AI-based Arabic Language and Speech Tutor
Project Overview
The document explores the integration of generative AI in education, focusing on the development of an AI-based Arabic Language and Speech Tutor (AI-ALST) aimed at supporting learners of the Moroccan Arabic dialect. Utilizing advanced artificial intelligence and natural language processing, the AI-ALST fosters a self-learning environment that offers personalized pronunciation training and feedback. It incorporates deep learning techniques such as bidirectional LSTM and attention mechanisms, which enhance its capability to detect and address pronunciation errors effectively. The findings indicate that this AI-driven approach not only improves learners' pronunciation skills but also promotes autonomous learning, showcasing the potential of generative AI to transform language education by providing tailored support and facilitating deeper engagement with the learning material. Overall, the document highlights the promising applications of generative AI in educational contexts, demonstrating its effectiveness and the positive outcomes associated with personalized learning experiences.
Key Applications
AI-based Arabic Language and Speech Tutor (AI-ALST)
Context: Teaching the Moroccan Arabic dialect to students at the University of Arizona.
Implementation: The AI-ALST system uses MFCC feature extraction, bidirectional LSTM, and an attention mechanism to analyze and assess pronunciation.
Outcomes: Successful detection of pronunciation errors with metrics such as F1-score, accuracy, precision, and recall indicating effective performance.
Challenges: Challenges include the lack of sufficient low-resource language datasets and the need to address class imbalance in training data.
Implementation Barriers
Technical barrier
Limited availability of Arabic language corpora suitable for training AI systems.
Proposed Solutions: Proposed solutions include the development of more open-source Arabic corpora and enhancing existing tools for Arabic language processing.
Resource barrier
Challenges in obtaining knowledgeable human tutors and sufficient data for low-resource languages.
Proposed Solutions: AI-driven chatbots and intelligent tutoring systems can provide self-study and adaptive learning environments.
Project Team
Sicong Shao
Researcher
Saleem Alharir
Researcher
Salim Hariri
Researcher
Pratik Satam
Researcher
Sonia Shiri
Researcher
Abdessamad Mbarki
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Sicong Shao, Saleem Alharir, Salim Hariri, Pratik Satam, Sonia Shiri, Abdessamad Mbarki
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai