AI, Inequality, and the Future of Higher Education
GenAI is reshaping higher education, bringing both far-reaching opportunities and emerging challenges. While these tools can expand access to knowledge, they may also intensify existing inequalities in education and employment. How the education sector responds will play a crucial role in determining the longer-term impact of AI.
These concerns are core to the work of Professor Jisun Jung in the Faculty’s Academic Unit of Social Contexts and Policies of Education. Her research examines how higher education policy and practice intersect with inequality, particularly in relation to learning, access, graduate outcomes and institutional responses to technological change.
Revolution or Reform?
For Professor Jung, the rise of AI needs to be understood as part of a historical trajectory of invention and innovation. From the Industrial Revolution in 18th-century Britain to the automation of manufacturing during the United States’ 20th-century economic boom, new technologies have repeatedly transformed labour in pursuit of productivity gains. What makes AI distinctive, however, is the speed, scale, and scope of its impact.
Earlier waves of technological change mainly affected blue-collar jobs, reducing demand for heavy physical and industrial labour. Today, with the rise of AI, technologies are increasingly reaching white-collar and middle-class professions, including fields once considered to be relatively protected from automation.
A university degree was once a “safety net” for professionals in areas such as medicine, law, healthcare, science, literature, and journalism. In the age of AI, that protection seems to be weakening. As advanced knowledge becomes more accessible outside formal university admission, differences in resources, technological access and skills are becoming more consequential.
Division and Inequality
Underlying these observations is the concept of the “digital divide,” which Professor Jung describes as the widening of social and economic inequalities driven by unequal access to technological systems. This divide operates at all levels of education, and its effects accumulate over time: well-resourced institutions and more privileged students are better positioned to adapt to the AI era, while those lacking resources risk falling further behind.
The problem is not only unequal access to AI tools. AI systems – specifically large language models (LLMs) – are also shaped by the data and social contexts in which they are developed. “AI is trained to reflect and reproduce existing inequalities and norms in knowledge production,” Professor Jung notes. “As a result, people are repeatedly exposed to similar perspectives, which can further entrench bias.”
There is already evidence that the use of LLMs can reinforce structural inequalities related to gender, religion, ethnicity, and culture. Over time, these patterns may become more pronounced, widening both knowledge gaps and disparities in perspective among students and the wider population.
Outcomes for University Graduates
This trend is highly relevant to one of Professor Jung’s key research streams: how university graduate employment is shaped by socio-economic conditions. Currently, many graduates are facing different forms of mismatch between their university experience and the demands of the employment landscape. These include gaps in skills, qualifications, geography and identity.
What is driving this mismatch? A key factor, according to Professor Jung, is technological change, specifically automation, combined with the unequal capacity to adapt. The labour market is becoming increasingly polarised, with growth concentrated at the top and bottom, while the middle-level roles are shrinking.
Graduates from more advantaged backgrounds – including those from privileged families, prestigious institutions, or with diverse educational and professional pathways – are more likely to benefit from this shift. They tend to possess higher technological literacy and more advanced skills, making them more competitive in the workforce.
“On the other hand,” says Professor Jung, “students from less advantaged backgrounds have fewer opportunities and face greater challenges in adapting to the evolving employment environment, where advanced skills and knowledge are increasingly required. They often lack the resources and training needed to adapt to these changes.”
The Way Forward
For Professor Jung, the challenge is not whether we should reject emerging technologies, but how we should engage with them responsibly. The possibility of misuse does not necessarily mean that universities need to prohibit AI use. However, institutions should be cautious about rushing to introduce AI-related courses without first considering their pedagogical purposes.
“Instead, the education sector, and society as a whole, needs to make more deliberate choices about regulation, governmental responsibility, and the role of educational curricula in the age of AI,” Professor Jung suggests. These decisions will shape how the benefits and risks of AI are distributed across different groups.
At the macro level, Professor Jung commented that government policy on AI should enforce responsible conduct, social inclusivity, and environmental sustainability. Within higher education, universities play a key role in preparing graduates for a future increasingly shaped by AI. This requires more than adding new courses or technical training; it calls for a fundamental shift in pedagogy.
Professor Jung is sceptical of purely lecture-based approaches to teaching ethical AI use. Providing information alone, she points out, is insufficient to convince today’s students. For this reason, she advocates a student-centred, experiential model of learning.
“Students need to experience things for themselves: they should use AI to explore examples, identify problems, and develop solutions, then apply these insights to their own lives,” she says. “They should also be given more opportunities to gain experience beyond the university, in society, and within their communities.”
As AI continues to reshape the very concept of a profession, roles once defined by specialised knowledge and restricted access are changing. Professional identity is increasingly linked to creativity, flexibility and the ability to integrate expertise across disciplines and excel in hybrid roles.
Against this backdrop, Professor Jung emphasises that universities should cultivate independent and creative thinking in their students. This includes educating them to use AI constructively – as a collaborator that enhances thought and output, rather than as a substitute for intellectual engagement. She also highlights the importance of clear expectations and appropriate responses in cases of misuse, alongside recognition of AI’s potential to foster high-quality learning when used in disciplined and reflective ways.
Professor Jisun Jung
Associate Professor
Academic Unit of Social Contexts and Policies of Education
