Cultural Approach to Artificial Intelligence Literacy in Gen Z Students

Document Type : Original Article

Authors

1 PhD Student in Educational Psychology, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.

2 Professor, Department of Educational Psychology, Allameh Tabataba’i University, Tehran, Iran.

3 Assistant Professor, Department of E-Learning in Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

4 Associate Professor, Department of Cultural Management and Planning, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.

5 Associate Professor, Department of Psychology, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.

Abstract

A B S T R A C T
The present study aimed to identify the components of artificial intelligence literacy in Generation Z students with a cultural approach and within the framework of qualitative research using the inductive qualitative content analysis method based on the Braun & Clarke (2006) model. Based on the principle of theoretical saturation, 10 experts in the field of study were interviewed using a purposive sampling method dependent on the semi-structured interview criterion. To assess data validation, two methods were used: double-coder review, review by experts not participating in the research, and return to the interviewees. The data were analyzed through several stages of open and axial coding. To analyze the qualitative research data, the content analysis form was used through the MAXQDA2022 software. The results showed that the components of AI literacy in Generation Z students with a cultural approach include 6 categories of components: awareness of cultural orientation, sensitivity to cultural differences, caring literacy, cultural responsibility, cultural future-building with AI, and cultural flexibility. The findings show that AI literacy for Generation Z should not be limited to technical skills but should also have a socio-cultural approach.

Keywords

References

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History

  • Receive Date: 05 October 2025
  • Revise Date: 10 December 2025
  • Accept Date: 05 January 2026
  • First Publish Date: 05 January 2026
  • Publish Date: 21 March 2026

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