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| Published: July 19, 2026

AI Anxiety, Trust, and Literacy: A Mixed Method Study of AI Attitudes Across Generations

Briscila Esther A

Research Scholar, Women’s Christian College, Chennai - 600006, India Google Scholar More about the auther

, Caren Rebeccah Zebuline P

Research Scholar, Women’s Christian College, Chennai - 600006, India Google Scholar More about the auther

, Harshini A

Assistant Professor, Women’s Christian College, Chennai - 600006, India Google Scholar More about the auther

, Dr.  Zarina Ahmed

Associate Professor, Women’s Christian College, Chennai - 600006, India Google Scholar More about the auther

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.

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Briscila Esther A @ a.briscila24@gmail.com

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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