Uttarakhand Transformation Dialogues 2026: AI-led strategies to accelerate infrastructure and education
The Times of India's forum in Dehradun put one message front and center: AI is now a practical lever for Uttarakhand's next phase of growth - especially in infrastructure and education. For educators, the takeaway is clear. Align curriculum, data, and partnerships so students build solutions the state needs right now.
Who was in the room - and why it matters
The panel brought together Mayank, Founder-CEO & Managing Director, ADROSONIC; Shikhar Saxena, Additional Director, Department of Industries, Government of Uttarakhand; and Prof. Dr. Sachin Ghai of Graphic Era University. The discussion focused on moving past terrain-bound limits to sector-driven progress through technology, talent, and collaboration.
The five layers of AI you can apply in education
- Infrastructure: Chips, GPUs, and computing power (on-prem or cloud).
- Data quality: Clean, consented data with clear structure and access rules.
- Models: The "brain" of AI (where players like OpenAI operate).
- Orchestration: Turning one query into coordinated actions across tools.
- Applications: Interfaces where teachers, students, and admins interact.
"If your data quality is not good, you can't have AI on top of it. If we feed it wrong data, AI will always give a wrong response," Mayank said. Build your data foundation first; the rest stacks cleanly on top.
Talent, hubs, and meaningful application
Uttarakhand can compete globally by nurturing local talent and building smaller innovation hubs. "We have an enormous amount of talent. If we nurture this talent and build small hubs, we can provide services anywhere in the world using the power of AI - and that itself will fuel the growth of the entire state," Mayank noted.
He urged institutions and government to share problems openly so universities can research and companies can deliver workable solutions. His reminder is useful for campuses too: "No matter how cool a technology is, until you find how it's going to be applied, it's of no use." He added, "Technology is for the business, business is not for the technology."
Government momentum - and a window for education
Shikhar Saxena acknowledged that public systems often adopt late: "Whenever some new technology comes, government is usually the last to use it." That said, he pointed out how processes that were offline in 2015 are now digitised - and AI should move faster this time with the Uttarakhand AI Mission launched on November 26 to take a "first-mover advantage."
He called out a practical use case: "Understaffing is a chronic problem in almost all government departments. AI can help solve this problem, and that's why we are focusing a lot on it." For educators, this opens doors for internships, pilot projects, and applied research that reduce manual load across departments.
Education as the long-term lever
Prof. Dr. Sachin Ghai spotlighted curriculum change and institutional mindset. Uttarakhand has seen students move out for higher education; building quality programs at home is key to reverse that flow.
"We recognised the importance of participation of all stakeholders," he said, adding that education should "always enlighten and empower." Investment in roads and labs must match investment in people and partnerships - academia, industry, and government working as one system.
What this means for educators and administrators
- Curriculum refresh: Integrate AI literacy across disciplines, with hands-on projects tied to local needs (roads, disaster response, healthcare, tourism, MSMEs). Teach prompt skills, retrieval with trusted sources, data basics, and ethics.
- Data foundations: Clean student and institutional datasets; define consent and anonymisation; document metadata. No clean data, no reliable AI.
- Faculty development: Run ongoing micro-workshops. Appoint AI leads per department. A practical starting point: AI Learning Path for Teachers.
- Student pathways: Co-create capstones with state departments and industry. Offer credits for applied AI work that ships (dashboards, assistants, models).
- Institutional ops: Pilot AI for admissions triage, timetabling, procurement, and student support. Keep a human in the loop and document decisions.
- Community hubs: Build small, focused labs with district administrations and local businesses. Host monthly problem clinics; invite alumni and diaspora as remote mentors.
- Infrastructure strategy: Use shared GPU labs and cloud credits; prefer open-source where viable to control costs and customize.
- Ethics and safety: Adopt clear guidelines on fairness, bias testing, data minimisation, and accessibility. See UNESCO's guidance on AI in education for policy anchors: UNESCO AI guidance for education.
- Quality assurance: Track learning outcomes, placement rates in AI-adjacent roles, time saved in admin, and the number of live deployments with public bodies.
Quick wins in the next 90 days
- Audit current tools and data flows; fix one data-quality issue per unit.
- Spin up two micro-pilots: an AI teaching assistant in a large-enrolment course, and an admin copilot for timetabling or procurement FAQs.
- Run a faculty workshop on prompt skills, citation discipline, bias checks, and student assessment redesign.
- Launch a 4-week student module on AI fundamentals with a local problem brief from a district office or UIIDB.
- Form a small AI governance group (academics, IT, legal, student rep) to approve pilots and set guardrails.
- Publish a call for problems to government departments and MSMEs; select three for student projects.
The bottom line
Infrastructure, data, and human capital move together. Use AI where it removes friction, builds student capability, and delivers public value. Let technology serve learning and outcomes - and let your students build what the state uses.
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