Spektrum Iran

Algorithmic reconstruction of Iranian identity; A futures study of artificial intelligence in Iran's communication culture

Document Type : Original Research Paper

Authors

Tahereh Jahanparvar 1
Mohammad Mehdi Jahanparvar 2

1 PhD Candidate, Department of Social Communication Sciences, Faculty of Culture and Communication, Soore International University, Tehran, Iran

2 Assistant Professor, Department of Islamic Studies, Faculty of Medical Sciences, Islamic Azad University, Tehran, Iran

10.22034/spektrum.2026.592954.1077
Abstrakt
The convergence of AI and IoT is redefining cultural norms and collective identity at an unprecedented pace. In Iran, this transformation carries deeper complexity due to its ancient civilization, rich heritage rooted in Iranian-Islamic values, and collectivist social structure. This study investigates how intelligent algorithms and connected objects are reshaping communicative cultural norms and identity among Iranians, and projects plausible future trajectories. Employing a qualitative futures studies methodology, we conducted environmental scanning to extract and analyze cultural biases embedded in algorithms of three major domestic platforms—Snapp, Divar, and Rubika. We applied Causal Layered Analysis and the Critical Uncertainty Matrix to develop scenarios for the 2041 horizon. Three distinct scenarios emerged: (1) "Silent Assimilation"—accelerated algorithmic redefinition of norms without societal resistance; (2) "Indigenous Resilience"—development of algorithms aligned with Iranian-Islamic ethics and local priorities; and (3) "Schizoid Ambivalence"—dual existence within conflicting value systems, generating profound identity crisis. Current evidence indicates Iranian society is trending toward the third scenario. Algorithms are never neutral; they participate as non-human cultural agents in redefining identities through visibility, behavioral prediction, and normalization of biases. Iranian users are not passive; they employ circumvention, hidden resistance, creative reinterpretation, and selective rejection. The prevalence of the "ambivalent actor" identity type (40% of the sample) serves as a serious warning. The findings underscore a critical transition from content-centric to intelligent algorithmic governance. We propose strategic investment in indigenous ethical AI and promotion of critical algorithmic literacy as key policy strategies.

Keywords

Algorithmic reconstruction of identity
artificial intelligence
Iranian communication culture
cultural futures studies
Subjects
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Spektrum Iran
Volume 39, Issue 1
January 2026
Pages 269-301

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