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India’s AI education boom faces a faculty crunch. Experts say universities must upskill teachers, attract specialised talent and deepen industry-academia partnerships.
The question of who qualifies as an AI faculty member is also becoming more complicated. (AI Image)
While Indian colleges are rushing to launch the latest AI and machine learning programmes, there is but one problem: the country may not have enough faculty equipped to teach them.
“The biggest issue is that demand has grown much faster than the supply of people who can teach AI and machine learning at an advanced level,” says Prof. (Dr.) Sanjay Kumar, Vice-Chancellor of Rayat Bahra University, Mohali.
Prof. (Dr.) Kumar, who has previously served leadership roles in DRDO and ISRO, explains that while institutions do not have enough faculty with advanced AI expertise, there is “a large base of capable Computer Science teachers, and that is where a significant part of the solution lies.”
Nishant Narang, Associate Professor in the Department of Computer Science and Information Systems at BITS Pilani, Off-campus, Delhi too echoes Prof Kumar, “India’s AI faculty challenge is not simply a shortage of teachers; it is a shortage of professionals who can bridge research, technology, and real-world deployment.”
Faculty need strong grounding in mathematics, statistics, algorithms and programming, while also keeping pace with generative AI, LLMs and multimodal systems. That pace of change is itself creating pressure on universities. “In this field, a syllabus cannot remain unchanged for several years,” Kumar told News18.
Academia vs Private Sector
At IIT Delhi too, the challenge is about attracting high-quality researchers in a fiercely competitive global market. Prof. Manav Bhatnagar, Head of the Bharti School of Telecommunication Technology and Management at IIT Delhi, says strong AI/ML candidates are simultaneously being pursued by technology companies, start-ups and leading international universities. “Academia cannot compete with the private sector on salary alone; it must offer a wider value proposition,” he says.
Institutions must also provide better computing infrastructure, research support, flexible funding and faster recruitment processes, says Prof Bhatnagar. “The more serious gap is often not academic capability, but limited exposure to production-scale systems, real-world data, deployment constraints, emerging tools and rapidly changing industry practices. These dimensions are difficult to acquire through classroom teaching and publications alone,” he stated.
Can Traditional Computer Science Faculty Teach Advanced AI?
The question of who qualifies as an AI faculty member is also becoming more complicated. Traditional Computer Science academics can certainly transition into AI, particularly because AI rests on established foundations in algorithms, mathematics, statistics and software systems. But advanced areas such as Gen AI, foundation models, AI agents, computer vision and reinforcement learning require deeper specialisation.
According to Prof Narang, traditional Computer Science faculty can teach many AI subjects effectively, however, for advanced areas such as Gen AI, LLMs, foundation models, AI agents and autonomous systems, AI safety and governance, and enterprise AI implementation, specialised expertise becomes increasingly important.
“To attract and retain AI talent, universities should also provide competitive start-up grants, advanced computing facilities, high-quality doctoral and postdoctoral support, joint industry laboratories, international mobility and faster administrative processes,” says Prof Bhatnagar.
Do Institutes Have The Required Infrastructure?
“The biggest challenge when recruiting faculty to teach AI and ML is ensuring their understanding of the real-world implications of developing and implementing AI and ML solutions,” says Dr. Monit Kapoor, Pro Vice Chancellor, Amity University, Bengaluru, and Director, Amity School of Engineering and Technology.
He adds that modern AI solutions involve several factors that must be carefully considered. Today’s AI engines rely on highly expensive IT infrastructure, making the efficient utilisation of this underlying infrastructure a key concern, even for IT organisations. Equally important is the software architecture needed to develop solutions that can effectively harness AI’s capabilities.
“The future is likely to favour a hybrid faculty model,” believes Narang, with universities combining traditional academics with specialist researchers, adjunct faculty and industry practitioners.
He also stresses that AI is no longer restricted to Computer Science. “The impact of AI cuts across many non-IT disciplines,” he says, adding that areas including management, law, psychology, design, mass communication and the humanities too need academic practitioners who are well-versed in AI usage.
Upskilling Is The Way Forward
For Indian higher education, therefore, recruitment alone will not solve the problem. Universities need sustained faculty upskilling, industry immersion, visiting and adjunct faculty, joint research laboratories, practitioner-led modules and stronger academic-industry partnerships. Research funding and opportunities for faculty and students to work on real-world AI projects will be equally important.
“Skill gap can be addressed through continuous faculty upskilling, interdisciplinary hiring, practitioner participation, co-designed curricula and stronger movement of experts between academia and industry,” says Prof Bhatnagar.
While Prof Kumar explains that recruitment alone will not solve faculty shortage. “Institutions need to invest much more seriously in upskilling the faculty already teaching in engineering colleges. Faculty development programmes have to go beyond short workshops and give teachers exposure to current research, industry projects and emerging AI technologies,” he said.
“The larger objective should be to create an environment where faculty continue learning alongside their students. In AI, that is going to be essential because the technology itself is evolving so rapidly,” he added.
Key Questions Answered
Ask News18Indian universities can attract top AI talent by upskilling existing teachers, attracting specialised talent, and deepening industry-academia partnerships. They also need to bridge the gap between research, technology, and real-world deployment.
Ask News18About the Author
Sukanya Nandy is a sub-editor at News18.com. She has been writing and reporting for the education and careers section of the website since 2021. She completed her graduation in English followed PG in …Read More
August 18, 2026, 3:53 PM IST
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