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Paper ID:IJSREM63364

AskNLearn - AI-Powered Educational Platform

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63364

Volume:

10 |

5 |

May 2026

Pages:

1-20

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM63364

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.

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Authors

Arjumand Masood Khan

Arjumand Masood Khan

Department of Computer Science &Engg.

Government College Of Engineering,Chandrapur

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AC

Aditya Chudhari

Department of Computer Science &Engg.

Government College Of Engineering,Chandrapur

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HG

Harsh Gorantiwar

Department of Computer Science &Engg.

Government College Of Engineering,Chandrapur

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SP

Sujal Patil

Department of Computer Science &Engg.

Government College Of Engineering,Chandrapur

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kB

kartik Bandpatte

Department of Computer Science &Engg.

Government College Of Engineering,Chandrapur

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ST

Sujata Taksande

Department of Computer Science &Engg.

Government College Of Engineering,Chandrapur

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AV

Archita Vairagade

Department of Computer Science &Engg.

Government College Of Engineering,Chandrapur

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Mentors

MMK

Ms.Arjumand Masood Khan

Department of Computer Science &Engg.

Government College Of Engineering,Chandrapur

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Abstract

AskNLearn is an AI-powered educational platform designed to improve the learning and revision process through intelligent content generation, adaptive practice, and performance analytics. Traditional study methods often require students to use multiple disconnected tools for notes, quizzes, and progress tracking, resulting in reduced efficiency and fragmented learning experiences. The proposed system provides a unified platform where users can upload or enter study material in multiple formats, including text, documents, images, audio, video, and URLs. The system processes the input using OCR, transcription, and AI-based content extraction techniques to generate structured learning resources such as summaries, flashcards, multiple-choice questions, and memory maps. The platform also includes subject-wise quiz practice, adaptive learning support, dashboard analytics, gamification features, and secure authentication mechanisms. AskNLearn is developed using React.js, Node.js, Express.js, FastAPI, MySQL, and AI model APIs such as Gemini/OpenAI. The architecture follows a dual-backend approach, separating application services from AI-processing tasks to improve scalability, maintainability, and system performance. The implementation demonstrates that integrating artificial intelligence with modern full-stack technologies can significantly improve revision efficiency, personalized learning, and student engagement. The proposed platform reduces the manual effort involved in study preparation while encouraging continuous learning through personalized practice, analytics-driven feedback, and interactive educational tools. Overall, the system demonstrates the practical application of artificial intelligence in enhancing digital education and building intelligent learning environments suitable for modern students and educational institutions. .

KEYWORDS

Keywords: AskNLearn Artificial Intelligence Adaptive Learning Educational Platform Quiz Analytics FastAPI AI-Based Learning Smart Education

Cite This Article

Arjumand Masood Khan, Aditya Chudhari, Harsh Gorantiwar, Sujal Patil, kartik Bandpatte, Sujata Taksande, Archita Vairagade, Ms.Arjumand Masood Khan, "AskNLearn - AI-Powered Educational Platform," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-20, May 2026. https://doi.org/10.55041/IJSREM63364

Full Article/Conclusion

Crime Education and learning management have become increasingly dependent on digital technologies, intelligent automation, and personalized educational systems. Traditional learning methods often involve manual preparation of notes, fragmented study workflows, limited analytics, and inefficient revision processes. Therefore, the integration of Artificial Intelligence and modern web technologies into educational systems has become an important area of development. This research paper presented a comprehensive study of AskNLearn, an AI-powered educational platform developed using modern full-stack technologies, Artificial Intelligence, OCR systems, transcription tools, and adaptive learning techniques. The platform provides a centralized learning environment where students can upload study material, generate structured learning resources, practice adaptive quizzes, track progress, and access analytics-driven educational insights. The study also discussed important modules such as secure authentication, AI-based content generation, OCR and transcription processing, adaptive quizzes, dashboard analytics, gamification systems, and real-time community learning support. These features improve revision efficiency, personalized learning, student engagement, and accessibility of educational resources. The implementation results show that the developed system performs efficiently under normal operating conditions. Core modules such as AI generation, quiz systems, analytics dashboards, and secure authentication demonstrated reliable performance and positive user experience. The project proved that Artificial Intelligence combined with modern web technologies can significantly improve digital learning workflows and educational productivity. Although the platform achieved successful results, challenges such as AI dependency, OCR limitations, scalability requirements, media processing delays, and security considerations still remain. These limitations can be addressed through future enhancements such as advanced AI recommendation systems, cloud-native deployment, mobile applications, and stronger security mechanisms. In conclusion, AskNLearn provides a practical, scalable, and intelligent solution for AI-assisted education and adaptive digital learning. With continuous advancements in Artificial Intelligence and educational technologies, such platforms can play an important role in building smarter, more personalized, and more efficient learning environments for students and educational institutions.

References

[1]        T. Brown, B. Mann, N. Ryder et al., “Language Models are Few-Shot Learners,” Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 1877–1901, 2020.  [2]        A. Vaswani, N. Shazeer, N. Parmar et al., “Attention Is All You Need,” IEEE Transactions on Neural Networks and Learning Systems, pp. 5998–6008, 2017. [3]        J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” Proceedings of NAACL-HLT, pp. 4171–4186, 2019. [4]        OpenAI, “GPT Models and AI-Based Educational Applications,” Available: https://openai.com, Accessed: 2026. [5]        Google, “Gemini API Documentation,” Available: https://ai.google.dev, Accessed: 2026. [6]        R. Smith, “An Overview of the Tesseract OCR Engine,” Proceedings of the International Conference on Document Analysis and Recognition, pp. 629–633, 2007. [7]        A. Radford, J. Kim, T. Xu et al., “Robust Speech Recognition via Large-Scale Weak Supervision,” OpenAI Whisper Research Paper, 2022. [8]        Facebook Inc., “React.js Documentation,” Available: https://react.dev, Accessed: 2026. [9]        FastAPI, “FastAPI Framework Documentation,” Available: https://fastapi.tiangolo.com, Accessed: 2026. [10]    Oracle Corporation, “MySQL Reference Manual,” Available: https://dev.mysql.com/doc, Accessed: 2026.

Editorial & Review Transparency

Publication Date

5/17/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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