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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM65775

An AI Powered Python Full Stack E-Learning Platform for Personalized Learning and Career Guidance

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM65775

Volume:

10 |

7 |

Jul 2026

Pages:

1-3

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM65775

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

M Aruldevi

M Aruldevi

Computer Science and Engineering

Kuppam Engineering College

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T Sivasankari

T Sivasankari

Computer Science and Engineering

Kuppam Engineering College

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M Swarupa Vani

M Swarupa Vani

Computer Science and Engineering

Kuppam Engineering College

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Abstract

The rapid expansion of digital education has transformed conventional learning by enabling flexible, remote, and self-paced study. However, many existing e-learning systems lack structured guidance, personalized recommendations, and career-focused learning paths, resulting in reduced learner engagement and unclear professional outcomes. This paper proposes Career Map, an AI-powered Python full stack e-learning platform designed to deliver adaptive learning experiences and intelligent career guidance. The system utilizes Django for backend services and MySQL for data management while supporting role-based access for administrators, instructors, and students. A hybrid rule-based and AI-assisted recommendation model guides learners toward suitable courses, skill pathways, and career tracks based on performance, interests, and objectives. The platform offers course management, progress tracking, adaptive learning paths, certification services, and career analytics dashboards. Experimental results demonstrate improved learner engagement, higher course completion rates, and enhanced career clarity compared to conventional learning management systems, validating the effectiveness of intelligent guidance in scalable digital education environments.

KEYWORDS

Personalized learning AI-driven e-learning Python full stack Django framework career guidance system. Keywords— Personalized learning AI-driven e-learning Python full stack Django framework career guidance system.

Cite This Article

M Aruldevi, T Sivasankari, M Swarupa Vani, "An AI Powered Python Full Stack E-Learning Platform for Personalized Learning and Career Guidance," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 7, pp. 1-3, Jul 2026. https://doi.org/10.55041/IJSREM65775

Full Article/Conclusion

The system integrates adaptive learning mechanisms with structured career pathway mapping to enhance learner engagement and professional clarity. Experimental evaluation demonstrates improved course completion rates and stronger alignment between learning activities and career objectives. The proposed architecture is scalable, cost-effective, and suitable for institutional deployment.

References

[1] R. K. Ellis, Guide to Learning Management Systems. American Society for Training and Development, 2009. [2] D. Al-Fraihat, M. Joy, R. Masa'deh, and J. Sinclair, "An empirical assessment of e-learning system effectiveness," Computers in Human Behavior, vol. 102, pp. 67–86, 2020. [3] S. Graf and Kinshuk, "Personalized learning technologies," in Handbook of Educational Communications and Technology, 3rd ed., Springer, 2008, pp. 347–359. [4] P. Brusilovsky, "Systems for personalized web-based learning," User Modeling and User-Adapted Interaction, vol. 11, no. 1–2, pp. 87–110, 2001. [5] C. Romero and S. Ventura, "Current trends in educational data mining research," IEEE Transactions on Systems, Man, and Cybernetics: Part C, vol. 40, no. 6, pp. 601–618, Nov. 2010. [6] G. Siemens, "Learning in networked digital environments: Connectivism theory," International Journal of Instructional Technology and Distance Learning, vol. 2, no. 1, 2005. [7] Django Software Foundation, "Official Django Developer Documentation," https://www.djangoproject.com/, accessed Jan. 2026. [8] Oracle Corporation, MySQL Version 8.0 Reference Manual, https://dev.mysql.com/doc/, accessed Jan. 2026. [9] Richard E. Mayer, Multimedia Learning, 2nd ed., Cambridge University Press, 2009. [10] George Siemens and Stephen Downes, Connectivism and Connective Knowledge, 2008.

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Publication Date

7/10/2026

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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