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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM48384
Volume:
9 |
5 |
May 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Over the past few years, educators have faced mounting pressure to deliver personalized support to ever-larger and more diverse student cohorts—a challenge that traditional methods struggle to meet effectively. AI-powered teaching chatbots have emerged as a promising solution, offering students instant, tailored explanations and practice exercises around the clock. In a recent pilot involving 100 varied academic queries, our chatbot achieved 92% factual accuracy, mirroring expert-verified answers in most cases. Meanwhile, average response times consistently fell below five seconds, ensuring learners remain engaged rather than waiting on lagging system. By hosting the DeepSeek-R1 model locally via Ollama, our platform sidesteps external API dependencies, minimizes latency spikes, and keeps sensitive student data entirely within institutional boundaries. This local-first approach also cuts ongoing usage fees and grants full control over model updates and prompt engineering. User surveys revealed that learners felt more supported and less isolated, echoing broader findings that AI chatbots can foster a sense of community and reduce anxiety in virtual learning environments. This article presents a comprehensive overview of the chatbot’s architecture, prompt-engineering strategies, adaptive assessment algorithms, and rigorous evaluation metrics. It demonstrates how a thoughtfully designed AI tutor can augment human teaching—freeing educators from repetitive tasks while delivering personalized, context-aware support to every student.
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Publication Date
5/20/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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