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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2507.05212 |
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| _version_ | 1866908438211067904 |
|---|---|
| author | Korir, Emmanuel Wechuli, Eugene |
| author_facet | Korir, Emmanuel Wechuli, Eugene |
| contents | Juvenotes is a real-time AI-driven pipeline that automates the transformation of academic documents into structured exam-style question banks, optimized for low-resource medical education settings in Kenya. The system combines Azure Document Intelligence for OCR and Azure AI Foundry (OpenAI o3-mini) for question and answer generation in a microservices architecture, with a Vue/TypeScript frontend and AdonisJS backend. Mobile-first design, bandwidth-sensitive interfaces, institutional tagging, and offline features address local challenges. Piloted over seven months at Kenyan medical institutions, Juvenotes reduced content curation time from days to minutes and increased daily active users by 40%. Ninety percent of students reported improved study experiences. Key challenges included intermittent connectivity and AI-generated errors, highlighting the need for offline sync and human validation. Juvenotes shows that AI automation with contextual UX can enhance access to quality study materials in low-resource settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05212 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-Time AI-Driven Pipeline for Automated Medical Study Content Generation in Low-Resource Settings: A Kenyan Case Study Korir, Emmanuel Wechuli, Eugene Computers and Society Juvenotes is a real-time AI-driven pipeline that automates the transformation of academic documents into structured exam-style question banks, optimized for low-resource medical education settings in Kenya. The system combines Azure Document Intelligence for OCR and Azure AI Foundry (OpenAI o3-mini) for question and answer generation in a microservices architecture, with a Vue/TypeScript frontend and AdonisJS backend. Mobile-first design, bandwidth-sensitive interfaces, institutional tagging, and offline features address local challenges. Piloted over seven months at Kenyan medical institutions, Juvenotes reduced content curation time from days to minutes and increased daily active users by 40%. Ninety percent of students reported improved study experiences. Key challenges included intermittent connectivity and AI-generated errors, highlighting the need for offline sync and human validation. Juvenotes shows that AI automation with contextual UX can enhance access to quality study materials in low-resource settings. |
| title | Real-Time AI-Driven Pipeline for Automated Medical Study Content Generation in Low-Resource Settings: A Kenyan Case Study |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2507.05212 |