Conversational AI in cognitive and social training for people with dementia : a systematic review

Abstract

Background: Conversational artificial intelligence (AI), including text-based chatbots, voice-based agents, multimodal systems, and socially assistive robots (SARs), offers a scalable adjunct to therapist-led dementia care. The post-2022 emergence of large language models (LLMs) has accelerated development, yet few reviews apply a unified conversational AI taxonomy across dementia care. This review synthesized the effectiveness, limitations, and implementation challenges of conversational AI across the dementia care continuum. Methods: Six databases (PubMed, Embase, Web of Science, Scopus, IEEE Xplore, ACM Digital Library) were searched for English-language studies (January 2010–March 2026) evaluating conversational AI targeting cognitive, social, or caregiver outcomes. Two reviewers independently screened and extracted data following PRISMA 2020 guidelines; risk of bias used standard tools and findings were synthesized narratively. Protocol: PROSPERO CRD420261333625. Results: Forty studies (8 randomized controlled trials [RCTs], 32 non-randomized) were included. SARs were the largest category (n = 24; 60.0%), followed by text-based chatbots (n = 12; 30.0%), multimodal systems (n = 3; 7.5%), and voice-based chatbots (n = 1; 2.5%). The strongest cognitive evidence came from a social robot RCT (gain of 3.9 points on a 30-point screening measure (p < 0.001). For caregivers, an international RCT (n = 274) showed significant reductions in depression (d = 0.37) and burden (d = 0.34). LLM-based systems produced an 18-fold increase in conversation duration. Speech recognition failure was the most consistently reported technical barrier. Conclusions: Conversational AI shows directional benefit across cognitive, social, and caregiver outcomes. Critical research gaps remain regarding voice-only randomized evidence and adequately powered LLM trials against usual care.