AI Engineering Courses: Will ‘new-age’ programmes help BTech graduates in testing times?

NEW DELHI: In 2019, in a bid to correct the oversupply of engineering graduates and ballooning vacant seats in technical colleges, the All India Council for Technical Education (AICTE), the apex regulator for the discipline, announced an unprecedented freeze on starting new engineering institutes or courses. As it rolled out the ban in the academic year 2020-21, the council made an exception for courses in certain ‘emerging areas’, listing Artificial Intelligence (AI), Internet of Things (IoT), Blockchain, Robotics, Quantum Computing, Data Science and Cyber Security among them.

The provision saw a number of engineering institutes around the country adding new programmes in the emerging areas, especially in AI, Machine Learning (ML) and Data Science, using a variety of nomenclatures. The programmes gained popularity among engineering aspirants, even as lay-offs in global and Indian tech companies stagnated campus placements. With the generative AI tools rapidly disrupting a large swathe of professions and sectors, including the bulk recruiting IT services industry, the jury is still out on the utility and impact of these programmes.

Lokmanya Tilak College of Engineering, an autonomous institution under the University of Mumbai (MU) at Navi Mumbai, was among the first engineering colleges to embrace the new-age technologies, introducing three specialised programmes under the computer science branch – AI and ML; data science; and IoT and cyber security – in the 2020-21 session. The first batch of students to take up these courses has already graduated.

The institute found the three programmes to be in as much demand as the plain BE in Computer Engineering. This reflects a state-wide trend, with 8,514 of the 9,642 (88%) seats allotted to emerging areas in undergraduate courses in Maharashtra in 2023-24 filled. Overall, 90% of computer science and allied branches’ spots were taken that year.

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‘Ready engineers’

LTCE Navi Mumbai principal Subhash Shinde also serves as the chairman of MU’s Board of Studies in Computer Engineering – the body that decides the structure and curricula of the courses. He said that these programmes are aimed at giving “ready” engineers to the industry. “While the syllabus for the first two years of the new courses is the same as that of the parent branch, the latter half sees the addition of subjects specific to the respective specialisations. We can't put everything industry needs in the curricula, as we have to think about the first as well as the last person. But the idea is to equip students with the basic knowledge to sustain in the industry,” he said.

Ajay Mittal, a professor at Panjab University’s University Institute of Engineering and Technology, who was part of the AICTE committee that prepared the model curriculum for BE/BTech in Computer Science and Engineering (CSE) - AI & ML in 2021, also makes a similar claim. “These courses are specialised industry-oriented courses, specifically designed keeping employability and employment opportunities for graduates in mind,” he said.

AI, ML Courses: ‘Marketability potential’

However, some point out that, more than the actual content of the course, it was their marketability potential that led to them being taken up by private colleges. “In 2020-21, many institutes lapped up these courses. This is because they are allowed only a limited number of seats in a particular course. To bypass this restriction, they seek to add programmes with different nomenclature. That has always been the game in the country,” said Pankaj Jalote, the founding director of and currently a distinguished professor at the Indraprastha Institute of Information Technology (IIIT) Delhi. He had also chaired the AICTE panel that revised the council’s model syllabus for the undergraduate CSE programme in 2022.

Jalote suggests that, considering the frequent evolution of technology, integrating the generic CSE course with AI and other elective courses is a better approach than having multiple specific programmes. “When the new-age courses were first introduced in 2020, machine learning was the big thing and the courses were shaped around it. Today, it’s no longer as important. Generative AI is the next big thing,” he said.

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Work opportunities

Opinion is similarly divided on whether these courses offer a better deal to students seeking work after graduation. While most agree that employers make hiring decisions based on their specific requirements, some believe that these courses give job hunters a leg up, as the AI-driven automation is increasingly making entry-level jobs at software firms scarcer by the day.

“The industry looks for all kinds of positions – AI, data science, IoT or plain computer engineering – depending on their specific requirement. One can’t be a generalised assessment. If a learner’s good and can adapt, the industry may take them,” said Shinde.

Kamal Karanth, co-founder of Xpheno, a Bengaluru-based specialist staffing firm, has a different perspective. “Enterprises hiring freshers and entry-level talent are always on the lookout for talent with the shortest runways to becoming job ready. Under these conditions freshers with specialisations, especially in hot skills and trending domains have a sure edge over the generic qualifications. Under continuing conditions of oversupply of fresh engineering talent, specialised skills and qualifications help clear the clutter in a recruitment funnel.”

However, others like Jalote believe that specialised courses might prove to be unnecessary at the time of recruitment. “The companies look for foundational knowledge and skills, such as data structure, among candidates. The colleges hype and teach more advanced stuff, even though that’s not useful in entry-level assignments,” he said.

According to experts, if the new-age programmes are to meet their objective, they need to be constantly updated in tandem with changes at the workplace. This becomes particularly challenging as many of the colleges, especially those outside the metropolitan areas, are struggling to provide faculty and laboratories for these programmes. The faculty teaching these courses should also undergo periodic upgrades, they said.

“Earlier courses had to be shut down because they didn't evolve with the time. Now is the time of AI; one can't run away from it. The role of teachers is changing. The government is clearly advising the institutes to conduct faculty development programmes. Such programmes are needed continuously to polish teachers’ skills. Many institutes are hiring professors of practice,” said Sanjeev Singh, a computer science professor at the Delhi University’s Cluster Innovation Centre.

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‘Oversupply’ of engineers

Even with the specialised skills, the students may not find jobs readily available due to the present sluggishness of the tech job market. However, experts remain hopeful, expecting a turnaround if and when the impact of automation plateaus and the Indian companies move towards more skilled work.

“There's definitely an oversupply in the computer [science] and allied branches. The demand was much higher during the pandemic, but it subsided in the last two years. However, the intake in these courses kept increasing,” said Shinde.

“The low-to-no campus activity seen in more than the last three years has negated nearly all interventions in improving the employability of graduates,” adds Karanth.

According to Jalote, the future of Indian IT engineers is currently uncertain.

“There's a reasonable argument that lower-level skills may not be valued and will keep reducing as the AI tools keep getting sophisticated. But people with higher skills, such as conceptualising user interface (UI) and databases are more likely to be in demand. Unfortunately, for Indian IT aspirants, a lot of work was often low tech - documentation, testing and coding. So, the manpower requirement may reduce,” he said.

He predicts that while the Indian IT sector will continue to grow, the pace of absorption of employees will likely be slower. “The Indian IT model was essentially linear (the recruitments were directly proportional to industry growth). However, for some time that linearity may not hold. One hypothesis is that productivity gains from new technology will cap out and the equation will become linear again,” he said.

Karanth, however, remains more optimistic. “India is set to shift its position in the AI/ML and Data Science domain from the service end to engineering end in the coming years. This shift in position will mean and require intake of specialised local engineering talent. The return of mainstream hiring action in the technology domain will open up opportunities for specialised talent with qualifications and experience in this space.”