QuesGenie: Intelligent Multimodal Question Generation
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866908518305497088 |
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| author | Mubarak, Ahmed Ahmed, Amna Nasser, Amira Mohamed, Aya El-Sadek, Fares Ahmed, Mohammed Salah, Ahmed Sobhy, Youssef |
| author_facet | Mubarak, Ahmed Ahmed, Amna Nasser, Amira Mohamed, Aya El-Sadek, Fares Ahmed, Mohammed Salah, Ahmed Sobhy, Youssef |
| contents | In today's information-rich era, learners have access to abundant educational resources, but the lack of practice materials tailored to these resources presents a significant challenge. This project addresses that gap by developing a multi-modal question generation system that can automatically generate diverse question types from various content formats. The system features four major components: multi-modal input handling, question generation, reinforcement learning from human feedback (RLHF), and an end-to-end interactive interface. This project lays the foundation for automated, scalable, and intelligent question generation, carefully balancing resource efficiency, robust functionality and a smooth user experience. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03535 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QuesGenie: Intelligent Multimodal Question Generation Mubarak, Ahmed Ahmed, Amna Nasser, Amira Mohamed, Aya El-Sadek, Fares Ahmed, Mohammed Salah, Ahmed Sobhy, Youssef Computation and Language Artificial Intelligence In today's information-rich era, learners have access to abundant educational resources, but the lack of practice materials tailored to these resources presents a significant challenge. This project addresses that gap by developing a multi-modal question generation system that can automatically generate diverse question types from various content formats. The system features four major components: multi-modal input handling, question generation, reinforcement learning from human feedback (RLHF), and an end-to-end interactive interface. This project lays the foundation for automated, scalable, and intelligent question generation, carefully balancing resource efficiency, robust functionality and a smooth user experience. |
| title | QuesGenie: Intelligent Multimodal Question Generation |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2509.03535 |