By: Prof. May El Barachi, Dean & Full Professor, University of Wollongong in Dubai

Artificial intelligence is no longer a future technology. It has become part of our everyday lives almost overnight. Whether we are writing emails, analysing data, generating code, creating presentations, or conducting research, AI has fundamentally changed how knowledge is created and consumed.

For higher education, this represents one of the biggest disruptions since the arrival of the internet.

Much of today’s conversation revolves around a simple question: Will AI replace educators?

I believe we are asking the wrong question.

The real question is whether universities can reinvent themselves quickly enough to prepare graduates for an AI-first world.

Having worked extensively with generative AI technologies, I see AI not as a replacement for education, but as an extraordinary opportunity to redefine it. From One-Size-Fits-All Learning to Personalized Education.

Traditional education has largely been built around standardized delivery: one lecturer, one classroom, one pace, and one curriculum for every student.

AI changes that equation.

For the first time, every learner can potentially have access to an intelligent learning companion available 24 hours a day. AI tutors can explain difficult concepts, generate additional practice exercises, adapt explanations to different learning styles, provide immediate feedback, and support students until genuine understanding is achieved.

Instead of asking students to adapt to education, education can finally adapt to students. This has important implications for accessibility, allowing high-quality learning experiences to reach individuals regardless of geography or socioeconomic background.

In many ways, AI has the potential to become the great equalizer in education.

Teaching Students How to Think; Not What to Memorize

At the same time, AI forces universities to rethink their educational philosophy.

When information is instantly accessible, memorization becomes less valuable.

Future graduates will be judged less by what they know, and more by how effectively they can solve problems, evaluate evidence, think critically, collaborate, communicate, and exercise sound judgement. This means assessment methods must evolve as well.

Rather than rewarding students for reproducing information that AI can generate in seconds, universities should increasingly emphasize authentic projects, real-world problem solving, teamwork, creativity, ethical reasoning, and applied learning. Ironically, AI may push higher education to become more human, not less.

Educators Are Becoming AI-Enabled Mentors

There is growing concern that AI will eventually replace lecturers. I see the opposite happening.

The educator’s role is becoming even more important; but it is changing.

Rather than acting primarily as transmitters of knowledge, educators are evolving into mentors, coaches, facilitators, and critical thinking partners who help students interpret information, challenge assumptions, and develop professional judgement.

To do that effectively, universities must invest heavily in AI literacy. Faculty need more than basic familiarity with AI tools. They must understand how these systems work, their limitations, their biases, and how they can be integrated responsibly into teaching, assessment, and research. AI literacy is rapidly becoming as fundamental as digital literacy was twenty years ago.

Preparing Graduates for an AI-First Workforce

Perhaps the biggest transformation is happening outside the classroom. Virtually every profession; from healthcare and finance to engineering, education, law, and government; is being reshaped by AI.

Graduates entering the workforce will collaborate with intelligent systems every day. This requires a new combination of technical and human capabilities. Understanding AI, data, automation, and digital technologies will become essential across disciplines. Equally important will be creativity, emotional intelligence, leadership, adaptability, ethical decision-making, and lifelong learning. The most successful professionals will not compete against AI. They will learn how to work alongside it.

Looking Ahead

The future university may look very different from today’s institution. Degrees are likely to become more modular and flexible, complemented by stackable micro-credentials that allow professionals to continuously update their skills throughout their careers.

Immersive technologies such as virtual and augmented reality will create richer learning experiences, while learning analytics will enable institutions to identify struggling students earlier and provide personalized support. Education will become increasingly global, connected, and lifelong.

The Human Advantage

Despite all these technological advances, one thing remains unchanged. Education has never been solely about transferring knowledge. It is about inspiring curiosity, building confidence, developing character, nurturing empathy, and preparing individuals to make meaningful contributions to society.

No algorithm can replace the inspiration of a great teacher or the mentorship that shapes a student’s future.

AI should not diminish the human element of education. It should amplify it.

The universities that thrive over the next decade will not be those that simply adopt AI tools. They will be those that successfully combine technological innovation with the uniquely human qualities that no machine can replicate. Because ultimately, the future of higher education is not about artificial intelligence. It is about human intelligence; enhanced by AI, guided by educators, and applied to solve the world’s most complex challenges.

By Amjad Alqaqaa – Vice President – MEAI

Power sectors around the world are undergoing rapid transformation. Digital technologies, advanced materials, and the shift towards lower-carbon energy are reshaping how power plants and critical infrastructure are designed, operated, and maintained. Yet one persistent challenge continues to hold the sector back: a shortage of people with the right engineering and technical skills.

As the UAE continues to advance its ambitions as a leading hub for innovation and technology, there is an increasing need to strengthen and future-proof STEM capabilities to keep pace with evolving industry demands. According to a report by STEM workforce consultancy SThree, 40% of STEM professionals in the UAE believe that upskilling and reskilling are the most effective ways to boost productivity and competitiveness. While more than a third (32%) point to skills shortages as a barrier to productivity, highlighting a clear gap between workforce capabilities and industry needs.

