By Upasana MahantaAI has become an undeniable reality in higher education in India, reshaping how students engage in the classroom, faculty redesign curricula and pedagogies, research is conducted, and assessments are planned. Universities are experimenting with automating routine administrative tasks, AI-enabled chatbots, and digital learning platforms. The conversation on future-ready universities, therefore, has largely been centred on AI adoption, scaling technological innovations, and developing policies on permitted tools and preventing misuse. However, to be future-ready, universities must think beyond AI adoption alone. Future-ready does not necessarily mean technology-ready, although technology does inform a critical part of this readiness.From adoption to capabilityA future-ready university must shift its focus from AI adoption to strengthening AI capabilities. The question is whether our students, faculty, and staff can use AI intelligently, critically, and ethically. Success in embedding AI in education cannot be measured simply by the percentage of administrative tasks being automated, or the number of faculty and students using AI enabled platforms. The real questions are whether AI has improved learning outcomes, enabled curricular and pedagogical innovation, or freed time and resources for greater institutional effectiveness. This requires training students, faculty, and staff on AI’s limitations and possibilities, hallucination and reliability, bias and embedded assumptions, source verification, privacy and intellectual property, and sustainable use. UNESCO’s AI Competency Framework for students and teachers offer useful guidance to countries on developing the knowledge, skills, and values necessary to engage with AI, while emphasising a human-centred approach. A move from adoption to capability, therefore, requires putting people before platforms. As universities invest in digital infrastructure, this must not be at the cost of the fundamentals: faculty development and empowerment, student skills rooted in interdisciplinary knowledge that remain valuable as jobs change, and an understanding of employability that extends beyond AI literacy.Responsible AI governanceA future-ready university needs an operational framework of policies, processes, principles, and accountability mechanisms that ensure AI is used with fairness, transparency, and clarity across disciplines, departments, and functions. Responsible AI governance is not about prohibition; it is about empowering all stakeholders to move with clarity and confidence. This means governing risks rather than restricting technology itself. For example, AI used to brainstorm ideas for a class presentation does not carry the same risks as AI used in admissions, grading, or financial-aid decisions. As risks increase, so should human oversight, transparency, and accountability. In such cases, responsible AI governance must include meaningful human review, the ability to challenge decisions, documentation of how AI was used, and clear lines of institutional responsibility. Sensitive data such as student records, unpublished manuscripts, and personal information of faculty and staff raise privacy, confidentiality, and intellectual property concerns. Therefore, institutions need clear data governance and privacy policies specifying approved platforms, cybersecurity safeguards, and rules on what information cannot be uploaded or shared to external systems. The EU AI Act, the world’s first comprehensive legal framework on AI, adopts a risk-based approach. India unveiled the AI Governance Guidelines under the India AI mission in 2025 supporting safe, inclusive, and accountable AI development and deployment. While India’s approach so far has been more principle-based, sectoral, and adaptive, establishment of the AI Governance and Economic Group in 2026 signals an evolving governance architecture.Universities must resist turning AI governance into surveillance systems, responding primarily through detection software, proctoring, policing, and suspicion. An example of emerging institutional approaches in India is the IIT Delhi’s initiative to develop guidelines for the responsible us of generative AI, incorporating student and faculty perspectives. As AI technologies change faster than university regulations, responsible AI governance also demands stakeholder consultations in policy making, periodic feedback, AI training, and ethics review of high-risk applications.Stakeholders as co-designersAcademic transformation is unlikely to succeed when it is exclusively top-down. Meaningful participation of all stakeholders – faculty, students, staff, and institutional leadership – is crucial to ensuring that pockets of innovation are translated into institutional capabilities. The focus should not be on scaling a particular product – a chat bot in admissions or a platform for readings and feedback – but on identifying whether AI can address genuine institutional needs and enable successful practices to spread. A future-ready university cannot rely on static five-year strategic plans while technology evolves continuously. University of Sydney, for instance, recently adopted a forward-looking ‘two-lane approach’ to assessments designed to respond to student learning and academic integrity: ‘secure’ assessments that demonstrate learning without external assistance, and ‘open’ assessments that permit relevant tools including AI. The University’s AI in Education Working Group, comprising students and staff, co-designed an online guide to AI in education that is publicly available.When stakeholders become co-designers, the questions we ask about AI integration shift from scale to outcomes – has learning improved? has access become more equitable? or is feedback more meaningful? A bottom-up approach that is agile, collaborative, evidence-based, and adaptable will truly determine whether our universities are future-ready.Equitable accessFuture-readiness must also address equitable access. The digital divide is no longer only about access to the internet and smartphones, but also about access to AI technologies and the ability to use them effectively. Expanding university’s digital infrastructures must expand access and opportunities rather than amplify existing inequalities. Investing in multilingual translation tools, voice-based interfaces, personalised learning and accessibility tools can meaningfully empower socially and economically marginalised students, or those disadvantaged by language, geography, or disability. The BHASHINI initiative developed by the Ministry of Electronics and Information Technology, Government of India, demonstrates this potential through AI-powered multilingual and voice-based services designed to bridge language and digital divides.Equity must remain central to any conversation on future-ready universities. An important case in point is the University of Michigan creating an institutional infrastructure including generative AI tools such as U-M GPT and U-M Maizey –available for free to the campus community with explicit emphasis on privacy, accessibility, and equity.AI may transform how universities teach, research, and function, but it is the thoughtful integration of technology into educational and institutional purposes that will truly define a future-ready university. The goal is not to be simply AI-driven, but to be an institution of higher learning equipped to navigate an AI world with its commitment to fairness, equity, access, and knowledge production firm and intact.Upasana Mahanta is a faculty member and a Dean at OP Jindal Global University, Sonipat, Haryana.DISCLAIMER: The views expressed are solely of the author and ETEDUCATION does not necessarily subscribe to it. ETEDUCATION will not be responsible for any damage caused to any person or organisation directly or indirectly.
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