Perceptions of Potentials and Risks of Artificial Intelligence Across Diverse Levels of AI-literacy

Abstract

Perceptions of the potential benefits and risks of artificial intelligence (AI) vary widely among people with different levels of knowledge about the technology. By better understanding these variations, we can identify and address challenges in the adoption and responsible use of AI for all. This study employs a qualitative approach to explore these differences through criterion sampling. A total of 15 participants who self-identified as belonging to one of three AI-literacy categories (Limited, Intermediate, and Expert) were interviewed. The audio-recorded and transcribed interviews were thematically analyzed, identifying differences in understanding among different literacy groups regarding AI’s impact on employment, creativity, bias, trust, data privacy, and the need for regulation and awareness. The study reveals that varying levels of AI-literacy shape perceptions of bias, privacy, creativity, job security, and trust. A key finding was that participants with limited AI-literacy did not link AI to data and therefore did not associate concerns like data privacy with AI, and also did not believe that AI can be biased, as they viewed bias as a human quality rather than something a machine could possess. These findings underline the need for initiatives that strengthen AI-literacy across different AI user groups to enable informed and equitable engagement with AI technologies, while also highlighting the need for education, transparency, and policy measures that build trust and accountability. In addition, how diverse users understand and interact with AI has important implications for developers, educators, and policymakers seeking to advance explainability and inclusive literacy. 

Received: 23 February 2025 | Revised: 1 August 2025 | Accepted: 13 January 2026 

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data available on request from the corresponding author upon reasonable request.

Author Contribution Statement

Zeba Khadhijah: Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualisation, Project administration. Bhupesh Kumar Mishra: Conceptualisation, Methodology, Software, Validation, Resources, Writing – review & editing, Visualisation, Supervision.