Nagaland’s September 3 launch of the IndiaSkills Nagaland Skill Competition 2026-27 gives young people a concrete route to demonstrate capability across 17 practical trades.
Nagaland’s September 3 launch of the IndiaSkills Nagaland Skill Competition 2026-27 gives young people a concrete route to demonstrate capability across 17 practical trades. That matters because employers increasingly want evidence of what people can do, not simply certificates showing what they studied.
The next step should be to build AI judgment into that skills agenda.
Nagaland already has a useful model. NIELIT’s Cyber Kushti 2026 will test whether participants can reason through security findings, validate them, prioritise them, defend their decisions, and distinguish genuine findings from false, duplicated, or incomplete AI-generated results. That design captures a skill that will matter far beyond cybersecurity.
As AI enters software development, administration, marketing, finance, design, and other fields, workers will face a new problem. Producing an answer will become easier. Deciding whether the answer deserves trust will become more valuable.
That means training should give students repeated practice in four things: checking AI output against reliable evidence, spotting exceptions that fall outside normal patterns, explaining why they accepted or rejected an AI recommendation, and escalating high-risk decisions to a human with the right expertise.
Nagaland has another reason to move quickly. Eastern Mirror reported in July that only 22 of 123 Nagaland participants had completed courses under the national SOAR AI-skilling programme, a completion rate of 17.9 percent. That does not argue against AI training. It suggests that access to courses alone will not build competence.
A better model would connect AI learning to visible performance. Students could receive a flawed AI-generated plan, identify the errors, correct them, and explain their reasoning. A mobile-app learner could test AI-generated code against edge cases. A graphic-design learner could verify whether generated material creates copyright, factual, or cultural problems. A retail learner could decide when an automated recommendation should be overridden.
These exercises would make AI literacy concrete. They would also give employers a better signal than tool familiarity.
Gleb Tsipursky, PhD, a behavioral scientist, CEO of Disaster Avoidance Experts, and author of The Psychology of AI Adoption at Work: From Resistance to Results (Georgetown University Press, 2026).