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How experiential learning is reshaping India's top colleges - The...
Indian colleges are shifting from traditional lecture-and-examination models to a culture...
OPINION | The future lies in integrating AI and core engineering...
India's goal of becoming a developed nation by 2047 is intrinsically linked to transformin...
What do future engineers need? How India's IITs are redefining en...
Engineering education in India is shifting from knowledge acquisition to skill development...
Why GCCs are moving towards specialised hiring from mass recruiti...
This evolution has led to a slowdown in overall hiring within the segment, with some large...
Indian colleges are shifting from traditional lecture-and-examination models to a culture of inquiry, application, and knowledge creation, emphasizing critical thinking and problem-solving.
India's goal of becoming a developed nation by 2047 is intrinsically linked to transforming its higher education system, emphasizing innovation, entrepreneurship, and nation-building beyond mere employment through the National Education Policy 2020.
Engineering education in India is shifting from knowledge acquisition to skill development and practical application, driven by a need to prepare students for rapidly evolving technological landscapes.
This evolution has led to a slowdown in overall hiring within the segment, with some larger GCCs experiencing headcount reductions
AI sector became a space where women could establish themselves as pioneers from the outset
Oral cancer remains one of the most significant health challenges in India, accounting for nearly 30 per cent of all cancer cases in the country
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Technical universities need to prioritize quality over quantity in engineering education, addressing deficiencies in practical skills and continuous learning.
While employers seek AI talent, a stark reality is that many graduates, despite having degrees, lack the hands-on skills to be deployable
AI reliance, while efficient, carries significant risks as these systems are trained on past data, potentially perpetuating existing biases and overlooking candidates with untapped potential