EXPLORING THE POLICY GAP OF ARTIFICIAL INTELLIGENCE IN THE ENGINEERING CURRICULUM IN IRAN

Rashidi, Zahra

doi:10.22047/ijee.2026.585492.2254

EXPLORING THE POLICY GAP OF ARTIFICIAL INTELLIGENCE IN THE ENGINEERING CURRICULUM IN IRAN

Document Type : Scientific - Research

Author

Zahra rashidi

Assistant Professor of the department of educational innovation and training, institute for research and planning in higher education, tehran, iran

10.22047/ijee.2026.585492.2254

Abstract

The purpose of this study was to explore the policy gap of artificial intelligence in the engineering curriculum in Iran and to formulate policy implications and recommendations for reducing this gap. The study was conducted using a qualitative approach within an interpretivist paradigm. Data were collected from three complementary sources: analysis of upstream and policy documents, semi-structured interviews with 22 key actors related to engineering education in technical and engineering universities and faculties in Tehran, and review of international reference documents in the field of artificial intelligence and engineering education. Documentary data were analyzed using directed content analysis, interview data were analyzed through thematic analysis, and the integrated findings were examined through the logic of gap analysis and contextualized policy transfer. The findings showed that official policies mainly frame artificial intelligence in relation to technological development, training of specialized human resources, skill development, and digital infrastructures, whereas the executive experiences of actors indicate fragmented implementation, ambiguity in educational roles, gaps in data-related and ethical rules, and slow curriculum revision processes. The integration of documentary and field findings showed that the policy gap of artificial intelligence in the engineering curriculum can be formulated in four dimensions: discursive, pedagogical, engineering curriculum governance, and infrastructural–data-related. Based on these gaps and considering international reference documents, policy implications and recommendations were presented at macro, meso, micro, and cross-cutting levels. The study concludes that reducing the policy gap of artificial intelligence in the engineering curriculum requires the simultaneous connection of policymaking, curriculum governance, learning design, and data-related rules.

Keywords

Artificial intelligence

engineering education

curriculum

policy gap

curriculum governance

20.1001.1.16072316.1405.28.110.3.0

Subjects

Assessment of Courses and Programs in Engineering

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