Abstract
This paper proposed a comprehensive mixed-methods framework with varied samples of older adults, including user experience, usability assessments, and in-depth interviews with the integration of Explainable Artificial Intelligence (XAI) methods. The experience of older adults’ interaction with the E-health interface is collected through interviews and transformed into operatable databases whereas XAI methods are utilized to explain the collected interview results in this research work. The results show that XAI-infused e-health interfaces could play an important role in bridging the age-related digital divide by investigating elders’ preferences when interacting with E-health interfaces. Furthermore, the study identifies important design factors, such as intuitive visualization and straightforward explanations, that are critical for creating efficient Human-Computer Interaction (HCI) tools among older users. Furthermore, this study emphasizes the revolutionary potential of XAI in e-health interfaces for older users, emphasizing the importance of transparency and understandability in HCI-driven healthcare solutions. This study’s findings have far-reaching implications for the design and development of user-centric e-health technologies, intending to increase the overall well-being of older adults.
X. Huang and Z. Zhang—Contributed equally to this work and should be considered co-first authors.
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Huang, X., Zhang, Z., Guo, F., Wang, X., Chi, K., Wu, K. (2024). Research on Older Adults’ Interaction with E-Health Interface Based on Explainable Artificial Intelligence. In: Gao, Q., Zhou, J. (eds) Human Aspects of IT for the Aged Population. HCII 2024. Lecture Notes in Computer Science, vol 14726. Springer, Cham. https://doi.org/10.1007/978-3-031-61546-7_3
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