As artificial intelligence (AI) rapidly transforms engineering and industry, India’s Indian Institutes of Technology (IITs)—long seen as the nation’s flagbearers of technical excellence—face a critical question: Are they cultivating independent thinkers capable of shaping the future, or primarily training students to operate today’s fast-evolving tools? Central to this challenge is faculty readiness. AI evolves faster than traditional academic cycles, raising concerns about whether IIT faculty, especially mid-career professors, can keep pace with technological change and teach AI at scale.
“IITs have always aimed to produce thinkers rather than mere tool-users, and that remains the goal as AI becomes a core engineering skill,” says Professor Manindra Agrawal, Director of IIT Kanpur. He highlights that the foundational strengths of IITs—mathematics, algorithms, systems, and cross-disciplinary research—anchor innovation, even as AI tools evolve rapidly. Faculty and PhD students actively publish in top-tier AI venues, ensuring that research continually feeds into teaching, mentorship, and curriculum development.
Yet challenges remain. “Not all faculty were originally trained in AI,” Professor Agrawal notes, “and enabling mid-career academics to transition effectively is an ongoing process.” IITs are responding through continuous upskilling, interdisciplinary recruitment, industry partnerships, and curriculum renewal, though he emphasizes that more is needed to maintain flexible, forward-looking programs.
Some initiatives show progress. IIT Guwahati’s dedicated Data Science and AI School offers structured degree programs, maintains strong industry collaborations, and continuously updates curricula to reflect emerging trends. Faculty across IITs engage in workshops, seminars, AI bootcamps, and online courses, helping them stay aligned with global developments. “Our faculty are prepared to teach AI at scale and are already doing so,” says Professor Ratnajit Bhattacharjee of IIT Guwahati.
IIT Kanpur is extending training beyond degree programs through AI-focused online courses, leadership programs, and corporate upskilling initiatives. Such measures ensure that both faculty and students remain connected to industry-relevant AI practices.
A major concern is whether academic curricula can keep pace with rapid industry innovation. Traditional course structures often lag behind real-world developments. Some IITs are addressing this: IIT Delhi recently overhauled undergraduate, postgraduate, and PhD syllabi to emphasize flexibility, hands-on learning, and emerging technologies like AI. Still, gaps remain. Graduates often leave with tool knowledge but limited practical experience in deploying cutting-edge AI systems.
To bridge this gap, IITs are deepening industry and institutional partnerships. IIT Kanpur collaborates with sectors ranging from fintech to digital agriculture, integrating AI applications into teaching and research. MoUs with public enterprises, like NMDC, bring real-world challenges into the classroom, reinforcing the importance of industry-academia linkages.
The debate over whether IITs produce thinkers or tool-users is not binary. It lies at the intersection of pedagogy, research culture, and institutional agility. Delivering deep theoretical grounding while providing practical, scalable AI education is complex—but IIT faculty are rising to the challenge. Success will depend on continuous upskilling, flexible curricula, strong industry engagement, and institutional courage to redefine engineering education for the AI era.
As Professor Agrawal concludes, “The real task is to ensure that curricula remain flexible and forward-looking, so that graduates are prepared not just for today’s tools, but for tomorrow’s transformations.”