Additionally, data from the Hays 2026 US Salary & Hiring Trends Guide indicates that companies in the UAE are starting to slow down recruitment and instead are investing in the skills of their existing workforce, with around 42% of employers prioritising upskilling over hiring.

Research from LinkedIn also suggests demand for green skills is rising much faster than supply, highlighting a widening gap between the skills needed for the energy transition and the talent currently available in the workforce.

For power generation companies, this is more than a recruitment issue. Skills shortages can impact equipment reliability, delay maintenance programmes, and slow the deployment of new technologies. In a sector where uptime, safety, and efficiency are critical, having the right expertise in place is essential.

At the same time, interest in STEM subjects among young people has fallen in recent years.  This weakens the future talent pipeline. This means companies must do more to attract and develop STEM talent.

Showing young people what engineering looks like today

One of the challenges is perception. Many young people still associate engineering with traditional industrial roles, rather than the highly advanced, technology-driven careers available today.

Today’s engineers work with advanced digital tools, automation systems, and real-time monitoring technologies. In the power sector, they help keep turbines, pumps, and other critical systems running efficiently. They also work on challenges linked to sustainability, energy efficiency, and emissions reduction.

To address this gap, employers must play a more active role in educating emerging talent about the career opportunities in the sector. That means working more closely with schools, colleges, and universities to showcase the wide range of careers available across engineering and energy.

Partnerships between industry and academia play an important role here. For example, John Crane works closely with the University of Sheffield to support research and PhD programmes in areas such as materials science and engineering. Collaborations like this help connect academic research with real industrial challenges and encourage more students to consider careers in engineering.

These partnerships also help ensure that new research translates into practical solutions that can support industries such as power generation.

Why apprenticeships matter

Alongside academic pathways, apprenticeships are another key way to attract new talent into engineering.

They offer a practical, accessible route into engineering, allowing individuals to gain hands-on experience while working towards recognised qualifications. For employers, apprenticeships provide an opportunity to develop skills aligned to real operational needs, from maintenance and reliability engineering to digital and software capabilities.

But apprenticeships are not only for new recruits. They can also help people who are already in work develop new skills. Programmes linked to areas such as leadership, project management, and digital technologies allow employees to adapt as roles change and technology evolves.

This matters because the skills challenge is not only about bringing new people into the sector. It is also about helping the existing workforce build the capabilities needed for the future.

Building the right skills through training partnerships

Developing a skilled workforce requires more than internal programmes alone. Strong partnerships with external training providers are essential to ensure employees gain the specialist knowledge needed in highly technical environments.

Working with a network of training providers enables organisations to deliver structured learning alongside on-the-job experience. This approach ensures that training remains aligned with real operational challenges, including maintaining equipment reliability, improving efficiency, and meeting evolving safety standards.

Reaching a broader talent pool

Engineering companies need to widen their outreach and look beyond traditional recruitment channels. This includes engaging with students earlier and encouraging people from different backgrounds to consider technical careers.

In addition, requalification programmes are increasingly important in some regions. For example, in the Czech Republic, targeted requalification initiatives are helping individuals transition from other industries into engineering roles, providing a practical route to address skills shortages while bringing valuable experience into the sector.

Ensuring training programmes cater to a wide range of people with varying levels of experience can upskill new and existing workers and build a healthier talent pipeline. Providing that support is an investment that helps create a stronger, more resilient workforce in the long term.

Building the workforce of the future

The power sector plays a central role in driving the global energy transition. In the Middle East, this transition is expected to drive demand for a wide range of engineering roles, particularly in renewable energy, grid infrastructure, and related technologies, highlighting the need for targeted training and workforce development programmes to equip both new entrants and existing workers with relevant technical skills.

Engineers and technicians will be needed to maintain power plants, improve equipment performance, and develop new energy technologies. But these goals will only be possible if the industry has access to the right skills.

To achieve this, companies must think differently about talent. Strengthening collaboration with educators, improving outreach to diverse talent, and offering practical training routes such as apprenticeships all play an important role in addressing the STEM skills gap.

Apprenticeships alone will not solve the skills gap. But when combined with research partnerships and targeted workforce development, they can play a major role in rebuilding the STEM talent pipeline. By investing in people and skills today, the power sector can build the workforce it needs to support a more reliable and sustainable energy system for the future.

by Dr Ekaterina Abramova, Adjunct Assistant Professor of Management Science and Operations at London Business School

The AI revolution is not on the horizon; it is already transforming how we work, solve everyday problems, and interact both with one another and with technology. From generative models to agentic systems capable of disrupting entire industries, artificial intelligence has advanced at a pace that few institutions, businesses, or governments are fully prepared for. What once felt like a distant technological possibility has become a structural force shaping labour markets and economies. As a result, one of the most pressing questions facing societies is no longer whether AI will change the world, but whether it will make it fairer. Increasingly the answer depends not only on the technology itself, but on the choices organisations and governments make about how its benefits are shared.

