A Lab in Every College?
Across India's educational landscape, a significant transformation is underway. Aligning with the National Education Policy (NEP) 2020, which emphasizes integrating AI at all levels, universities and even schools are rapidly setting up dedicated Artificial
Intelligence laboratories. From specialized AI schools in Maharashtra to government-funded seats in technical specializations, the push is undeniable. Institutions like Chandigarh University are collaborating with tech giants like Intel to launch 'IndiaAI Data Labs', promising students hands-on experience. On paper, the strategy is clear: build the infrastructure to create a generation of AI-ready professionals. The goal is to position India as a global leader in technology, and these labs are the visible, concrete first step in that ambitious journey. But as the infrastructure grows, a critical question emerges about its real-world impact.
The Persistent Skills Gap
Despite this flurry of activity, industry reports paint a concerning picture. There is a massive and growing gap between the demand for AI talent and the supply of work-ready graduates. A NASSCOM-McKinsey report projected a potential shortage of over 1.4 million AI professionals by 2026 without a significant acceleration in upskilling. Data from the Ministry of Electronics and Information Technology has shown that only a small fraction of the country's IT workforce is actually skilled in AI. The issue is not a lack of degrees or certifications, but a deficit in practical, applicable skills. One report highlighted that only about 46% of graduates are considered employable for AI and machine learning roles, indicating that more than half are not ready for the workplace despite their qualifications. This reveals a fundamental disconnect between academic training and industry needs.
Beyond Theory and Textbooks
The core of the problem lies in how AI skills are taught. Traditional classroom learning, focused on theory and textbooks, is fundamentally unsuited for a field as dynamic and application-driven as artificial intelligence. A 2026 report from the HR professional society SHRM found that while organizations recognize the skills challenge, nearly 60% of learning and development budgets are spent on less effective formats like digital self-paced content, with only 3% dedicated to hands-on learning. True AI proficiency is not just about understanding algorithms; it's about knowing how to frame a problem, source and clean messy real-world data, select and train a model, interpret its results, and understand its ethical implications. These are not skills learned from a multiple-choice quiz. They are forged through experience, iteration, and failure—the very essence of practical project work.
What Makes a Good Practical Assignment?
This is where the focus must shift. A truly effective AI lab is not just a room with powerful computers; it's an environment for applied problem-solving. A good practical assignment moves beyond generic textbook examples. Instead of building yet another image classifier for a standard dataset, students should be tasked with developing an AI solution for a local, tangible problem—like optimizing traffic flow in their city, predicting agricultural yields for local farmers, or creating a language tool for a regional dialect. These projects force students to confront the entire project lifecycle, from data acquisition to deployment challenges. This approach transforms them from passive learners into active creators and problem-solvers, developing the critical thinking and adaptability that employers desperately seek.
Industry as the Co-Professor
Universities cannot and should not do this alone. The most effective practical assignments are born from deep collaboration between academia and industry. Companies can provide real-world problem statements, anonymized datasets, and mentorship from practicing data scientists and engineers. Partnerships, such as the one between UPES and Salesforce to create a co-developed curriculum, exemplify this 'classroom-to-corporate' model. When industry experts help design and evaluate projects, the curriculum remains relevant to a field where tools and techniques change every few months. This synergy ensures that the skills being taught in the lab are the same ones required on the job, directly bridging the gap between education and employability. These collaborations turn a university lab from a simple training centre into a true engine of innovation.
