When AI can write code: students rethink learning, exams, and engineering careers

When artificial intelligence can write code in seconds, debug programs, and even explain complex algorithms, many computer science students are beginning to question the value of spending four years learning to do the same. Across Indian campuses, a growing reliance on AI tools is transforming how students learn, how teachers teach, and how future engineers are being prepared for an uncertain job market.

“I am learning core subjects like OS, DBMS, and DSA, and I have noticed that many students around me are relying more on external resources and projects than on textbooks or lectures. At the same time, I see self-taught developers building impressive portfolios, contributing to open source, and landing solid jobs without a degree at all. It makes me wonder— in 2026, is a computer science degree still worth the time, effort, and cost, or is it just one of many valid paths into tech now?” says Sukhwinder Singh, a final-year Information Technology student from a reputed university in Maharashtra.

Empowerment and uncertainty

Such questions have become common among the student fraternity in Indian colleges and universities ever since generative AI tools began eliminating redundant jobs in the technology industry across the globe. “In the next two years, jobs like data entry, customer service, and manual coding will disappear from existence. New roles such as AI prompting, AI architecture, and system design will be in demand. In a similar manner, the pattern of learning has undergone tremendous changes in recent times,” says Professor Jayakumar Sadhasivam of VIT University.

“At this point, AI does all the coding—not most of it, but all of it. My classmates and I don’t write code anymore. We describe the problem, get a complete solution from AI, and then our job is to understand what the AI has produced. We try to understand the logic and make small fixes, but the solution is entirely generated. Writing code line by line just doesn’t happen,” admits R. Rahul, a third-year Computer Science student at BITS Pilani.

With the reduced emphasis on coding in education, learners may experience a mix of empowerment and uncertainty. A final-year Computer Science student, Nikita Vasa says, “I am overly reliant on AI tools for assistance while completing my coursework. This caused internal conflict during a lab examination. We were not given any smart tools, only an old Ubuntu text editor for a programming test.” She confides that the experience left her worried and made her realise that the amount of thinking, writing, and testing code she did had drastically reduced over the past few months.

“Coding is still a core skill in computer science education. A strong foundation is essential, and understanding basic structures is crucial. AI can write code, but it can never be fully secure. For instance, when we plan to personalise education to individual needs, we have to feed in more details, yet we still get only 60–70% accuracy. AI can explain code, but how many lines can it explain? Students must understand functionalities to carry out modifications,” says Prof. Sadhasivam.

When traditional exams meet AI-era learning

S. Subhashini, a first-year Computer Science student at Shri Govindram Seksaria Institute of Technology and Science, Indore, recently appeared for a midterm test in Python. “The coding portion went horribly. We were not informed about the test pattern. Also, we were never instructed to write complete code on paper in class—so I just froze. It is 2026; why are we still taking programming exams on paper?” she wonders.

VIT University offers examination formats that eliminate the use of AI assistance. “At VIT, we do not allow students to use any AI tools during examinations. We follow a traditional pattern where students take exams without AI support,” says Prof. Sadhasivam.

He adds that the question paper pattern focuses on testing problem-solving skills, including application, evaluation, understanding, analysis, and creative thinking. “However, an IIT has allowed students to use generative AI tools during examinations. This, in my view, reduces students’ capacity for creative thinking and learning,” he says.

Teaching and syllabus

“Until two years ago, I taught using conventional methods such as writing code on the blackboard while students copied it,” says Prof. Sadhasivam. He notes that new teaching approaches aligned with current realities have since been adopted. “Classes have become more interactive. Tools have changed immensely, and students’ methods of consuming knowledge have evolved drastically. I hardly see students taking notes anymore. I even encourage them to use AI tools, Google, and institutional reference materials,” he says.

He adds that AI tools are being aggressively utilised in premier institutions across the country. These tools have reduced academic stress by personalising learning and explaining concepts in simple terms.

“I study at a Tier-1 college with a dedicated AI department. The curriculum was designed recently and included good courses in Artificial Intelligence and Machine Learning. However, AI discoveries are happening so fast that a four-year curriculum becomes outdated by the third or fourth year,” says Akshat Gosain of MIT World Peace University, Pune.

“The syllabi for B.Sc. Computer Science and BCA remain different. B.Sc focuses more on theory, while BCA is application-oriented, with earlier exposure to coding and less mathematics. These students often need to pursue a master’s degree to become employable. B.Tech Computer Science covers technologies in greater depth,” says an academician on condition of anonymity.

From syntax to systems: What employers really want

The IT industry has undergone a major shift with the advent of AI tools. If AI can generate code, what differentiates a strong entry-level engineer from an average one?

“A strong junior understands why the code works—UI/UX design, logic, data flow, and debugging—while an average one can only prompt AI and hope it runs. Juniors who can reason, debug, and read messy code are still rare and still hired,” says G.K. Divya, IT leader and Senior AI Architect in Bengaluru.

The industry has long highlighted the gap between education and employability. Has AI bridged this gap, or are companies still retraining graduates? “Most graduates are trained only in basic syntax. Companies still spend months retraining them in system design, real-world constraints, and debugging,” says Ms. Divya.

“when AI output breaks, candidates who rely too heavily on AI freeze. It shows weak debugging skills and a lack of mental models of systems,” she adds.

Prof. Sadhasivam emphasises that awareness of AI’s dual-use nature is essential. “Hackers can use it to scam people, but on the positive side, AI tools help learners understand concepts, automate simple tasks, convert text to speech, summarise content, and assist problem-solving. How you utilise it matters,” he concludes.

(Sign up for THEdge, The Hindu’s weekly education newsletter.)