College students in engineering and pc science are considerably extra prone to combine synthetic intelligence into coursework than are humanities and social sciences college students. However no matter their main, many college students report uncertainty about when and the way AI use is acceptable. These and different findings after we studied how college students at Virginia Tech are utilizing generative AI instruments are instructive. Most report experimenting with AI, however utilization patterns reveal an AI proficiency hole.
This raises a problem for establishments: if AI competence will quickly be foundational to the fashionable office – as basic as figuring out tips on how to use spreadsheets or conduct on-line analysis – can we afford to let proficiency rely on main, teacher or private curiosity? No matter their subject, our college students will encounter AI in trade. It’s already built-in into transportation programs, city planning, environmental administration and public well being. Our graduates have to be prepared.
However what does “AI readiness” imply?
I started reframing that query after attending a world expertise gathering of 148,000 attendees and greater than 4,000 firms in Las Vegas this January. At CES (Shopper Electronics Present) 2026, leaders from Nvidia, AMD and OpenAI described the way forward for AI. I noticed robots enjoying desk tennis and AI programs embedded in the whole lot from mobility platforms to well being units.
One thought stood out.
Three important elements for AI success
A keynote speaker, Roland Busch, president and CEO of Siemens AG, described three important elements for achievement within the AI period: expertise, area know-how and partnerships. That framework has reshaped how I take into consideration AI proficiency – and the way I design my programs.
Know-how is the plain place to begin. College students should perceive what AI programs can – and may’t – do and tips on how to use them. They have to be conversant in GenAI fashions, knowledge pipelines and rising instruments. However technical information alone is inadequate.
Area know-how is equally vital.
I’m a geospatial knowledge scientist. My work focuses on place-based challenges comparable to transportation programs, sensible cities and environmental evaluation. AI fashions could also be highly effective, however they don’t “perceive” the nuances of geography context, advanced land use and transportation patterns or human behaviour throughout area and time. The truth is, my analysis highlights what I name geographic bias: AI programs usually carry out higher in dense, data-rich city areas than in rural areas, the place knowledge is sparse. With out area experience, these gaps can go unnoticed – and uncorrected.
That is why AI proficiency can’t merely imply “studying the software”. College students should develop deep information of their subject and learn the way AI interacts with that information. Laptop scientists can’t totally remedy transportation challenges with out transportation experience. Environmental scientists can’t depend on fashions with out understanding ecological context. Area information shouldn’t be being changed by AI; it’s turning into extra vital.
The third element – partnerships – would be the most transformative.
To sort out urgent international challenges, universities should construct productive partnerships throughout disciplines and past campus, together with collaboration with communities and trade. Traditionally, collaboration throughout disciplines has been tough. Every subject has its personal terminology, assumptions and methodologies. However AI is reducing these communication boundaries. After I encounter unfamiliar technical ideas, I can use AI instruments to assist translate and make clear them. Likewise, pc scientists can use AI to raised perceive domain-specific issues.
This doesn’t substitute human collaboration. It strengthens it.
Significant issues are solved by individuals working collectively – for individuals. AI can facilitate these partnerships, however it’s not an alternative choice to them. In my classroom, I now place better emphasis on collaborative, project-based work that integrates technical expertise with area challenges and interdisciplinary dialogue.
Entry and ethics in AI use
On the identical time, we should fastidiously take into account the moral use of AI, together with entry to AI companies. Entry to AI instruments will be uneven. Many superior programs require paid subscriptions, and prices can shortly accumulate. Whereas $20 (£15) monthly could appear manageable for some, it’s not trivial for all college students. Universities are inspired to increase institutional entry to superior AI infrastructure in order that proficiency doesn’t rely on private monetary capability.
The moral dimension of AI use is equally essential. Empirical analysis – together with work in my very own subject – demonstrates that AI outputs are neither goal nor unbiased. But. Bias can manifest politically, socially and geographically. College students should study not solely tips on how to generate outcomes however tips on how to query them.
Finally, AI literacy shouldn’t be about chasing the newest software; it’s about cultivating the capability to combine expertise with experience and human networks. Universities ought to realise they’re nicely positioned to guide. At Virginia Tech, our institutional motto “Ut Prosim (That I Could Serve)” emphasises fixing real-world issues by means of collaboration and repair. The three elements I noticed at CES – expertise, area information and partnerships – align naturally with that ethos, coupled with our establishment’s emphasis on experiential studying, the place college students “study by doing”.
The query shouldn’t be whether or not to convey AI into the classroom – it’s already there – however how we put together college students to interact with it meaningfully. Know-how issues. However with out area understanding and robust partnerships, it’s inadequate.
Junghwan Kim is an assistant professor within the division of geography and director of the Good Cities for Good analysis group at Virginia Tech.
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