The OECD’s initial prediction that around 1.1 billion jobs will undergo significant transformation due to artificial intelligence (AI) over the next decade now seems quite cautious in light of the rapid advancements in technology. Advanced AI systems, such as GPT-4 and Midjourney, have broadened the horizons of what AI can achieve. They can generate diverse content, understand complex scientific concepts and have the potential to reshape industries, such as medicine and entertainment.

It’s clear that the scope of AI’s capabilities has surpassed earlier expectations, prompting a re-evaluation of skills and professions. This changing landscape emphasises the importance of adapting higher education to equip individuals for this AI-driven world.

In this, the concept of ‘humanics’ put forward by the World Economic Forum’s Education 4.0 initiative is critical – fostering skills that are resistant to automation, such as creativity, interpersonal awareness and civic responsibility, will be invaluable in the changing job landscape.  

Yet, underscoring the ethical dimensions of AI’s growth is an open letter from the Future of Life Institute that calls for restraint in training AI systems beyond GPT-4’s capabilities. Endorsements of this letter from influential figures, such as Elon Musk and Steve Wozniak, demonstrate the concern for responsible AI development.

AI’s expansion

In spite of such challenges, AI’s power is expected to expand, reshaping industries and potentially redefining the nature of work itself. Generative AI’s transformative potential spans diverse fields and will make its presence keenly felt in education. Content creation is a prime example, as it offers industries that include media and marketing a tool to generate written materials, leveraging models such as GPT-3 to produce news articles and other forms of written content. Generative AI also has the power to aid researchers in crafting academic research papers and literature reviews, streamlining information synthesis and laying the groundwork for further refinement.

In higher education, generative AI is already reshaping simulations and virtual laboratories. Virtual patient simulators in healthcare, for example, simulate clinical scenarios and enable medical students to refine their skills in a risk-free environment. In engineering, digital prototypes and simulations replicate real-world situations, facilitating efficient product testing and development. Intelligent tutoring  technologies are also evolving, with generative AI helping platforms, such as Carnegie Learning, to deliver personalised guidance based on individual student performance, thus enriching the learning journey.

Why higher education must adapt

As AI continues to disrupt industries and redefine job roles, it’s essential for universities to step up and adapt their approach to education, ensuring that students are equipped not just with technical skills, but also with the cognitive and social skills that can’t be easily automated. Blending theoretical knowledge with practical experience through experiential learning programmes is one proactive strategy that can help business schools prepare students for an AI-driven future. Such programmes offer students the chance to establish meaningful connections with their environment and peers, giving them a competitive edge over automation. By immersing students in AI-driven workplaces, they can gain a deep understanding of industry fundamentals, even if AI begins to narrow traditional entry paths.

Experiential learning serves as a valuable alternative route, catering to a changing landscape. The impact of this shift extends far beyond the classroom. It reshapes apprenticeships, corporate training and educational technologies, fundamentally altering the existing educational system. It calls for universities to pivot their focus towards lifelong learning, recognising that the dynamic nature of AI and technology requires continuous adaptation.

In addition, this transformation aligns with the evolving needs of non-traditional learners by bringing the focus on to tailored programmes that cater to their evolving professional requirements. The synergy between experiential learning and generative AI showcases the dynamic potential of education in preparing individuals for a rapidly changing landscape of work and technology.

How AI can improve higher education

Generative AI’s influence in education spans a wide range of areas. Platforms such as Duolingo use generative AI to create language lessons and offer immediate feedback. Writing assistance systems, such as Grammarly, use generative AI to recommend ways to enhance your grammar, style and ability to spot plagiarism. Existing applications of the technology also extend to data analysis and visualisation, where tools like Tableau use generative AI to suggest efficient ways to present complex datasets in order to facilitate interpretation.  

These examples highlight the varied functions that generative AI may serve to improve education. It has the potential to transform higher education in a number of ways. One significant application is content creation, which enables generative AI models to create excellent instructional materials. This might entail writing well-organised research papers, perceptive essays and thorough textbooks and would allow educators and researchers to focus on other important facets of their job while also saving them significant time.

Generative AI also presents opportunities for individualised learning. The algorithms behind the technology can produce custom content that caters to the distinct requirements and preferences of each student by examining individual learning patterns. This could entail creating tests tailored to each student’s learning preferences, interactive activities, or even life-like simulations. Additionally, generative AI models acting as virtual teaching assistants accompany students in real-time by providing prompt responses, precise justifications and customised coaching. Bring with it a guarantee of prompt support and clarification, this type of personalised feedback stands to improve the learning process considerably. 

The significance of generative AI is further expanded by its potential applications in language learning and translation. To help learners master new languages, it can produce language drills, conversations and pronunciation manuals. Additionally, it can make translation work easier while encouraging a greater grasp of other languages and efficient interlinguistic communication. Similarly, it can speed up research and data analysis across a variety of academic domains, by developing hypotheses, navigating enormous datasets, and modelling difficult scenarios. In the arts, generative AI offers ways to ignite innovation and stimulate the birth of new artistic movements, musical compositions and architectural ideas. In this light, it becomes clear that generative AI has the potential to revolutionise education by precipitating and facilitating a re-evaluation of how humans discover, create and learn in a variety of contexts.