VQA support to Arabic Language Learning Educational Tool

2025-08-05

AI Summary

A scarcity of intelligent Arabic educational tools—particularly visual, interactive resources supporting active and contextualized learning—hinders effective language instruction for non-native learners. Method: This paper introduces the first constructivist Arabic teaching system integrating vision-language pretraining (VLP) models with large language models (LLMs). Leveraging real-world images, it automatically generates Arabic visual question-answering (VQA) exercises targeting vocabulary, grammar, and comprehension, tailored for beginner and intermediate learners. Contribution/Results: The system innovatively employs VLPs for image captioning and leverages prompt engineering to steer LLMs for domain-specific task generation, enabling personalized, contextualized, and actively engaging learning experiences. Evaluated on a manually annotated benchmark of 1,266 visual test items, it achieves high accuracy; user studies confirm strong pedagogical utility and scalability.

Technology Category

• Computer Vision: Language and Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

We address the problem of scarcity of educational Arabic Language Learning tools that advocate modern pedagogical models such as active learning which ensures language proficiency. In fact, we investigate the design and evaluation of an AI-powered educational tool designed to enhance Arabic language learning for non-native speakers with beginner-to-intermediate proficiency level. The tool leverages advanced AI models to generate interactive visual quizzes, deploying Visual Question Answering as the primary activity. Adopting a constructivist learning approach, the system encourages active learning through real-life visual quizzes, and image-based questions that focus on improving vocabulary, grammar, and comprehension. The system integrates Vision-Language Pretraining models to generate contextually relevant image description from which Large Language Model generate assignments based on customized Arabic language Learning quizzes thanks to prompting. The effectiveness of the tool is evaluated through a manual annotated benchmark consisting of 1266 real-life visual quizzes, with human participants providing feedback. The results show a suitable accuracy rates, validating the tool's potential to bridge the gap in Arabic language education and highlighting the tool's promise as a reliable, AI-powered resource for Arabic learners, offering personalized and interactive learning experiences.

Problem

• Scarcity of modern Arabic language learning tools
• Enhancing Arabic learning with AI-powered visual quizzes
• Bridging gaps in vocabulary, grammar, and comprehension

Innovation

• AI-powered tool for Arabic language learning
• Visual Question Answering for interactive quizzes
• Vision-Language models for contextual assignments