The Gap between Subjective and Objective AI Literacy: Operationalizing AI Maturity in Tehran

Ojagh, Zahra; Hasanzadeh, Mohammad

doi:10.22034/kps.2026.592614.1289

The Gap between Subjective and Objective AI Literacy: Operationalizing AI Maturity in Tehran

Document Type : Original Research Manuscripts

Authors

Zahra Ojagh ¹

Mohammad Hasanzadeh ²

¹ Department of Communication of Science and Technology, Faculty of Cultural Studies and Communication, Institute for Humanities and Cultural Studies

² Department of Information and Knowledge Science, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran

10.22034/kps.2026.592614.1289

Abstract

This study examines AI maturity among citizens of Tehran by analyzing the gap between subjective and objective AI literacy as an indicator of cognitive misalignment in engagement with AI technologies. Using a quantitative cross-sectional survey design, data were collected from 507 residents of Tehran. Subjective AI literacy was measured using the AILS, while objective AI literacy was assessed through the AICOS, a specialized instrument comprising 51 objective and generative items. Reliability was confirmed using Cronbach’s alpha and KR-20. Data were analyzed using independent-samples t tests, Mann-Whitney tests, Spearman correlation, and paired-samples t tests.
The mean score for subjective AI literacy was 4.99 out of 7, whereas objective literacy scores across dimensions ranged from 0.07 to 0.10 on a 0-1 scale. A paired-samples t test confirmed a significant gap between the two dimensions with a very large effect size (dz = 4.27). The correlation between subjective and objective AI literacy was weak and non-significant (rs = 0.062). Generational analysis further showed that younger respondents (born after 1373) scored significantly higher in subjective AI literacy, but did not differ significantly from older respondents on most components of objective AI literacy. The findings indicate that AI maturity in this population is characterized by a structural misalignment between perceived competence and actual knowledge. Increased access to and use of AI tools has not necessarily translated into conceptual understanding, critical evaluation, or ethical awareness. Advancing AI maturity therefore requires educational and cultural policies that align use with understanding, critical judgment, and realistic self-assessment.

Keywords

AI maturity

subjective AI literacy

objective AI literacy

cognitive misalignment

generational differences

Subjects

Knowledge management

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