Najran University has showcased its experience in developing and operating an advanced artificial intelligence system at its own data center during a national workshop on open-source large language models (LLMs), organized by the Digital Government Authority and attended by several government entities. The presentation, delivered by Eng. Adel Alkhalifi, Director of Data Management and Emerging Technologies at Najran University, highlighted how the university operates open-source AI models locally, keeping data within its own domain while allowing new use cases to be added without depending on external cloud service providers.
The workshop reflects a widening national conversation in the Kingdom of Saudi Arabia about how public institutions can adopt generative AI responsibly, securely, and at scale. Rather than treating artificial intelligence as a distant experiment, Najran University presented a working system already supporting dozens of internal services, thousands of digital twins, and tens of thousands of automated translations and image descriptions. For observers of Saudi Arabia’s technology sector, the session offered a practical case study in how a regional university can build sovereign AI capability on its own infrastructure. Could this model become a blueprint for other Saudi institutions preparing to integrate AI into daily operations?
Context and Background
The workshop on open-source large language models was organized by the Digital Government Authority, the Saudi agency responsible for advancing digital government services and standards across the Kingdom. It brought together several government entities to examine how open-source AI models can be deployed in ways that protect sensitive data, reduce reliance on foreign cloud platforms, and create reusable technical capacity inside public institutions. Najran University was invited to present its operational experience, placing it among the early Saudi institutions to run AI models entirely within their own data centers.
Najran University’s approach is rooted in a simple principle: data generated by the university should remain under the university’s control. By hosting open-source AI models locally, the institution avoids sending sensitive information to external servers while retaining the flexibility to customize models for its own academic and administrative needs. The Deanship of Digital Transformation and Knowledge Resources built the supporting toolset that makes this possible, including systems to manage AI models, monitor servers, balance workloads, manage access permissions, and track usage across the institution.
The timing of the workshop is significant. Saudi Arabia has been accelerating its national digital transformation under Vision 2030, with government entities increasingly expected to demonstrate measurable progress in automation, data governance, and AI adoption. Universities sit at the intersection of research, education, and public service, making them natural testing grounds for technologies that must eventually scale across ministries, agencies, and private sector partners. What does it take for an institution to move from pilot projects to production-grade AI systems?
Key Details of the Najran University System
During the workshop, Eng. Adel Alkhalifi explained that the university’s data center now operates open-source AI models locally, a configuration that keeps institutional data inside its own domain. This local-first model allows the university to add new use cases without relying on external cloud service providers, giving it greater control over cost, performance, and compliance. The system is not a single application but a platform, supported by tools developed internally by the Deanship of Digital Transformation and Knowledge Resources.
Those tools handle several critical functions. They manage the lifecycle of AI models, monitor the health and performance of servers running those models, distribute workloads to maintain efficiency, control which users and departments can access specific capabilities, and track usage patterns over time. Together, these functions transform raw open-source models into governed institutional services, an important distinction for any organization handling student records, research data, or administrative information.
The university also showcased several projects built on the system. These include an AI-powered data classification project, which helps organize and categorize institutional information; UniTwin, a digital twin platform for students that has had 5,943 digital twins in operation since November 2025; an AI platform for project management; and a smart environment supporting scientific research. Each project demonstrates a different dimension of the platform, from operational efficiency to personalized student services to research enablement. How many universities in the region can point to nearly six thousand active digital twins as a working service rather than a concept?
AI in the University’s Electronic Portal
One of the most concrete applications presented at the workshop involves the university’s electronic portal. Najran University has used AI to automate content translation and generate image descriptions, improving accessibility and multilingual reach for its online services. According to the figures shared at the workshop, 22,025 out of 24,131 pieces of content have been translated, reaching 91% completion, while 40,281 out of 44,074 images have been described, also at 91% completion.
These numbers matter because they reflect sustained operational deployment rather than a one-time pilot. Automating translation and image description at this scale reduces manual workload, speeds up updates, and ensures that portal content is more usable for diverse audiences, including international students, researchers, and visitors. The 91% completion rate across both workstreams also suggests a repeatable process that can be extended to remaining content and future additions.
The portal work illustrates a broader principle behind the university’s AI strategy: choose high-volume, repetitive tasks where automation delivers immediate and measurable value, then build governance around them. Translation and image description are ideal candidates because they are continuous, rules-based enough to benefit from AI assistance, and directly tied to user experience. What other university services could benefit from the same disciplined approach to automation?
International and Regional Impact
Najran University’s presentation at a Digital Government Authority workshop places Saudi Arabia’s regional institutions within a national conversation about AI sovereignty. Across the Middle East, governments and universities are weighing whether to depend on global cloud providers for AI services or to build local capacity. The Najran model leans toward local control, demonstrating that open-source models can be operated effectively on-premises when paired with the right management tools and technical expertise.
For international audiences, the development signals that Saudi Arabia’s AI ambitions extend beyond flagship megaprojects and national strategies into the daily operations of universities and government entities. A system that classifies data, supports research, manages projects, and serves students through digital twins represents practical capacity building. It also creates a reference point for other Saudi institutions that may adopt similar architectures, potentially strengthening the Kingdom’s position as a regional hub for applied AI. Could this workshop mark the beginning of a shared playbook for sovereign AI deployment across Saudi public institutions?
Economic and Strategic Significance
Running open-source AI models locally has economic implications that go beyond technology. By reducing reliance on external cloud service providers, institutions can manage long-term costs, avoid recurring licensing or usage fees tied to foreign platforms, and retain flexibility in how they scale. For a university, those savings can be redirected toward research, teaching, and student services. The platform approach also means that each new use case builds on existing infrastructure rather than requiring a separate procurement cycle.
