As the UK approaches its next general election, both the Labour and Conservative Party manifestos outline various educational reforms. However, neither adequately addresses the critical need for a robust strategy to integrate AI education into our schools and universities. Schools are important in this debate as they impact the pipeline into higher education and, if not dealt with now, will pass the burden onto the higher education system.
The rapid advancements in AI make it nearly impossible to train teachers in both systems effectively, even if we were to have plenty of teachers who are eager to upskill (an issue best explored another time) and an unlimited training budget. This issue, which I term the “AI divide,” threatens to widen the gap between those who can learn AI through alternative means and those dependent on our formal education system.
The AI Divide: An Urgent Educational Challenge
As AI continues to transform industries and daily life, the ability to understand and leverage these technologies will be crucial for future success. Without significant reforms, students in schools and universities alike, reliant on our current education system, will lag far behind their peers who have access to more dynamic learning environments, in the UK and other countries. They will then graduate and enter the workforce with the same growing gap, not only falling behind in securing good jobs but directly impacting how our whole economy navigates this transitional and transformational time of realigning and rebuilding itself for the AI-powered future.
Both the Labour and Conservative Party manifestos acknowledge the importance of digital skills, yet they fall short in proposing actionable and practical solutions to the AI divide. This divide separates those who can independently acquire AI knowledge from those relying on traditional educational frameworks.
Labour’s manifesto promises significant investment in education, including a focus on digital literacy. However, it lacks specificity regarding how to keep AI education current and relevant. Similarly, the Conservative manifesto emphasises skills and lifelong learning but does not address the need for continuous curriculum updates or the challenge of “who trains the trainers”.
The Need for a “new” Industry-Academia Collaboration
To effectively tackle the AI divide, we must create systematic incentives for technology professionals to participate in teaching part-time at schools and universities. The idea of University-Industry collaboration is not new, but in reality, it has only been implemented (barely) in Business and Management schools of universities.
The systematic and incentivised integration can build on that model, bridging the knowledge gap and ensuring students and teachers receive up-to-date and practical insights into AI. Professionals actively engaged in the industry are best positioned to provide the latest developments, trends, and applications, enriching the educational experience with real-world relevance.
Incentivise Industry Participation
Implementing policies that incentivise the professionals to dedicate some of their time to teaching and the companies to allow their employees to do so, can hugely contribute to solving the problem. This can be achieved by using tax incentives combined with behavioural science nudges. By redirecting the tax that these professionals and their companies would have paid, the education system can remunerate them for their teaching contributions. This approach aligns incentives without expanding bureaucratic government involvement and unrealistic plans for hiring more teachers (especially AI-literate or experts) when there is neither a clear pipeline nor a realistic hiring or training budget.
Of course, there will be a need to develop certification programs that enable industry professionals to qualify as part-time educators without extensive retraining with an emphasis on pedagogical skills and effective knowledge transfer, but this is far more achievable than training existing teachers to become AI literate and then educate the students.
Professionals won’t necessarily make good teachers
It is true that expertise alone does not guarantee effective teaching. However, the proposal is not about replacing teachers with industry professionals but rather augmenting the educational experience with their specialised knowledge. Some professionals will indeed excel at teaching, and by attracting these individuals through targeted tax incentives and behavioural science nudges, we can enhance the resources available to the education system.
These professionals will undergo the same rigorous professional reviews and continuous training as any other teachers, ensuring they develop their pedagogical skills over time. The key is to integrate these experts into the existing educational framework, where their primary role will be to ensure that curricula remain current and infused with real-world applications of AI. This approach supplements the efforts of traditional teaching staff rather than replacing them, enriching the educational experience with up-to-date knowledge and practical examples from the field. By drawing on the strengths of both educators and industry professionals, we can create a more dynamic and effective learning environment that prepares students for the AI-driven future.
Conclusion
Addressing the AI divide requires a fundamental rethinking of our educational strategies. By integrating industry expertise into the educational system and leveraging tax incentives to facilitate this, we can equip teachers and students with the necessary knowledge and skills to thrive in the AI-powered world. Both the Labour and Conservative parties, and whoever forms the next UK government, must recognise the urgency of this issue and commit to policies that facilitate meaningful collaboration between industry and academia.
Policymakers, educational leaders, and the business community must work together to bridge the gap between technological advancement and educational practice. Only through such innovative approaches can we prepare the next generation for the challenges and opportunities of the AI-driven future. This paradigm shift is not an option but an imperative for maintaining competitiveness and fostering inclusive growth in the era of artificial intelligence.