AI has the potential to unlock unprecedented prosperity. Yet history shows that technological revolutions rarely distribute their rewards evenly. Without deliberate intervention, the benefits of AI risk concentrating in the hands of a small number of large technology firms, highly skilled professionals and capital owners. This pattern has already emerged in earlier waves of digital transformation, where wealth and opportunity accumulated disproportionately in regions best positioned to adapt. For AI to foster equality rather than widen disparity, policymakers must treat inclusion as an ex-ante design principle rather than an ex-post correction.

The first crucial step for achieving fairness is improving the data that AI systems rely upon. Algorithms are only as representative as the information used to train them. When datasets exclude marginalised or underrepresented communities, AI risks reinforcing existing biases. Organisations and governments developing AI algorithms should prioritise collecting data from communities historically overlooked in policy design, such as rural populations, low-income groups, minority communities and those outside the formal labour markets. More inclusive datasets lead to fairer systems, more effective public services and policy decisions that better reflect the realities of entire populations, rather than just their most visible segments.

Another equally important aspect is how governments distribute the productivity gains and wealth generated by AI into broader societal benefits. Different regions are experimenting with alternative approaches. In parts of the Middle East, including the United Arab Emirates, economic gains from technological advancement are often channelled through state-led investment strategies rather than relying solely on traditional taxation and redistribution mechanisms. While VAT and other taxes exist, governments often reinvest a significant share of national income derived from natural resources and state-owned enterprises directly into infrastructure, public services, education and economic diversification. This approach builds long-term national capability by funding human capital development, strengthening digital infrastructure and fostering new sectors that create employment and opportunity.

Such strategies highlight an important principle: AI benefits do not need to be redistributed after inequality has emerged. They can be embedded in development strategies from the outset. By investing in education, digital skills and access to technology, governments expand the number of people able to participate in the AI ecosystem rather than merely compensate those left behind. China, for example, has made substantial investments in AI education and research capacity, recognising human capital as central to technological leadership. Every year 100,000 selected teenagers are funnelled into elite science talent streams across top high schools. These “genius classes” systematically train students to excel in international maths, physics, chemistry, biology and computer science competitions.

The pace of the AI revolution makes this challenge more urgent than previous technological transitions. Earlier industrial transformations unfolded over decades, allowing societies time to adapt institutions and labour markets. AI development in recent years has gained pace. Breakthroughs that once took years are now emerging within months, with new capabilities rapidly spreading across sectors from healthcare diagnostics and financial analysis to logistics and defence industries. This acceleration has been further intensified by the present-day AI race to achieve Artificial General Intelligence (AGI), amid a widespread belief that the first government to reach this milestone will gain a decisive strategic advantage. Organisations at the forefront of AI development are reluctant to slow for fear of falling behind geopolitical or commercial rivals. Meanwhile, many governments are hesitant to introduce AI regulation, concerned that premature constraints could hinder innovation and weaken their competitiveness in the pursuit of AI leadership.

However, the path forward requires a global perspective. While governments should encourage innovation, they must also recognise that AI technology will diffuse across borders. Hence governments worldwide should collaborate towards a global AI governing body, or at the very least, agree on minimum safety and fairness standards for AI deployment. The EU AI Act provides an important foundation by identifying unacceptably high-risk AI applications that should be prohibited. When forming such regulatory frameworks, governments should seek guidance from leading AI scientists to ensure they fully understand where the principal risks originate. Indeed, many prominent experts in the field argue that regulation is failing to keep pace with AI innovation.

Allowing AI technology to evolve without placing guardrails in place early risks embedding structural inequalities, particularly in labour markets, education access and capital distribution. Ultimately, the debate about AI and inequality is not primarily about algorithms; it is about governance. Technology reflects the priorities of the societies that deploy it. If policymakers treat AI purely as an engine of leadership and economic growth, its benefits will likely accrue to those already best positioned to capture them. But if AI development is guided by a clear commitment to inclusion through better data, wider access and sustained investment in human capital, it has the potential to expand opportunity on a global scale. As AI reshapes labour markets, workers will need opportunities to develop capabilities that complement intelligent systems rather than compete directly with them. Access to AI infrastructure, computing resources, data and digital connectivity must not be confined to a small group of corporations or wealthy regions.

The direction of the AI revolution is not predetermined. The question is not whether AI will transform our world, but whether governments and institutions will act quickly and thoughtfully enough to ensure that its benefits are broadly shared. In the race to build increasingly powerful systems, equal attention must be given to building the social and economic frameworks that will ensure the future is genuinely fair.