Smart Autonomous System for Adaptive Student Profiling in Personalized Learning Environments

DOI

10.2991/978-94-6239-789-7_29How to use a DOI?

Keywords

Adaptive Student Profiling; Personalized Learning Systems; Multi-Agent Architecture; Autonomous Decision-Making; Educational Data Mining

Abstract

The proposed Smart Autonomous System for Adaptive Student Profiling delivers a fully implemented and experimentally validated framework for dynamic personalization in learning environments. The system integrates multi-source educational data, including academic, behavioral, cognitive, contextual, social, and emotional parameters, to construct a continuously evolving student profile. The architecture employs KNN-based imputation, PCA for dimensionality reduction, CNN for emotion recognition, XGBoost for profiling and risk prediction, and Deep Q-Networks for autonomous decision-making. A hybrid recommendation model ensures precise content delivery and learning path optimization. The system operates through a multi-agent architecture enabling real-time profiling, proactive intervention, and continuous feedback-driven updates. Experimental evaluation on benchmark datasets demonstrates significant improvements in engagement, learning outcomes, and early risk detection accuracy, confirming the robustness and scalability of the proposed framework.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Anjali Kapoor
AU  - Mridula Singh
AU  - Himanshu Tyagi
PY  - 2026
DA  - 2026/10/08
TI  - Smart Autonomous System for Adaptive Student Profiling in Personalized Learning Environments
BT  - Proceedings of theInternational Conference on Innovation and Practices in Artificial Intelligence (ICIPAI 2026)
PB  - Atlantis Press
SP  - 411
EP  - 427
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-789-7_29
DO  - 10.2991/978-94-6239-789-7_29
ID  - Kapoor2026
ER  -