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| Published: July 19, 2026
AI Anxiety, Trust, and Literacy: A Mixed Method Study of AI Attitudes Across Generations
Research Scholar, Women’s Christian College, Chennai - 600006, India
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Research Scholar, Women’s Christian College, Chennai - 600006, India
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Assistant Professor, Women’s Christian College, Chennai - 600006, India
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Associate Professor, Women’s Christian College, Chennai - 600006, India
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DIP: 18.01.017.20261403
DOI: 10.25215/1403.017
ABSTRACT
Artificial intelligence (AI) has become a driving force for the new round of industrial transformation across the globe (Wu et al., 2020). This rapid increase of AI has raised important questions pertaining to AIT, AIA, and AIAT across generations (Zubair et al, 2025). This study aimed to examine and compare generational differences in attitudes towards AI among Gen X, Gen Y, and Gen Z. Data was collected through convenience sampling methods which included surveys and semi-structured interviews. Artificial Intelligence Anxiety Scale (AIAS), Short Trust in Automation Scale (S-TIAS), Meta AI Literacy Scale (MAILS), and Artificial Intelligence Attitude Scale (AIAS-4) were used to collect data. Pearson correlation revealed a significant relationship between AI Trust and AI Attitudes and between AI Literacy and AI Attitudes. Kruskal–Wallis Test revealed no generational differences while gender differences were found in AI Trust and AI Anxiety. Reflexive thematic analysis on the data collected through semi-structured interviews were consistent with the correlation analysis whereas there were generational differences in AI trust, AI anxiety and AI literacy. These findings help us gain deeper insights on the mindset of people and elements that need to be considered when designing various AI technologies. Generational gap can also be bridged by addressing the results of the study.
Keywords
Artificial intelligence, Generational Differences, AI Anxiety, Trust in AI, AI Literacy, AI Attitudes, Mixed-Methods
This is an Open Access Research distributed under the terms of the Creative Commons Attribution License (www.creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any Medium, provided the original work is properly cited.
© 2026, Briscila, E.A., Caren, R.Z.P., Harshini, A., & Zarina, A.
Received: May 04, 2026; Revision Received: July 15, 2026; Accepted: July 19, 2026
Article Overview
ISSN 2348-5396
ISSN 2349-3429
18.01.017.20261403
10.25215/1403.017
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Published in Volume 14, Issue 3, July-September, 2026
