Iran finalizes national AI action plan: labs, curricula, and commercialization push
The Ministry of Science, Research, and Technology has finalized a national artificial intelligence action plan in cooperation with the Vice Presidency for Science, Technology, and Knowledge-Based Economy. The plan was unveiled at a Wednesday meeting attended by First Vice-President Mohammad-Reza Aref. It sets clear priorities to turn AI from policy intent into measurable outcomes across education, research, and industry.
What's in the plan
The action plan is built on five pillars that translate into concrete initiatives for universities, research centers, and tech teams:
- Transformation in education and training human resources
- Empowering research and innovation capacity building
- Promoting and commercializing AI
- Expanding science diplomacy and international cooperation
- Improving the scientific status of the country
Education and talent pipelines
Five national AI laboratories will be established in selected universities. Postgraduate programs will add interdisciplinary AI majors, and "AI and digital transformation" will be required for engineering undergraduates and offered as a supplementary course in other fields. Universities should begin curriculum mapping, faculty development, and lab readiness now to align with these standards.
Research capacity: labs, models, and data
A set of universities will be designated as national AI R&D centers. The plan backs development of at least three fundamental AI models-including language and vision-trained on domestic data. A secure national platform will be launched to collect and share research and educational datasets, enabling reproducibility and collaboration while protecting sensitive information.
Commercialization, startups, and competitions
The policy calls for faster company formation, AI competitions, and stronger pathways from lab results to market. Standardization and regulatory collaboration are on the agenda, alongside engagement with Iranian experts abroad. This creates room for joint ventures, spin-outs, and shared testing environments that reduce time-to-deployment.
Science diplomacy and international cooperation
Partnerships will expand with top universities in the Shanghai and BRICS networks, and with members of the Organization of Islamic Cooperation (OIC). The program also encourages collaboration on AI regulations and standards, making it easier to publish jointly, co-advise theses, and share infrastructure where feasible. Teams should identify counterpart labs and map bilateral projects early.
Improving scientific standing
Targets include maintaining and improving the country's international ranking in AI, indexing at least eight specialized journals in Q1-Q2, increasing support for top researchers and patents, and strengthening scientific associations and national AI networks. Expect new incentives around high-impact publications, quality datasets, and citation growth.
Where Iran stands now
By the Nature Index, Iran's scientific output in AI has moved from rank 33 to 30, placing the country among the global top 50. Regional rank varies between 14 and 17, with a focus on quality cited as a key factor. See the Nature Index methodology and ranking details here: Nature Index - Artificial Intelligence.
On government readiness to implement AI in public services, Iran is ranked 91 of 188 in the latest Oxford Insights report, up three places from 2023. The strongest pillar is Data and Infrastructure, scoring 66.29 vs. 55.88 last year, with sub-indicators of infrastructure (70), data availability (43), and data representativeness (121). Full report: Oxford Insights - Government AI Readiness Index.
What this means for universities, labs, and research teams
- Prepare proposals for the national AI laboratories: objectives, equipment lists, governance, industry partners, and sustainability plans.
- Update curricula now: core AI math, ML systems, data governance, and ethics for undergraduates; interdisciplinary project studios at the postgraduate level.
- Form consortia to build the three national AI models; align on compute access, benchmarks, data pipelines, and evaluation protocols.
- Stand up secure data infrastructure: metadata standards, consent/audit trails, privacy-preserving access, and reproducible ML workflows.
- Map your IP strategy: disclosure processes, patent support, and licensing frameworks that support spin-outs and joint development.
- Engage standards bodies and regulatory pilots early to reduce downstream friction for deployment.
- Connect with diaspora researchers for co-authored work, visiting appointments, and shared compute or datasets.
- Target Q1-Q2 journal partnerships and editorial development to meet the "8 journals" indexing goal.
- Use challenges and competitions to stress-test models and attract student talent to lab projects.
Practical next steps
- Nominate a cross-functional AI steering group (academics, IT, legal, tech transfer) to track calls from the Ministry and Vice Presidency.
- Audit your compute footprint and plan GPU/CPU expansions or cloud credits; document energy, security, and compliance baselines.
- Create a shared repository of domestic datasets with clear licenses, documentation, and bias/coverage assessments.
- Identify two immediate industry problems for tech transfer (e.g., vision QA, forecasting) and prototype with students or postdocs.
- Set quarterly goals for publications, datasets, and benchmarks aligned to the national model roadmap.
If your team is planning skill development for faculty, engineers, or policy leads, you can browse curated learning paths by role here: AI courses by job. It's a quick way to align training with the plan's education and deployment priorities.
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