VQA support to Arabic Language Learning Educational Tool
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Abstract (arXiv)
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.
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References · 16
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2024The Llama 3 Herd of Modelscited by 14,192
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
- 2015VQA: Visual Question Answeringcited by 2,489
- 2021CLIPScore: A Reference-free Evaluation Metric for Image Captioningcited by 2,388
- 2025Gemma 3 Technical Reportcited by 1,686
- 2019LXMERT: Learning Cross-Modality Encoder Representations from Transformerscited by 1,548
- 2024Evaluating Text-to-Visual Generation with Image-to-Text Generationcited by 465
- 2021Intriguing Properties of Vision Transformerscited by 446
- 2022VLP: A Survey on Vision-Language Pre-trainingcited by 159
- 2017Visual Question Generation as Dual Task of Visual Question Answeringcited by 41
- 2024Peacock: A Family of Arabic Multimodal Large Language Models and Benchmarkscited by 32
4 more
- 2024Dallah: A Dialect-Aware Multimodal Large Language Model for Arabiccited by 16
- 2025AIN: The Arabic INclusive Large Multimodal Modelcited by 8
- 2024Realizing Visual Question Answering for Education: GPT-4V as a Multimodal AIcited by 5
- 2024Question-Answering (QA) Model for a Personalized Learning Assistant for Arabic Languagecited by 5
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- 2024CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
- 2023Prompting Vision Language Model with Knowledge from Large Language Model for Knowledge-Based VQA
- 2023Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched Prompts
- 2022VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challenges
- 2023Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions