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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67775
Volume:
10 |
10 |
Oct 2026
Pages:
1-3
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.
Electronics
Siddhant College of Engineering, Sudhambare
Electronics
Siddhant College of Engineering, Sudhambare
Electronics
Siddhant College of Engineering, Sudhambare
Electronics
Siddhant College of Engineering, Sudhambare
This paper presents the design of a low-cost Artificial Intelligence of Things platform for personalized cognitive and memory training for children with special needs. The system separates deterministic physical interaction from multimedia presentation and adaptive analytics. An ESP32-S3 console provides four large colored inputs, a Start or Hint control, visual feedback and audio cues, while a Python application manages visual, working, spatial and associative memory activities. Trial accuracy, response latency, sequence span, attempts, hints and delayed recall are stored locally. A Random Forest model estimates success probability for candidate activities, and transparent guardrails constrain recommendations when learner history is limited. The proposed architecture supports reproducible evaluation, educator oversight and privacy-preserving local operation. It is intended for assistive training and progress monitoring rather than clinical diagnosis.
KEYWORDS
The proposed AIoT platform integrates accessible physical interaction, local multimedia activities, structured telemetry and explainable adaptive selection. Its separation of embedded control from edge analytics simplifies implementation and supports privacy-preserving operation. The next stage is to complete hardware integration, verify all scoring and communication paths, and conduct an ethically approved pilot using the defined baseline and delayed-recall measures.
[1] A. Baddeley, Working Memory, Thought, and Action. Oxford University Press, 2007. [2] A. Baddeley, 'The episodic buffer: a new component of working memory?' Trends in Cognitive Sciences, vol. 4, no. 11, pp. 417-423, 2000. [3] N. J. Cepeda, H. Pashler, E. Vul, J. T. Wixted, and D. Rohrer, 'Distributed practice in verbal recall tasks: a review and quantitative synthesis,' Psychological Bulletin, vol. 132, no. 3, pp. 354-380, 2006. [4] L. Breiman, 'Random forests,' Machine Learning, vol. 45, pp. 5-32, 2001. [5] Espressif Systems, ESP32-S3 Series Datasheet, 2024. [6] Python Software Foundation, Python 3 Documentation, 2026. [7] SQLite Consortium, SQLite Documentation, 2026. [8] World Health Organization and UNICEF, Global Report on Assistive Technology. Geneva: WHO, 2022.
Publication Date
10/12/2026
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.
Funding Information
No funding information available for this research.
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