Uttar Pradesh is preparing a wider artificial intelligence framework for its universities, with AI expected to be used not only in classrooms but also in research, administration, data management and institutional decision-making.
Governor and Chancellor Anandiben Patel announced the next phase during the concluding session of AI Manthan 2.0 at Chhatrapati Shahu Ji Maharaj University in Kanpur on September 13.
The two-day summit focused on bringing AI into teaching, assessment, university governance, healthcare, agriculture and research while building safeguards around responsible use.
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Two committees to shape policy and implementation
Two bodies have been constituted to take the initiative forward: an AI Policy Advisory Committee and an AI Policy Implementation Committee.
The advisory body includes academics and specialists from engineering, agriculture, medicine and higher education, including former IIT Kharagpur director Prof Partha Pratim Chakrabarti, BITS Pilani Group Vice Chancellor Prof V Ramgopal Rao, Dr B M Prasanna, Prof Krithika Rangarajan of AIIMS Delhi and CSJMU Vice Chancellor Prof Vinay Kumar Pathak.
The implementation committee has been structured by university category.
AKTU Vice Chancellor Prof J P Pandey has been assigned responsibility for technical universities, while KGMU Vice Chancellor Dr Sonia Nityanand will oversee the medical education segment.
Responsibilities for agricultural and general universities have also been divided among senior vice chancellors, with Deen Dayal Upadhyaya Gorakhpur University Vice Chancellor Prof K P Singh serving as member-convener.
The idea is to avoid one uniform AI model for every institution.
A medical university, for example, may need AI for diagnostics, clinical research and medical education, while an agricultural university may focus on crop modelling, pest detection and climate analytics.
That sector-specific approach was already visible at AI Manthan 2.0, where separate sessions examined AI in healthcare, agriculture and general university systems.
Decades of university records could become AI datasets
One of the more ambitious proposals involves old university records.
Institutions that have existed for 50 years or more hold decades of examination records, research material, photographs, administrative documents, institutional reports and other historical material.
The plan is to digitise and structure such material so that it can become usable for research and AI-assisted analysis.
This is where the term “multimodal data” becomes important.
Multimodal AI systems can work with more than one type of information at the same time, such as written text, scanned documents, photographs, audio or video.
A university archive containing handwritten files, photographs and printed reports could therefore be converted into searchable digital collections rather than remaining locked inside physical record rooms.
That could help researchers study institutional history, educational trends, older research findings and administrative patterns far more efficiently.
But converting archival material into AI-ready data will also raise questions over privacy, copyright, consent and access control, particularly where records contain information about former students or employees.
AI will move beyond classrooms
The policy push is broader than simply allowing students to use generative AI tools.
The official summit agenda included AI-enabled teaching and assessment, intelligent university administration, academic analytics, curriculum transformation, digital campuses and research discovery.
Startups showcased systems for personalised learning, smart classrooms, AI teaching assistants and automated evaluation of handwritten answer sheets.
Other university-management applications included digital verification of marksheets and degrees.
That means AI could eventually be used for tasks ranging from student support and examination analysis to research discovery, institutional planning and verification of academic records.
The Governor has also pushed universities to prepare time-bound AI roadmaps rather than treating AI as a standalone academic subject. A report on the closing session quoted her urging institutions to integrate AI across disciplines and convert the summit’s recommendations into an action plan.
Curriculum and assessment may have to change too
University teachers and experts at the summit also warned that AI is changing how students learn.
Prof C Patwardhan of Dayalbagh Educational Institute argued that universities will have to rethink traditional teaching and evaluation methods as students increasingly learn through AI-powered and digital platforms.
Experts discussed greater use of open-book examinations, portfolios, seminars, vivas and practical demonstrations rather than relying only on conventional written tests.
The shift matters because universities can no longer treat AI only as a cheating problem.
Students are already using AI for research, coding, summarisation and learning.
The policy challenge is therefore to define where AI assistance is permitted, where human work must remain independently assessable and how institutions can detect misuse without banning legitimate technology.
Data governance may become the hardest part
Large-scale AI adoption in universities will depend heavily on how student and institutional data is handled.
Universities hold marksheets, addresses, identity information, research datasets, medical records in some institutions and decades of internal documents.
Using that material in AI systems will require clear rules on who can access it, where it is stored, whether it can be used to train external models and how sensitive information is removed.
The AI Manthan agenda itself included responsible, ethical and secure AI as a core focus area.
That may ultimately be as important as the technology.
A university can gain efficiency from AI only if students, teachers and researchers trust that their data will not be exposed or reused without safeguards.
What this means for you: Students and teachers in Uttar Pradesh should expect AI to become more formally integrated into university work, rather than remaining an optional outside tool. The biggest changes are likely to appear in teaching, research, assessment, digital records and administrative processes.
The420 Insight: The most significant part of Uttar Pradesh’s plan is not simply putting AI into classrooms. It is the attempt to convert decades of university knowledge and records into usable digital intelligence. If done responsibly, that could create a major research resource. If privacy and governance are weak, the same archive could become a new data-risk problem.
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