Personalised Learning Through AI: A Vision for the Future

VIT's Vice-President emphasizes AI's role in enhancing education and faculty development for a transformative learning experience.

Gopi

•February 13, 2026•5 mins read

AI-driven multidisciplinary education shaping the future of higher learning in India

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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.

Quick Q&A

Everything you need to know

Integration of Artificial Intelligence (AI) in higher education refers to the systematic incorporation of AI-driven tools, data analytics, and adaptive systems into teaching, learning, assessment, and institutional governance. In the context of institutions like VIT, AI is used as a common denominator across disciplines, ensuring that students not only acquire domain expertise but also develop technological fluency. This includes exposure to agentic AI, AI in solar energy, cybersecurity law, drone-based agriculture, and smart farming technologies.

Personalised learning powered by AI transforms the academic ecosystem by tailoring content, pace, and assessment according to individual student needs. Key transformations include:

Adaptive curricula that adjust to student performance
Data-driven feedback mechanisms for continuous improvement
Cross-disciplinary flexibility such as programme migration

Such systems move away from a one-size-fits-all model toward competency-based learning. This approach enhances employability, encourages innovation, and prepares students for Industry 4.0 by integrating technology with traditional academic frameworks.

Faculty training is central to ensuring that technological reforms translate into meaningful educational outcomes. As highlighted in the article, centres such as the Teaching Learning Centre of Excellence and faculty development programmes equip educators with knowledge of agentic AI and emerging technologies. Without trained faculty, even the most advanced AI infrastructure may remain underutilised.

From a governance perspective, teachers act as knowledge multipliers. Their upskilling ensures:

Effective integration of AI tools into pedagogy
Ethical and responsible use of technology
Bridging the gap between theory and application

Moreover, faculty competence fosters research innovation and interdisciplinary collaboration. In a rapidly evolving technological landscape, continuous professional development safeguards academic relevance and global competitiveness.

Multidisciplinary education allows students to transcend rigid disciplinary boundaries. For example, a mechanical engineering student studying marketing or IoT enhances both technical and managerial competencies. Adaptive curricula for excellence enable programme migration and customised learning pathways, aligning education with evolving industry needs.

Advantages:

Increased employability through diversified skill sets
Promotion of innovation at the intersection of disciplines
Better alignment with the National Education Policy (NEP) 2020 vision

Concerns: Over-flexibility may dilute core expertise if not structured carefully, and resource constraints may limit implementation across institutions.

Thus, while multidisciplinary frameworks foster creativity and entrepreneurship, they require robust academic planning, faculty preparedness, and regulatory support to maintain academic rigour.

The digital divide stems from disparities in device access, network connectivity, digital literacy, and affordability. Despite government initiatives such as free laptop schemes and reduced bandwidth costs, remote and hilly regions continue to face infrastructural challenges. This creates inequities in accessing AI-driven and online educational resources.

Institutions can mitigate these issues by:

Launching online courses targeting remote learners
Providing device and network support during crises like pandemics
Collaborating with governments to enhance rural connectivity

Additionally, expanding scholarships and increasing public funding for higher education can ensure inclusivity. Bridging the digital divide is not merely a technological issue but a matter of social justice and equitable human capital development.

Hackathons function as experiential learning platforms where students solve real-world industry problems within structured timeframes. At VIT, such initiatives are integrated into assessment systems, thereby aligning academic evaluation with innovation outcomes. Problems sourced from industry foster relevance and market orientation.

Through technology business incubators, promising ideas evolve into startups. Key benefits include:

Development of problem-solving and teamwork skills
Early exposure to entrepreneurial ecosystems
Improved job readiness through industry mentorship

This model exemplifies a shift from classroom-centric education to innovation-driven ecosystems, contributing not only to employability but also to broader socio-economic development.

The pandemic posed an unprecedented disruption to higher education. Institutions like VIT demonstrated resilience by swiftly transitioning to live online education while ensuring continuity of academic activities. Beyond mere digitisation, efforts were made to provide devices and network access to students, thereby addressing immediate accessibility concerns.

The phased return of students to campus under isolation protocols reflected a balanced approach between public health and educational continuity. Lessons learned include:

Importance of digital infrastructure readiness
Need for crisis-responsive governance mechanisms
Value of blended learning models for future resilience

This case underscores how adversity can accelerate technological adoption and institutional reform, paving the way for a more flexible and inclusive higher education system.