The 90% of the Syllabus That Generative AI Just Made Obsolete
Indian engineering education has been characterised by structure - strict syllabuses, set laboratory textbooks, and exam-oriented studies. However, with the emergence of generative AI systems such as ChatGPT, GitHub Copilot, and Claude into the mainstream, the concept of what engineers should learn is experiencing a radical change. The skills previously thought to be fundamental are quickly becoming obsolete. This shift can be seen especially evident in most engineering colleges in UP, where students are becoming more and more dependent on AI-related tools not only to resolve problems but also to reconsider how engineering is learned and implemented.
How Generative AI Is Redrawing the Rules
Over the decades, engineering curriculum has been about learning theory - algorithms, equations, and standard syntax of code. Much of that knowledge has however been automated by generative AI. Currently, an AI model can write, and optimize, document, and simulate performance. Such automation does not remove engineers; it just transforms the definition of engineering.
The value of the future engineer lies no longer in syntax or imitating standard designs. Rather, it is in the knowledge of systems, technology integration, and posing the correct questions. AI has the capacity to produce solutions, however, only humans have the capacity to formulate problems worth resolving. It is where traditional syllabuses fail: they continue teaching students to repeat textbook answers instead of creating their own.
Obsolete Skills vs. New Skills
Most course outlines have not changed: a focus on rote concepts (such as manual circuit design or static programming logic) and neglect of the actual skills that a modern engineer requires. Innovation as a real-life action presently occurs on the crossroads: software/hardware, data/ethics, automation/creativity.
Repetitive design calculation or debugging which once took weeks of student effort can already be done by AI. It is now time to understand how to assess AI output, analyze the results, and leverage them into working systems. The engineer of 2026 must not only know how to use an AI model but also know when he cannot trust it.
Simultaneously, there are new competencies coming into being. Quick engineering - the craft of posing accurate technical questions to AI - is becoming as important as writing effective code used to be. Engineers should also learn to orchestrate the tools, combine cloud services, APIs, and data into harmonious systems that AI can augment and not eliminate. Such skills require creativity, situational knowledge and critical thinking - skills impossible to automate.
The Traditional Curriculum Is Falling Behind
Academia and industry have long been at odds, but generative AI has increased the divide by a significant margin. The approval of university syllabuses can take years of bureaucracy, whereas AI capabilities change within a few months. Consequently, students are studying courses that had been of value one decade ago even as the world outside has already advanced.
Even a well-meaning faculty is limited - old laboratories, less access to real-life datasets and less education in new technologies. The net effect is a disconnect between theoretical graduates and those who are better ready to handle work processes that are automated and AI-controlled, which is the prevailing reality of engineering today. To address this gap, institutional inertia is a significant impediment, though in engineering colleges in UP, there has been an attempt to start using open-ended projects or interdisciplinary hackeathons in certain departments.
The Path Forward
Generative AI is not coming to unemploy engineers; it is coming to showcase how much of what we are teaching no longer has a value addition. The change is painful but unavoidable. The ones that change the quickest: those that can rewrite their material, redesign tests, and allow the students to learn how to use the tools, rather than memorize the formulas, will usher in the new wave of technical education.
Changing the attitude towards knowledge is the real struggle facing engineering colleges in UP and the rest of India, rather than implementing AI tools. When machines are able to remember all the formulae that have ever been written, the real engineer is the one who can create something new.