Why India Should Focus on Depth Over Breadth, ETEducation
India’s engineering education system has long been defined by scale. Thousands of colleges, dozens of specializations, and a curriculum architecture inherited from an era when physics labs, circuit diagrams and mechanical workshops represented the core of technological training. But the world around that system has changed faster than the system itself.
Artificial intelligence (AI) now cuts across every discipline, cloud infrastructure has replaced physical servers, and rapid prototyping has made it possible for undergraduates to build what used to require full-fledged research labs. Yet the country continues to produce graduates who often struggle with precisely what industry expects of them: judgment, adaptability, and the ability to navigate ambiguous, real-world problems.
Across the ecosystem, a quiet but pointed question has begun to surface: is the breadth-heavy, discipline-segmented model of engineering education still serving India’s needs? Or is it time to rethink what engineers should learn – and how?
ET Education interacted with industry and education stalwarts of Plaksha University: Trustee Hitesh Oberoi, Founding Vice Chancellor Rudra Pratap, and Dean of Academics & Professor Srikant Srinivasan. Instead of the prevailing model that asks students to memorise vast amounts of content across multiple streams, they advocate a leaner, sharper approach, one that privileges depth over catalogues, integration over silos, and reflection over rote. Edited excerpts:
There are hundreds of engineering colleges in India offering dozens of specializations. Why would a university choose to offer only a small number of technology-focused courses, rather than follow the broad-and-diverse model?
Srinivasan: We asked ourselves up front: what kind of graduates does India really need now? Instead of starting with disciplines, we started with attributes – ability to solve unstructured problems, technical rigor, interdisciplinary sensibility, teamwork, curiosity, ethical awareness. We designed the academic programme around those attributes.
The investment will power three core priorities: strengthening Plaksha’s capabilities in artificial intelligence (AI), data science, biological systems and robotics; building a robust startup incubation ecosystem; and developing state-of-the-art laboratories and student infrastructure comparable to leading global institutions.
By limiting under-graduate majors to just a few – such as Computer Science & Artificial Intelligence, Robotics & Autonomous Systems, Biological Systems Engineering, and Data Science, Economics & Business – we aim for depth over breadth.
Too many streams often means surface-level exposure. A lean, focused set allows us to insist on integration: computing plus biology, or AI plus social understanding. A narrow catalogue forces us to think of tech not as isolated silos – but as a system built by humans for humans.
Given that engineering education in India has traditionally emphasized breadth – physics, mathematics, core engineering, electives – what are the risks and benefits of breaking from that legacy?
Oberoi: The risk is that some may miss certain legacy areas – classic branches, perhaps long-standing notions of core courses. But we saw that many of those survive because “this is how engineering was done.” That’s tradition, not necessarily relevance. What we lacked was a design aligned to what the world needs today.
The benefit is considerable. By mixing disciplines – say life sciences with computing, or economics with data science – we create technologists who not only code, build or design but also appreciate context, policy, society. The narrow-but-deep curriculum invites students to think across fields. That kind of hybrid thinking is increasingly rare, but essential.
Moreover, this approach addresses a chronic challenge in Indian engineering education: many graduates emerge with degrees but lack judgment, problem-solving acumen, or readiness for real-world complexity.
But in a fast-changing world driven by AI, cloud, rapid prototyping – shouldn’t students learn everything? Doesn’t specialization come with a trade-off of flexibility?
Pratap: That’s exactly why we rethought the model. The future will demand both deep technical mastery and nimble adaptation. The core of computing, data, and AI must remain strong – as we embed, say, machine learning, computational thinking, data science from day one.
At the same time, we offer an environment where students explore broader foundations in the initial phase – before declaring a major – giving them time to discover their interests, rather than locking them into a specialization they may come to regret.
That blend of flexibility and focus can equip them not just for known problems, but for problems yet to emerge: bio-engineering, socially conscious AI, sustainable robotics, data-driven policy, and more.
Recent studies show that many Indian engineering graduates struggle with employability or lack the real-world readiness expected by industry. How does this lean-course, deep-learning model address that?
Srinivasan: The problem stems not just from outdated curricula, but from structure: segmented courses, compartmentalised learning, and little connection to real-world complexity. We believe education should be about building judgment. Thus students engage in long-duration, mentored projects, often combining technology, design, context, and uncertainty. Real life doesn’t hand you neat homework problems.
Also, by integrating fields – say biology and computing, or data science and economics – graduates get more than technical competence. They get cross-disciplinary understanding that allows them to adapt, to ask bigger questions, to build solutions that matter to society.
In a rapidly evolving job market where adaptability is as valuable as specialisation, this kind of holistic training can offer graduates a better foundation than a narrow, outdated degree.
Looking ahead, what does this kind of focused yet interdisciplinary model mean for the future of technical education in India?
Oberoi: It might signal a shift from mass-education to meaningful-education. When we try to expand course offerings endlessly, we replicate a one-size-fits-all model. But “one size” rarely fits complexity. The future will belong to those who understand systems – technical, social and ethical.
Pratap: If India wants to build technology that addresses local needs, climate challenges, healthcare stress, inequities – we need graduates who are not just coders or engineers, but systemic thinkers. A narrow but deep curriculum helps build that.
Srinivasan: More broadly, I hope the experimentation proves that scaling in higher education does not have to mean dilution. Lean design, thoughtful integration, and interdisciplinary depth could become a model – not just for niche tech universities, but for mainstream engineering education across India.
According to the Japan Student Services Organisation (JASSO), Japan today hosts more than 3 lakh international students. Yet, Indians account for just over 1,600 of them – fewer than even Nepal or Bangladesh send each year.
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