Prof. (Dr.) Daviender Narang
Artificial intelligence is not just challenging education – it is exposing its weaknesses.
Universities rely on models that reward information retention and standardized assessment. These models focus on predictable outputs. AI systems now perform many of these tasks faster than students.
They write essays, solve case studies, generate code, and simulate discussions. This creates a direct challenge for higher education. If you measure success through tasks machines perform easily, you reduce academic relevance. This is not an argument against AI. This is a call for change in universities. Many teaching methods and assessments now match tasks handled by algorithms.
Students complete work with minimal human effort. You need to rethink what you value in education. The key question shifts. Why do you focus on skills machines already perform well. AI does not replace human judgment. You need context aware and ethically grounded decisions. Real world decisions are not binary. You face trade-offs, uncertainty, and long term consequences. Data alone does not capture these factors. Human judgment evaluates context, values, and impact. Universities need to shift focus.
You train students to handle ambiguity. Universities move beyond fixed answers. They build the ability to make informed decisions in complex situations. AI does not carry accountability. One should remain responsible for decisions and outcomes. When decisions fail, consequences fall on people, not algorithms. This creates a clear need for responsibility in education. Universities must build ownership and ethical clarity in students. They should integrate these elements into every course and treat them as core outcomes, not optional topics.
AI tools increase output speed. Students produce more work in less time. Speed does not equal understanding. High output does not reflect depth of learning. Universities need to avoid output focused evaluation. Universities measure depth, not volume. True learning shows in questioning ability. Students connect ideas across domains. Students apply knowledge in new situations. They should focus on these outcomes during assessment. Discomfort plays an important role in learning. AI tools reduce effort and simplify tasks. Learning improves when students face difficulty. Students deal with complex problems. Students make mistakes and reflect on them. If AI removes this process, learning weakens. Students gain efficiency but lose resilience. Universities need to design tasks that retain challenge. The universities should ensure students engagement with complexity to build resilience through effort and reflection. This is where universities must draw a line.
UNIVERSITIES NEED TO DESIGN TASKS THAT RETAIN CHALLENGES AND ENSURE ENGAGEMENT
Higher education does not compete with AI. The universities should focus on human capabilities, developing critical thinking, ethical reasoning, strengthen creativity. In this way the educators should train students to take decisions with incomplete information. These capabilities define performance in an AI driven environment.
AI will continue to evolve. One cannot avoid this change. Universities must choose their response. The key issue is not tool usage rather the key issue is judgment. The universities should train students to decide when to rely on AI and train them to act without it.
The author is Director, Jaipuria Institute of Management, Ghaziabad.
Published by Hindustan Times
