Quiz-Tube: Enhancing Video-Based Learning with Automated AI Quiz Generation

In the digital age, video-based learning has become a dominant educational medium, with platforms like YouTube offering a vast repository of instructional content. However, passive video consumption often leads to suboptimal knowledge retention and limited engagement. To address this challenge, we introduce Quiz-Tube, an AI-powered web platform that converts educational YouTube videos into interactive quizzes. By leveraging a fine-tuned Gemma-9B language model, Quiz-Tube generates contextually relevant multiple-choice questions (MCQs) tailored to users’ preferences. The system integrates transcript extraction via the YouTube Transcript API, followed by data preprocessing, question generation, and quiz customization. Users can define quiz parameters such as difficulty level and question count, enhancing personalized learning. Our evaluation demonstrates significant performance improvements, including a BLEU score increase from 45.3 to 68.7 and an accuracy boost from 64.5% to 81.9% post-model fine-tuning. Future enhancements include multilingual support and AI-driven adaptive learning.

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Publication Details

Journal
ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)
Published
2025-01-01
DOI
https://doi.org/10.5935/jetia.v11i54.1743
Primary Topic
Human Pose and Action Recognition
Type
article
Field-Weighted Citation Impact
0.00
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article

Quiz-Tube: Enhancing Video-Based Learning with Automated AI Quiz Generation

Anil Thakur, T. Ram, Vivek Singh, Rashmi Bhat et al.
ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)
Human Pose and Action Recognition
article

Quiz-Tube: Enhancing Video-Based Learning with Automated AI Quiz Generation

Anil Thakur, T. Ram, Vivek Singh, Rashmi Bhat, Ayush Shukla
article en

Abstract

In the digital age, video-based learning has become a dominant educational medium, with platforms like YouTube offering a vast repository of instructional content. However, passive video consumption often leads to suboptimal knowledge retention and limited engagement. To address this challenge, we introduce Quiz-Tube, an AI-powered web platform that converts educational YouTube videos into interactive quizzes. By leveraging a fine-tuned Gemma-9B language model, Quiz-Tube generates contextually relevant multiple-choice questions (MCQs) tailored to users’ preferences. The system integrates transcript extraction via the YouTube Transcript API, followed by data preprocessing, question generation, and quiz customization. Users can define quiz parameters such as difficulty level and question count, enhancing personalized learning. Our evaluation demonstrates significant performance improvements, including a BLEU score increase from 45.3 to 68.7 and an accuracy boost from 64.5% to 81.9% post-model fine-tuning. Future enhancements include multilingual support and AI-driven adaptive learning.

ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)Vol. 11(54)
St. John's College of Nursing (US)
Openalex Percentile: Top 78%
Human Pose and Action Recognition
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