Strategically, local operation of AI models supports data sovereignty, a priority for Saudi Arabia as it expands its digital economy. Keeping data within institutional domains simplifies compliance with national data policies and reduces exposure to cross-border data transfer risks. The tooling developed by the Deanship of Digital Transformation and Knowledge Resources, covering model management, server monitoring, workload balancing, permissions, and usage tracking, is precisely the kind of operational layer that turns experimental AI into governed, auditable services. What value does that kind of governance create for a public institution over the next decade?
Vision 2030 Alignment
The Najran University presentation aligns closely with Vision 2030, Saudi Arabia’s national transformation plan, which emphasizes digital government, innovation, and the development of a knowledge-based economy. By operating advanced AI systems locally and applying them to education, research, and administration, the university contributes to the Kingdom’s goals of improving public sector efficiency and building national technological capability. The involvement of the Digital Government Authority underscores that these efforts are part of a coordinated national direction rather than isolated institutional projects.
Looking ahead, the university’s experience offers a foundation that can be extended to more services, more departments, and potentially more institutions. The combination of local infrastructure, internal governance tools, and demonstrated use cases such as UniTwin, automated translation, and AI-assisted project management positions Najran University as a contributor to Saudi Arabia’s broader AI ecosystem. As the Kingdom continues its digital transformation, examples like this show how public institutions can turn national ambition into working systems that serve students, researchers, and the wider community. Isn’t that the kind of practical progress Vision 2030 was designed to deliver?
20 Questions
Q1. What did Najran University showcase at the LLMs workshop?
A1. Najran University showcased its experience in developing and operating an advanced AI system at its data center, presenting the work during a workshop on open-source large language models organized by the Digital Government Authority and attended by several government entities.
Q2. Who presented Najran University’s experience at the workshop?
A2. Eng. Adel Alkhalifi, Director of Data Management and Emerging Technologies at Najran University, presented the university’s experience operating open-source AI models locally within its own data center infrastructure.
Q3. What is the main advantage of operating AI models locally?
A3. Operating AI models locally keeps data within the university’s own domain, allows the addition of new use cases, and reduces reliance on external cloud service providers, giving the institution greater control over security, cost, and compliance.
Q4. Which Saudi authority organized the LLMs workshop?
A4. The workshop on open-source large language models was organized by the Digital Government Authority, the Saudi agency responsible for advancing digital government services, and was attended by several government entities from across the Kingdom.
Q5. What tools support the university’s AI system?
A5. The system includes tools developed by the Deanship of Digital Transformation and Knowledge Resources to manage AI models, monitor servers, balance workloads, manage access permissions, and track usage across the institution.
Q6. What is UniTwin and how many digital twins are active?
A6. UniTwin is a digital twin platform for students developed by Najran University. It has had 5,943 digital twins in operation since November 2025, demonstrating a working service rather than a concept.
Q7. What other projects were built on the AI system?
A7. Projects include an AI-powered data classification project, the UniTwin digital twin platform, an AI platform for project management, and a smart environment supporting scientific research at the university.
Q8. How has AI improved the university’s electronic portal?
A8. AI has automated content translation and image description on the portal. A total of 22,025 out of 24,131 pieces of content have been translated, and 40,281 out of 44,074 images have been described, both at 91% completion.
Q9. Why is data sovereignty important in this context?
A9. Data sovereignty ensures institutional information remains under local control, simplifying compliance with national data policies and reducing risks associated with cross-border data transfers to external cloud providers.
Q10. How does the AI system handle access and usage tracking?
A10. The system includes dedicated tools to manage access permissions and track usage, ensuring that AI capabilities are governed, auditable, and available only to authorized users and departments within the university.
Q11. What role does the Deanship of Digital Transformation play?
A11. The Deanship of Digital Transformation and Knowledge Resources developed the tools that manage AI models, monitor servers, balance workloads, manage permissions, and track usage, forming the operational layer of the university’s AI platform.
Q12. How does this work support scientific research?
A12. The university operates a smart environment supporting scientific research, built on its AI system. This environment helps researchers access tools and services that accelerate their work within a governed institutional platform.
Q13. What does the 91% completion rate indicate?
A13. The 91% completion rate for both translation and image description indicates sustained operational deployment at scale, with repeatable processes that can be extended to remaining content and future additions on the portal.
Q14. How does local AI operation reduce costs?
A14. By avoiding reliance on external cloud service providers, the university can manage long-term costs, reduce recurring usage fees tied to foreign platforms, and redirect savings toward research, teaching, and student services.
Q15. What is the significance of the Digital Government Authority workshop?
A15. The workshop brought government entities together to examine open-source AI deployment, positioning Najran University’s experience as a practical case study for other Saudi institutions preparing to adopt similar sovereign AI architectures.
Q16. How does the AI platform support project management?
A16. The university built an AI platform for project management on its system, helping teams plan, track, and coordinate work more efficiently while benefiting from the governance and monitoring tools that support all AI services.
Q17. What does the data classification project do?
A17. The AI-powered data classification project helps organize and categorize institutional information, improving how the university manages its data assets and supporting better decision-making across departments.
Q18. How does this initiative align with Vision 2030?
A18. The initiative supports Vision 2030 goals for digital government, innovation, and a knowledge-based economy by building local AI capability, improving public sector efficiency, and developing national technological talent.
Q19. What can other Saudi institutions learn from Najran University?
A19. Other institutions can learn that open-source AI models can be operated effectively on-premises with the right management tools, governance practices, and a focus on high-volume use cases that deliver measurable value.
Q20. What comes next for the university’s AI system?
A20. The university is expected to extend the platform to more services and departments, building on demonstrated use cases such as UniTwin, automated translation, and AI-assisted project management to serve students and researchers.
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