AI is no longer just for engineers: How it is changing what students expect from universities

Artificial intelligence is beginning to change the value proposition of higher education in India. As AI tools become part of everyday work across sectors, students are increasingly looking beyond a degree or campus brand and asking whether their university can give them the skills to remain relevant in an AI-driven job market.

The shift is already visible in the way institutions are redesigning courses, introducing AI labs, expanding industry partnerships and bringing artificial intelligence into disciplines far beyond computer science.

The Centre, too, is pushing institutions towards more practical AI education. In May, the Ministry of Electronics and IT said an AI Curriculum Taskforce was working on revamping AI education, with greater emphasis on industry use cases, practical exposure, faculty development and responsible AI.


For universities, this is becoming more than a curriculum exercise. It is increasingly a question of how they remain attractive to students who know that the skills they learn today could be transformed by technology before they graduate.

Students want more than a degree

For decades, the traditional higher education proposition was relatively straightforward: enrol in a recognised institution, earn a degree and use that qualification to enter the job market. AI is complicating that equation.

Students are increasingly looking for practical exposure, industry interaction, technology skills and evidence that their education is aligned with the jobs they are likely to pursue. Recent research and industry discussions have highlighted the growing gap between academic learning and the practical AI capabilities employers want.

This is pushing universities to rethink what happens inside classrooms. Instead of treating AI as another specialised subject for engineering students, institutions are beginning to integrate it into multiple disciplines.

“Artificial Intelligence is no longer confined to computer science, it is transforming every profession. We believe every graduate, irrespective of their discipline, must understand how AI will shape their profession,” said Viraj Sagar Das, Pro Chancellor, Babu Banarasi Das University, and President, BBD Group.

The change reflects a broader realisation: a lawyer may need to understand AI-generated evidence and intellectual property issues, a marketing professional may work with generative AI tools, an educator may use AI-assisted learning platforms, while a healthcare professional may increasingly interact with AI-supported systems.

The technology may differ, but the underlying requirement is similar, professionals need to know how to work with AI without surrendering their own judgement.

The rise of ‘AI plus domain expertise’

This is creating a new model of employability: AI skills combined with domain knowledge. A student does not necessarily need to become an AI engineer to benefit from artificial intelligence. What matters increasingly is knowing how the technology can be applied to a particular profession.

That is why universities are experimenting with programmes combining AI with management, law, data science, cybersecurity and other areas.

“Today’s employers seek professionals who can collaborate across disciplines, think critically and adapt to rapidly changing environments. BBDU, for instance, has incorporated AI into nearly 10% of the curriculum across its programmes, including engineering, management, law, dentistry, pharmacy, architecture, agriculture, hospitality, media and education” Das said. \

From AI courses to AI-enabled campuses

The transformation is not restricted to what appears in the syllabus. Universities are also investing in AI laboratories, digital learning platforms, smart classrooms and technology-enabled teaching. Industry partnerships are becoming another route for institutions to expose students to tools and practices that may not yet be fully established within conventional academic structures.

Government policy is moving in the same direction. According to the Ministry of Electronics and IT, more than 2,100 AICTE-approved institutions offered courses in AI and other emerging areas during 2025-26, with an approved intake exceeding 2.8 lakh students. The government has also called for greater practical exposure and industry-linked learning in AI education.

The emphasis on practical learning is important because simply adding the word “AI” to a course title may not be enough.

Employers are increasingly looking for people who can use technology to solve actual problems, evaluate AI outputs, understand limitations and work responsibly with the technology.

Faculty face a new challenge

AI is also changing the role of teachers. Universities cannot expect faculty members to teach emerging technologies effectively if their own knowledge is not regularly updated. This makes faculty training an important part of the AI transition.

The Centre's proposed AI curriculum overhaul has specifically identified faculty development as one of the focus areas, alongside infrastructure and practical exposure.

For institutions, this could mean a move away from the traditional model in which a curriculum remains relatively stable for several years. AI's rapid evolution makes continuous updating increasingly necessary.

“Curriculum today cannot remain static. Industry evolves every year, technology evolves every few months, and universities must evolve continuously to remain relevant,” Das said.

That puts pressure on universities to establish stronger feedback loops with employers, technology companies and industry professionals.

Students also need to learn what AI cannot do

There is another dimension to the changing university experience: learning how to use AI responsibly.

As generative AI becomes easier to access, students can use it for research, writing, coding, analysis and other academic tasks. That creates opportunities for personalised learning but also raises questions around academic integrity, over-reliance on technology and whether students are developing the underlying skills they need.

Universities are therefore having to rethink assessment as well as teaching.

The government’s AI curriculum taskforce has proposed responsible AI and AI governance as continuous components of AI education rather than isolated subjects.

Das argues that the objective should go beyond teaching students how to operate AI tools.

“We want them to understand where AI can create value, where human judgment remains indispensable, and how technology can be applied responsibly, ethically and for the benefit of society,” he said.

That distinction could become increasingly important as AI systems become more capable.

The university experience is being redefined

The larger change is that students may increasingly judge universities on outcomes beyond examination results and placement statistics.

Access to AI tools, practical projects, industry exposure, research opportunities, entrepreneurship programmes and interdisciplinary learning could become increasingly important factors when students compare institutions.

For universities, that means competition is likely to shift from simply offering new courses to demonstrating whether those courses translate into useful capabilities. India's higher education system is already moving in that direction, with policymakers emphasising multidisciplinary learning, skills integration, experiential education and stronger university-industry links.

The challenge will be ensuring that the AI rush does not turn into a race to attach AI labels to existing programmes without meaningful changes to teaching and learning.

For students, the most valuable university may ultimately not be the one that offers the most AI courses, but the one that teaches them how to combine technology with judgement, creativity and deep knowledge of their chosen profession.

As Das puts it, “The future will not belong to those who compete with AI, it will belong to those who know how to harness its potential.”

That is increasingly becoming the new test for higher education: not simply whether universities can teach students about AI, but whether they can prepare them to work with it.