ABSTRACT 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.
Alon et al. (Wed,) studied this question.
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