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MCI Cognitive Care App: An AI-Powered Personalized Platform for Cognitive Training in Mild Cognitive Impairment
Subject area: Science,Engineering and Technology · Area of research: AI, Cognitive Health
DOI: https://doi.org/10.64388/IREV9I11-1717294
Abstract
Mild Cognitive Impairment (MCI) is a transitional neurological condition characterized by measurable cognitive decline beyond normal aging while preserving independence in daily activities. Individuals diagnosed with MCI face an elevated risk of progression to dementia, highlighting the importance of early and adaptive intervention. This paper presents the MCI Cognitive Care App, an AI-driven digital cognitive training platform designed to deliver personalized cognitive rehabilitation through adaptive gameplay. The system integrates a suite of cognitive exercises targeting memory, attention, processing speed, and executive function. Personalization is achieved using a reinforcement learning framework based on an Epsilon- Greedy Contextual Bandit algorithm, enabling real-time adaptation of task difficulty, hint frequency, and task sequencing. The platform is implemented as a fully functional prototype comprising a React.js frontend, Node.js backend, and Python-based AI engine. Gamification mechanisms and a multi-role dashboard support sustained engagement and collaborative monitoring by patients, caregivers, and clinicians. A pilot observational evaluation demonstrates improved engagement, smooth difficulty progression, and positive usability outcomes, indicating the feasibility of AI-driven personalization for scalable cognitive rehabilitation.
Keywords
Mild Cognitive Impairment, Reinforcement Learning, Contextual Bandits, Cognitive Training, Digital Health, Gamification
How to cite this paper
@article{1717294,
author = {Sangita Patil, Vedashree Kulkarni, Aditya Gavhane, Aditya Inamdar, Yash Sonawane},
title = {MCI Cognitive Care App: An AI-Powered Personalized Platform for Cognitive Training in Mild Cognitive Impairment},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {558-561},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717294.pdf},
abstract = {Mild Cognitive Impairment (MCI) is a transitional neurological condition characterized by measurable cognitive decline beyond normal aging while preserving independence in daily activities. Individuals diagnosed with MCI face an elevated risk of progression to dementia, highlighting the importance of early and adaptive intervention. This paper presents the MCI Cognitive Care App, an AI-driven digital cognitive training platform designed to deliver personalized cognitive rehabilitation through adaptive gameplay. The system integrates a suite of cognitive exercises targeting memory, attention, processing speed, and executive function. Personalization is achieved using a reinforcement learning framework based on an Epsilon- Greedy Contextual Bandit algorithm, enabling real-time adaptation of task difficulty, hint frequency, and task sequencing. The platform is implemented as a fully functional prototype comprising a React.js frontend, Node.js backend, and Python-based AI engine. Gamification mechanisms and a multi-role dashboard support sustained engagement and collaborative monitoring by patients, caregivers, and clinicians. A pilot observational evaluation demonstrates improved engagement, smooth difficulty progression, and positive usability outcomes, indicating the feasibility of AI-driven personalization for scalable cognitive rehabilitation.},
keywords = {Mild Cognitive Impairment, Reinforcement Learning, Contextual Bandits, Cognitive Training, Digital Health, Gamification},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717294}
}