A Study on the Interface Design of Conversational AI Mobile Applications for the Elderly Based on KANO-AHP

The interface design of conversational AI mobile applications is shaped by natural language interaction, multi-turn feedback, and dynamically generated content. While these features may reduce certain operational barriers, they can also create new difficulties for older adults in understanding system functions, judging generated results, and recovering from interaction errors. To address these challenges, this study integrates the KANO model and the Analytic Hierarchy Process (AHP) to develop a systematic framework for analyzing interface requirements in conversational AI mobile applications for older users. Field surveys and semi-structured interviews were first conducted to identify 15 interface design requirements. These requirements were then classified through a KANO questionnaire into must-be, one-dimensional, attractive, and indifferent categories, with no reverse requirements identified. On this basis, an AHP hierarchy was established to determine the relative priority of each requirement. The results show that clear functional explanations, interface simplicity, absence of advertising interference, voice interaction, and error-tolerant interaction design are the key factors influencing older adults’ experience with conversational AI interfaces. Basic usability requirements mainly reduce barriers to use, while functional explanations and voice interaction help older users understand system capabilities and task procedures. Error-tolerant interaction further enhances users’ sense of security and control in dynamic and uncertain conversational contexts. These findings suggest that age-friendly design for conversational AI mobile applications should not be limited to isolated adjustments of fonts, icons, or colors. Instead, it should adopt a systematic approach centered on low-complexity interfaces, clear task guidance, interpretable feedback, and recoverable interactions. Based on the classification and weighting results, this study proposes an interface design framework for age-friendly conversational AI mobile applications, providing a reference for requirement analysis, interface optimization, and design decision-making.