Why university curriculums should include the fundamentals of Generative AI

Generative AI's transformative potential in India requires universities to adopt interdisciplinary, inclusive, and research-driven approaches to education.

Generative AI is transforming industries globally. The growing integration of AI across sectors like healthcare, agriculture, and entertainment highlights the urgent need for a skilled workforce proficient in these technologies. Indian universities and higher education institutions, therefore, have a critical responsibility to equip students with a strong foundation in Generative AI, including the science driving this transformative technology. As AI increasingly powers the algorithms shaping our world, students must become both creators and expert users of these systems.

However, this task is far from straightforward. Unlike traditional software, where the logic is predefined, deep learning models continuously adapt and improve through training. AI model development is as much an art as it is a science. To build Generative AI models, students must master the mathematics of optimisation and neural network architectures, learning how these models evolve and generalise from data.

How should university curriculums evolve in response to these new challenges? This responsibility extends beyond Computer Science students. As AI permeates every industry, even students in fields such as Economics, Biology, or Design can benefit from understanding the fundamentals of Generative AI algorithms to be able to leverage AI technology within their respective disciplines.

A robust curriculum that balances theoretical knowledge with practical application is essential to teach Generative AI. Indian universities must design courses that include theoretical modules on machine learning algorithms, neural networks, and natural language processing complemented with practical labs and projects that allow students to apply concepts in real-world scenarios.

For example, students could work on developing large language models (LLMs) for natural language generation in regional languages such as Hindi or Tamil to allow them to gain a deep understanding of the model training process, while also learning to use advanced cloud GPUs and the necessary libraries to optimise training. Collaborating with industry leaders, as recommended by NASSCOM’s AI Adoption Report, could further bridge the gap between academic learning and industry needs.

Beyond the technical knowledge of building models, Generative AI has the potential to transform a wide range of industries. Its applications intersect with numerous fields, including Computer Science, Maths, Linguistics, Media and Communication, and Social Sciences. To offer a well-rounded education, Indian universities should adopt an interdisciplinary approach, integrating insights from multiple disciplines to prepare students for the broad impact of this technology.

Indian universities can also leverage online platforms such as Coursera, edX, and India’s NPTEL to offer supplementary courses on Generative AI. Equally vital are industry internships, which provide students with practical, hands-on experience in real-world AI projects.