1. AI as the Future of Academic Advancement

Vellore Institute of Technology (VIT) has positioned artificial intelligence (AI) as a core driver of personalised learning and academic transformation. At The Hindu Tech Summit 2026, the Vice-President of VIT highlighted AI as the “common denominator” across disciplines, signalling a structural shift in higher education.

The emphasis is not merely on digital tools but on embedding AI into curriculum design, teaching-learning systems, and student evaluation. This reflects a broader transition from content delivery models to adaptive, technology-enabled learning ecosystems.

The institutional focus on AI aligns with India’s aspirations under Digital India, National Education Policy (NEP) 2020, and the push toward Industry 4.0 competencies. Ignoring such integration risks widening the employability gap and making graduates misaligned with emerging labour market demands.

The governance logic is that higher education must anticipate technological change rather than react to it. If AI integration is delayed, India risks producing graduates with obsolete skill sets, undermining demographic dividend advantages.

2. Faculty Development and Institutional Capacity Building

VIT has established facilities such as a Teaching Learning Centre of Excellence and faculty development centres to train educators in emerging domains like agentic AI and AI applications in solar energy.

The emphasis on faculty upskilling underscores that technological reform in education cannot succeed without teacher preparedness. Faculty training is identified as a key determinant of quality education.

This approach reflects a systems-level reform, where institutional capacity building is prioritised over isolated technological adoption. In public policy terms, human capital enhancement within institutions is foundational for sustainable reform.

The logic is that technology without trained educators results in superficial adoption. Without continuous faculty development, AI integration may remain symbolic rather than transformative.

3. Multidisciplinary Integration of AI Across Disciplines

AI and IoT are being integrated beyond engineering disciplines. Law students are exposed to cybersecurity law and AI in law, while agriculture students learn drone technology, remote sensing, and smart farming techniques.

The model promotes domain expertise combined with technological literacy, thereby operationalising the multidisciplinary vision of NEP 2020. AI becomes a transversal skill rather than a specialised niche.

By enabling programme migration and allowing students to study subjects beyond their core discipline (e.g., mechanical engineering students learning marketing or specialising in IoT), the institution aims to enhance employability and adaptability.

The development rationale is that future labour markets demand hybrid skill sets. If higher education remains siloed, graduates may struggle in interdisciplinary, innovation-driven sectors.

Implications:

• Enhanced employability through skill diversification
• Greater alignment with Industry 4.0 requirements
• Promotion of innovation and entrepreneurship

4. Digital Divide and Technology Access

While promoting AI-based education, the issue of digital divide was acknowledged. VIT has initiated online courses to reach students in remote areas.

The role of the State and Central governments was highlighted, particularly:

• State schemes providing free laptops to students
• Reduction in bandwidth costs by the Union government

However, network reach in remote and hilly areas remains a constraint, pointing to infrastructure asymmetry across regions.

This connects directly with GS2 themes of inclusive governance and equitable access to public goods. Without robust digital infrastructure, AI-led education reforms risk reinforcing inequality.

The governance logic is that technological advancement must be inclusive. If connectivity gaps persist, AI-enabled education may deepen rural-urban disparities rather than bridge them.

Challenges:

• Inadequate network coverage in remote areas
• Financial limitations of private institutions in extending scholarships
• Infrastructure constraints in digital delivery

Policy Needs:

• Increased public funding for higher education
• Expansion of broadband infrastructure
• Wider reach of schemes like the Prime Minister’s Research Fellowship

5. Affordable Higher Education and Regulatory Liberalisation

The discussion emphasised the need for affordable higher education and greater government funding. Private institutions face constraints in providing large-scale scholarships.

It was suggested that government policies should adopt a liberal approach by allowing institutions to open more campuses and increase student intake. Such expansion could improve access and reduce supply-side constraints in higher education.

This aligns with India’s Gross Enrolment Ratio (GER) targets under NEP 2020, aiming to significantly expand higher education participation.

The development logic is that demographic dividend benefits materialise only if higher education is accessible and affordable. Without adequate funding and regulatory flexibility, expansion may remain limited and exclusionary.

6. Hackathons, Industry Linkages and Entrepreneurship

Hackathons at VIT are used as assessment tools, integrating industry-provided problem statements into coursework. This bridges the gap between theoretical instruction and practical problem-solving.

The Technology Business Incubator supports startups emerging from classroom innovation, demonstrating a pipeline from academic exercises to entrepreneurship.

Close industry collaboration also includes modules by industry experts and soft skills training, improving job readiness. Short programmes are offered for industry professionals, fostering academia-industry synergy.

This approach reflects the Triple Helix Model (University-Industry-Government collaboration), crucial for innovation-led growth under GS3.

The governance rationale is that employability and innovation require ecosystem integration. If academia remains detached from industry, skill mismatch and underemployment may increase.

Outcomes:

• Enhanced startup culture
• Industry-aligned curriculum
• Improved employment prospects
• Promotion of problem-solving skills

7. Pandemic Resilience and Continuity of Education

During the COVID-19 pandemic, VIT transitioned to live online education and ensured device and network access for students. Students were brought to campus in isolation phases to ensure continued learning.

This reflects institutional adaptability and resilience in crisis management, linking to disaster preparedness in social infrastructure sectors.

The experience highlights the importance of digital readiness, institutional agility, and contingency planning in maintaining educational continuity.

The policy lesson is that crisis resilience requires prior investment in digital infrastructure and administrative flexibility. Without preparedness, learning disruptions can cause long-term human capital losses.

Conclusion

The VIT model illustrates how AI-driven, multidisciplinary, and industry-linked higher education can align with India’s developmental priorities. However, technological transformation must be accompanied by faculty capacity building, inclusive digital access, adequate public funding, and regulatory reform.

In the long term, sustainable integration of AI in education can strengthen India’s knowledge economy, enhance employability, and contribute to innovation-led growth—provided equity and access remain central to policy design.