Who adapts to whom: technology or older adults? Mechanisms of technology anxiety among older AI users

TL;DR

This study explores why older adults feel anxious about AI technology and suggests that broader societal and systemic changes are needed to help them adapt.

Contribution

The study reveals a multi-level mechanism of AI technology anxiety in older adults, emphasizing the role of social ageism and structural barriers.

Findings

Technology anxiety in older adults is directly triggered by AI literacy gaps, physical limitations, and complex tech.

Social ageism, cognitive decline, and resource barriers are key drivers of AI anxiety.

Systemic interventions are needed beyond training or interface improvements to address deep-rooted issues.

Abstract

As AI rapidly permeates diverse social domains, technology related anxiety among older adults during adaptation, particularly in the context of AIGC, has become a major barrier to digital inclusion. This study aims to systematically uncover the generative mechanism and hierarchical transmission pathway of older adults’ AI technology anxiety and to derive intervention implications.

A mixed methods design was adopted. First, in depth interviews were conducted with 36 older AIGC users, and 14 core categories were derived using grounded theory. Second, an integrated analysis using Interpretive Structural Modeling (ISM) and Cross Impact Matrix Multiplication Applied to Classification (MICMAC) was performed to identify the hierarchical structure of influencing factors and their driving and dependence relationships.

ISM revealed a clear hierarchical transmission pathway. Technology anxiety is directly triggered by surface factors including insufficient AI literacy, physiological functional limitations, and technological complexity. It is transmitted through intermediate factors and ultimately driven by the deep rooted factor of social ageism. MICMAC further identified cognitive decline, social ageism, and basic resource barriers as high driving and low dependence independent factors. Insufficient AI literacy and technological complexity were categorized as high dependence surface factors whose improvement relies on systemic interventions.

The findings demonstrate a multi level mechanism in which deep structural forces shape surface level anxiety experiences, suggesting that training or interface optimization alone may be insufficient. Coordinated interventions across policy guidance, inclusive technology design, and community support network development are proposed to help reduce the older adult digital divide.

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