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Rural Chinese older people face significant information poverty and elevated health anxiety, yet their health information behavior remains understudied in non-Western contexts. This study employs in-depth interviews, grounded theory, and information horizon mapping to investigate the health information seeking patterns of this vulnerable group. Our findings reveal a concentric life circle pattern, where health information behavior radiates outward from the core life circle (kinship ties/close community) to transitional life circles (extended networks). By integrating Chatman's small world theory and life in the round theory with China’s localized context, we propose the original "Concentric Life Circle Theory" (CLCT). This theory advances cross-cultural information behavior research and offers actionable solutions to mitigate health anxiety through culturally tailored information interventions.

Generative Artificial intelligence (GenAI) has provided opportunities for multimodal expressions among people who lack AI literacy like older adults. A 10-lesson AI literacy for lifelong learning (AILL) programme was designed for 23 older adults aged 56 to 75 in Hong Kong to enable them to interact with AI to create songs, images, and stories. Adopting a mixed-methods approach, this study investigated the impact of AILL on the participants’ AI literacy, well-being, social connection and collaboration. Results indicated that the AILL programmes significantly enhanced the participants’ psychological well-being, life satisfaction, and reduced loneliness. Qualitative interviews identified four key themes: AI literacy, well-being, socialization and ethical concerns. These themes highlight technological, psychological, ethical, and pedagogical dimensions, offering valuable insights for policymakers and professionals in gerontology. This study enhances the existing AI literacy model by incorporating the concept of “Enable AI” to advance the framework to GenAI literacy. This updated model aims to foster an AI-inclusive society that supports and empowers older adults who can learn other knowledge with the support of AI.

As artificial intelligence (AI) increasingly shapes how individuals seek and evaluate information, older adults (75+) encounter distinct challenges in navigating AI-driven systems. While AI-powered tools can enhance information access, decision-making, and digital communication, older users may struggle with algorithm opacity, trust calibration, and cognitive overload, leading to misinterpretation, overreliance, or disengagement. This paper introduces a theoretical framework that examines how older adults interact with AI-based information technologies through three interrelated mechanisms: cognitive adaptation, trust calibration, and behavioral reinforcement. By integrating insights from cognitive science, human-computer interaction, and information behavior, the framework highlights the barriers older users face and the strategies needed to improve AI engagement. The study identifies key design considerations, including progressive model refinement, transparent feedback mechanisms, and user-driven customization, to enhance explainability, trust, and usability in AI systems tailored for aging populations. As a first step in a larger empirical study, this research lays the groundwork for future qualitative and quantitative investigations into how older adults navigate AI-generated information, assess reliability, and develop long-term AI engagement patterns. Overall, this study contributes to the broader effort to create inclusive, user-friendly, and transparent AI-driven information environments for older adults.