Conversational AI in Cognitive and Social Training for People with Dementia: A Systematic Review.

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.