Artificial intelligence (AI) for social innovation in health education: promoting health literacy through personalized ai-driven learning tools – a systematic review

BACKGROUND: Artificial Intelligence (AI) is transforming health education by enabling personalized, adaptive, and scalable approaches that may enhance aspects of health literacy. Despite rapid adoption, comprehensive synthesis of AI tools' impact on health literacy as social innovation is limited. Understanding these effects guides educators, developers, and policymakers in designing potentially effective, inclusive, and ethical AI interventions. This review examines generative AI models, chatbots, and adaptive learning systems in supporting health literacy globally. METHODS: A systematic review was conducted following PRISMA guidelines. Literature was identified primarily through PubMed/Medline, Scopus, and ScienceDirect. Connectedpapers.com was used exclusively as a citation chasing tool, performing both backward and forward reference searches to identify thematically linked studies not captured by database searches. All records retrieved via Connected Papers were subjected to the same eligibility criteria as database-sourced studies, covering publications from 2000-2025. A total of 75 peer-reviewed empirical and theoretical studies focusing on AI tools for health literacy and social innovation were included. Titles, abstracts, keywords, and full texts were screened using predefined criteria. Data were managed and de-duplicated using Zotero. Screening and eligibility decisions were recorded in Excel spreadsheets. Thematic synthesis was conducted manually. PRISMA 2020 and PRISMA-S checklists were used to ensure transparent reporting. RESULTS: AI research in health education was minimal until 2020 but rose sharply from 2021, peaking in 2023-2024 with generative AI (e.g., ChatGPT). Of the 75 included studies, 68 (90.7%) were co-authored by two or more researchers, 54 (72.0%) were published as Open Access, and review articles dominated with 41 studies (54.7%), while empirical research was limited, highlighting moderate to weak evidence. Research focused on personalized AI tools and learning effectiveness, with limited exploration of ethics, technical barriers, or social innovation. Findings suggest that AI interventions may improve readability, metacognitive engagement, cultural accessibility, and learner autonomy in the short term, particularly when multifaceted. However, evidence for long-term behavior change and real-world impact is sparse, indicating caution in generalizing results. Challenges include algorithmic bias, digital inequity, and lack of transparency, emphasizing the need for inclusive, equity-driven AI strategies. CONCLUSION: AI-powered tools have potential to support health literacy and learner-centered innovation, while contributing to social impact. Multifaceted, adaptive interventions may offer greater benefits than single-tool approaches. Findings provide preliminary guidance for standardized training, AI literacy integration, and policy frameworks, while acknowledging the current limitations in evidence, generalizability, and long-term outcomes.