Pilot evaluation demonstrates improved cognitive training engagement in individuals with mild cognitive impairment, suggesting feasibility for scalable interventions.
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.
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Patil et al. (2026) studied this question.
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