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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM57121

Personalized Learning Through Cognitive State Analysis: A Context-Aware AI Companion

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM57121

Volume:

10 |

3 |

Mar 2026

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM57121

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

MSV

Mr. Somagani Venkatesh*1

EM

Eeri Manusha*2

TBR

Tarumani Bharath Raj*3

KV

Korupula Vamshi*4

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Abstract

Personalized Learning through Cognitive State Analysis is an intelligent web-based learning system that monitors a learner’s attention level using real-time webcam analysis. The system detects facial presence and eye activity using OpenCV and Haar Cascade algorithms to determine whether the learner is attentive or distracted. Based on the detected cognitive state, the system dynamically adapts the learning content. If the learner is attentive, video-based learning continues. If the learner is distracted multiple times, the system recommends alternative learning modes such as text-based content or quizzes. The platform also provides a dashboard to analyze attentive time, distracted time, and overall performance. This system aims to improve learning efficiency, engagement, and personalization in digital education environments.

KEYWORDS

Personalized Learning Cognitive State Analysis Haar Cascade OpenCV Adaptive Learning Attention Detection E-learning.

Cite This Article

Mr. Somagani Venkatesh*1, Eeri Manusha*2, Tarumani Bharath Raj*3, Korupula Vamshi*4, "Personalized Learning Through Cognitive State Analysis: A Context-Aware AI Companion," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 3, Mar 2026. https://doi.org/10.55041/IJSREM57121

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Publication Date

3/2/2026

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Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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