, and a cost-based strategy for dealing with class-imbalance
learning. We evaluated our tutor on the word pronunciation of lesson 1 of the
Moroccan Arabic dialect class. The experimental results show that the AI-ALST
can effectively and successfully detect pronunciation errors and evaluate its
performance by using F_1-score, accuracy, precision, and recall.

该论文介绍了基于人工智能技术的阿拉伯语言和发音辅导系统（AI-ALST），使用自然语言处理技术和聊天机器人来创建一个智能的自我学习环境，该系统通过提取语音特征、双向 LSTM 和注意力机制来检测阿拉伯语口音单词发音是否准确，证明该系统能够有效地检测并评估学生的发音错误。