Background: Cognitive impairment is a common consequence of stroke, requiring effective rehabilitation strategies. Telerehabilitation has emerged as a promising alternative to in-person cognitive therapy, yet existing systems often lack mechanisms for real-time personalization and engagement monitoring. This study aimed to evaluate the clinical effectiveness of a self-guided AI-driven cognitive telerehabilitation compared to a therapist-supervised rehabilitation in subacute stroke patients.Methods: In this multicenter, parallel-group, randomized controlled non-inferiority trial, 63 participants with cognitive impairment within 6 months of stroke onset were randomized 1:1 to either a self-guided AI-driven telerehabilitation group or a therapist-supervised rehabilitation group. Both groups completed 24 sessions within 6 weeks using the same mobile platform. The primary outcomes were cognitive function measures, including the Korean Mini-Mental State Examination-2 (K-MMSE2), Trail Making Tests (A and B), and Digit Span Tests (forward and backward), with non-inferiority formally tested using the K-MMSE2. Secondary outcomes included functional independence, psychosocial measures and usability questionnaire.Results: Fifty-five participants completed the study. Both groups showed significant improvements across all primary cognitive measures, with no statistically significant differences between groups. Non-inferiority analysis confirmed that the self-guided AI-driven telerehabilitation was not inferior to the therapist-supervised rehabilitation based on K-MMSE2 changes. Usability assessment among users of the cognitive rehabilitation system indicated high overall satisfaction with no serious adverse events reported.Conclusion: This study demonstrates that a self-guided AI-driven telerehabilitation can deliver cognitive improvements comparable to a therapist-supervised rehabilitation in subacute stroke patients.Clinical trial registration: https://cris.nih.go.kr/cris/index/index.do, KCT0008969.

Background: Digital exclusion is a validated risk factor for cognitive decline in older adults. Digital interventions exhibit high dropout rates due to low digital literacy, technology anxiety, and limited adaptation to individual states, resulting in limited real-world transfer. Objective: This protocol aims to present the CTC Framework (Coach–Teacher–Companion), a tri-agent generative AI system proposed for exploring the feasibility of adaptive cognitive interventions in older adults with existing digital access. The protocol provides technical architecture, feasibility-stage implementation procedures, and methodological and ethical guidelines to assist clinicians in safely applying AI-based cognitive interventions in clinical research settings. Methods: The framework integrates three AI agents (Coach, Teacher, and Companion) designed to provide behavioral, cognitive, and emotional support. The system is designed to embed cognitive exercises in daily activities, monitor emotional states, and incorporate accessibility features for age-related limitations. Implementation safeguards include digital literacy assessment (MDPQ-16), technology anxiety monitoring (CARS), emotional safety protocols, and data privacy protections. The protocol specifies a six-week feasibility study (n$=14$, MMSE 18–25) to evaluate usability (System Usability Scale, primary outcome), user experience (UEQ-S), psychological needs satisfaction (BPNS), emotional safety (PANAS), adherence, and preliminary cognitive outcomes (MMSE, TMT-A/B, Digit Span). Conclusions: The CTC Framework is designed to provide methodological and ethical safeguards for clinical implementation, including standardized procedures for digital literacy assessment, technology anxiety management, emotional safety monitoring, and data privacy protections. Empirical validation of the framework’s feasibility and efficacy is required through future studies.

Objective: To test the feasibility and usability of an artificial intelligence (AI)-guided mobile cognitive telerehabilitation program for patients with stroke or older adults with mild cognitive impairment (MCI).Methods: Thirteen participants with cognitive impairment (Mini-Mental State Examination [MMSE] score≤26; nine with stroke and four with MCI) were enrolled in the study. Each participant was provided with an AI-guided mobile cognitive rehabilitation program (Zenicog®). Participants were instructed to complete 24 sessions within 6 weeks, and those with sufficient adherence (≥70%, 17 sessions) were included in the analysis. Cognitive assessments included the MMSE, digit span, and Trail Making Tests A & B. The usability questionnaire investigated equitable use and flexibility in use, simple and intuitive use, perceptible information, tolerance for error, low physical effort, size and space for use, overall product quality, overall satisfaction.Results: Eleven participants completed the study, and 10 participants met adherence criteria. The MMSE score increased significantly from 24.00 [21.00, 25.75] at baseline to 27.50 [26.00, 28.75] after intervention. The overall product quality (Likert scale: 1–5) score was 4.00±0.87. The lowest score in the usability questionnaire was for tolerance for error. Female participants and participants with <12 years’ education gave lower scores for tolerance for error and equitable/ flexibility in use, respectively.Conclusion: The AI-guided mobile cognitive telerehabilitation program is feasible and potentially beneficial for improving cognitive function in patients with stroke or older adults with MCI. Individuals who are less familiar with electronic devices require special consideration to improve their usability.

Kim S, et al. Brain Neurorehabil. 2026 ;19:e2. https://doi.org/10.12786/bn.2026.19.e2