Artificial Intelligence (AI) has moved beyond research laboratories and technology conferences. It has become a general-purpose technology comparable to electricity and the internet. It is transforming industries as diverse as finance, manufacturing, healthcare, agriculture, education and defence.
Countries that develop genuine AI capability will enjoy a lasting competitive advantage. Those that remain consumers of foreign AI will increasingly depend on others for technologies that shape their own economies.
That dependence comes at a price. It means recurring foreign exchange outflows to overseas AI providers, reliance on foreign cloud infrastructure that may become vulnerable to geopolitical tensions, arbitrary fee increases, weaker data sovereignty and fewer opportunities for domestic innovation. Countries that fail to develop indigenous expertise risk surrendering an important source of future productivity.
The global race for AI talent is still in its early stages. Demand far exceeds supply, giving late entrants an opportunity to catch up if they act intelligently. Pakistan possesses one important advantage. Every year, thousands of mathematically capable, English-speaking students enter universities. Properly trained, they could become a powerful engine of economic growth. The next three to five years will determine whether Pakistan becomes a producer of AI technologies or remains a customer buying them from others.
Developing a viable AI ecosystem does, however, present a set of challenges that need to be tackled and overcome through rational, data-driven policies.
The first challenge is straightforward. Pakistan does not have enough qualified AI educators. Universities graduate large numbers of computer scientists, but relatively few academics possess current expertise in machine learning, large language models, or modern AI engineering. Many of the country’s most talented specialists work abroad or in industry because universities cannot compete with private sector salaries. No education policy can succeed if it ignores the shortage of those expected to teach it.
The second challenge is pedagogical. The traditional lecture remains largely unchanged from a century ago. Professors speak while students take notes and later attempt difficult assignments in isolation. That model has produced generations of competent graduates, but AI requires a different way of learning. Success depends less on memorising concepts than on writing code, analysing data, debugging models and solving unfamiliar problems.
Evidence increasingly favours a different approach. Rather than treating classroom time as an opportunity to deliver information, universities should use it to develop competence.
Under the flipped classroom model, students study lectures and introductory material before class. Classroom time is then devoted to coding, discussion, practical exercises and individual feedback.
A meta-analysis by David van Alten and colleagues published in ‘Educational Research Review’ examined 114 studies and found that flipped classrooms consistently improved learning when face-to-face sessions focused on active problem solving rather than passive lectures. Similar conclusions emerged from Scott Freeman and colleagues’ influential study of Science, Technology, Engineering, and Mathematics (STEM) education, which showed that active learning substantially outperformed traditional lecturing.
These findings matter because AI is fundamentally a practical discipline. Students do not become better machine learning engineers by listening to longer lectures. They improve by building models, discovering why those models fail and refining them under the guidance of experienced instructors. Universities should therefore use AI itself to strengthen AI education.
Intelligent tutoring systems can answer routine questions, generate additional programming exercises, explain unfamiliar concepts and identify misconceptions before students enter the classroom. Professors can then devote their time to higher-value activities that machines cannot easily replace: mentoring, challenging assumptions, supervising projects and developing judgement.
The third challenge concerns institutional design. Not every university can become a centre of excellence in AI. Diluting scarce expertise across dozens of institutions is a recipe for mediocrity. Pakistan should instead establish a small number of National AI Institutes selected through rigorous international peer review rather than on political grounds. Their mandate should resemble that of India’s original IITs, South Korea’s KAIST, or Singapore’s A*STAR: institutions created to compete internationally rather than merely satisfy domestic administrative requirements.
Concentrating resources does not mean creating educational monopolies. Every selected university should continue teaching the foundations of AI, including mathematics, statistics, programming, introductory machine learning, neural networks and AI ethics. Those subjects are the equivalent of emergency departments in hospitals. Every institution needs them.
Frontier specialisations such as multimodal AI, advanced computer vision, reinforcement learning and Machine Learning Operations (MLOps), however, require deeper expertise and expensive research infrastructure. Concentrating these capabilities within a limited number of internationally competitive institutes would create critical mass while allowing specialised courses to be shared nationally through digital platforms.
These institutes must also rethink whom they employ. Academic researchers and industry practitioners contribute different strengths. Universities therefore need greater flexibility to recruit experienced AI engineers, data scientists and technology entrepreneurs as practitioner faculty with salaries that reflect market realities. Traditional academics would continue leading curricula and research while practitioners would bring current industrial experience into the classroom. That combination is essential in a field evolving as rapidly as AI.
The final challenge is governance. Creating excellent institutions requires more than generous funding. It requires protecting them from bureaucratic inertia. Around the world, the most successful research organisations have enjoyed considerable operational autonomy while remaining publicly accountable. Pakistan should adopt the same principle.
Pakistan needs a ‘National AI Education Authority’ established through an Act of Parliament and insulated from day-to-day political interference. Its governing board should be dominated by AI researchers, technology entrepreneurs, industry leaders, and members of the Pakistani technology diaspora rather than career civil servants.
The Higher Education Commission (HEC) and the Ministry of Information Technology and Telecommunication (MOITT) should certainly have seats at the table. The HEC understands the minutiae of higher education in Pakistan such as accreditation, quality assurance, and university governance. The MOITT brings valuable knowledge of Pakistan’s digital economy and its links with software firms, telecommunications companies, and data centres. Their expertise is indispensable. Their dominance in decision-making would, however, be detrimental to the Authority’s mission and should therefore not be countenanced.
The reason is simple. Frontier AI evolves in months, not years. Curricula, research priorities, and international partnerships must evolve at the same pace. Universities cannot wait for prolonged bureaucratic approval before revising courses, recruiting specialists or launching collaborations. An AI ecosystem requires institutions that adapt as quickly as the technology itself.
Building international partnerships should therefore become one of the Authority’s principal responsibilities. Long-term collaborations with leading universities would accelerate faculty development, curriculum design, joint research, and postgraduate training. Pakistan’s technology professionals working in Silicon Valley, London and other innovation hubs represent another underused national asset. They should become mentors, visiting faculty, research collaborators and board members, creating pathways through which Pakistani graduates can compete internationally.
Technology choices should reflect the same pragmatism. No single AI platform is optimal for every educational purpose. Frontier research, advanced reasoning and sophisticated coding exercises still benefit from proprietary frontier models such as those developed by Anthropic and OpenAI. Their capabilities justify their higher cost in postgraduate education and advanced research.
Undergraduate teaching presents a different challenge. Open-weight models from China such as Qwen and DeepSeek have dramatically lowered the cost of deploying AI tech stacks. Because they can run on local infrastructure, they reduce recurring foreign exchange costs while supporting large-scale instruction and multilingual applications.
Pakistan should adopt a pragmatic dual-platform strategy, selecting technologies according to educational value, cost, security and performance rather than geopolitical preference.
AI is rapidly becoming part of a country’s economic and security infrastructure. Through the development of the AI ecosystem, Pakistan can also counter the expected negative impact of AI on jobs by providing employment opportunities to those willing and able to harness the new technologies.
