SOUTH KOREA
The artificial intelligence revolution is no longer hypothetical; it is already reshaping software development. As tools such as OpenAI’s ChatGPT, Anthropic’s Claude and other generative AI systems produce functional code from simple prompts, long-standing assumptions about computer science education are shifting. Degrees once seen as secure pathways to stable, high-paying jobs now face uncertainty, as AI encroaches on tasks traditionally assigned to entry-level roles.
The impact is no longer distant but immediate, reaching higher education. So how is this mega-trend reshaping transnational and transglobal higher education models?
The generative AI shock
By late 2025, the tech job market had entered a structural slowdown. Demand for software developers fell sharply, with job postings dropping dramatically across major markets. While part of this reflects a post-boom correction, generative AI has emerged as a key constraint on recovery by automating many entry-level programming tasks and reducing the need for junior hires.
Major technology firms have increasingly cited AI-driven efficiency as a rationale for restructuring. Big tech companies have all announced new rounds of lay-offs or hiring freezes over the past two years, explicitly linking workforce reductions to AI-enabled productivity gains. Employers, meanwhile, are relying more heavily on AI development tools to offset new hiring altogether.
These shifts are already reshaping student behaviour. In South Korea, for example, applications to computer science programmes declined significantly for the 2025 academic year, reversing a period when the field rivalled medicine in prestige. This drop closely tracks AI-related hiring anxieties, as many IT firms have paused recruitment and leading companies pivot toward AI-driven workflows.
Nonetheless, the market is not collapsing so much as reconfiguring. Demand is weakening for conventional coders, but rising for professionals who can design, manage and strategically deploy AI. This transition raises a central question for higher education: whether universities can prepare graduates for AI-augmented roles or will continue training them for a labour market that has already moved on.
Let’s look at two different examples – SUNY Korea and Minerva University – of how this is impacting universities.
SUNY Korea: Local realities
SUNY Korea was founded with an ambitious transnational vision. Established in 2012 as the first American university campus in South Korea’s Incheon Free Economic Zone, it aimed to deliver US-quality education locally while serving global talent needs.
As an extension of Stony Brook University, SUNY Korea offers identical curricula and degrees, with computer science as one of its flagship and most selective majors. The value proposition is clear: an English-taught American computer science degree in Korea, combined with global faculty networks and mobility opportunities to the New York campus and a bigger job market in the US.
The rapid advance of AI, however, is testing that promise. Much of SUNY Korea’s computer science curriculum remains rooted in the academic foundations inherited from Stony Brook.
While these fundamentals remain essential, industry change is accelerating faster than traditional curricular cycles. The institution has begun adjusting its programme portfolio, including the launch of a masters programme in data science, reflecting growing demand for AI-adjacent skills.
However, structural and policy constraints complicate adaptation. As a foreign branch campus operating under Korean regulations, any substantive change to academic programmes requires approval from the Ministry of Education. This governance structure significantly lengthens the timeline for curricular reform, especially compared to independent or digitally native institutions.
In an AI-driven environment where industry practices evolve rapidly, such regulatory lag limits institutional agility and intensifies the challenge of keeping programmes aligned with market realities. In an era of AI-driven transformation, incremental adjustments and slow approval cycles may prove insufficient.
Minerva: Global experiment faces the AI age
If SUNY Korea represents globalisation through institutional transplantation, Minerva University embodies a digitised transglobal model. Minerva sends its students to live and study across multiple countries during their degree while taking all courses online.
Within this model, the computational sciences college has long been the most sought-after discipline, particularly among high-achieving and risk-taking students from the Global South.
For many, computer science has functioned as a strategic pathway into the US technology labour market, offering upward mobility, geographic access and alignment with Minerva’s stated commitment to geographic and socio-economic diversity. Increasingly, however, that pathway is being destabilised by shifts in the global tech economy.
Minerva’s response to AI-driven disruption diverges from conventional technical training. Rather than prioritising narrow coding proficiency, its curriculum emphasises cognitive frameworks, systems thinking, adaptability and problem-solving, positioning human judgment as a durable advantage in an AI-saturated world. The underlying logic is explicit: technical skills are rapidly automated, but the ability to reason, communicate and innovate across contexts remains scarce.
Yet Minerva is not insulated from structural constraints. Its youth and small alumni base limits network effects and the absence of a fixed campus complicates sustained employer pipelines, which are increasingly critical as entry-level tech hiring contracts.
More importantly, the historic appeal of Minerva’s computer science pathway, especially for Global South students seeking US-based tech employment, may be eroding. As AI reduces demand for junior roles and immigration-linked career trajectories become more uncertain, computational expertise alone may no longer guarantee labour market access.
In this context, adaptability and global exposure, while necessary, may prove insufficient without deep, current technical fluency and strong employer integration.
Uneven readiness
Stepping back, these two cases illuminate higher education’s uneven readiness for an AI-disrupted labour market. Both SUNY Korea and Minerva University reveal a tension between innovative institutional design and the accelerating pace of technological change. Despite their relative youth and ambition, both are now under pressure to recalibrate more quickly than their original models anticipated.
For SUNY Korea and similar transnational campuses, the challenge lies in coupling global academic standards with local technological relevance. Curricula cannot remain static exports from the US.
For many Global South students at Minerva, its computer science track has served as a gateway to US tech and entrepreneurial careers. As those pipelines narrow, Minerva’s relevance will hinge on reframing computer science not as access to a single market, but as preparation for diverse, AI-driven pathways of innovation.
The stakes are high. If universities misjudge this transition, they risk producing a generation of highly capable graduates whose expectations collide with a transformed labour market. Hence, they must respond with clarity and candour, demonstrating that technology careers remain viable, but no longer linear or guaranteed. Those that align honest messaging with substantive curricular reform may yet turn disruption into reinvention.
Dr Kyuseok Kim is a centre director of IES Abroad Seoul.
This article is a commentary. Commentary articles are the opinion of the author and do not necessarily reflect the views of University World News.
