Higher education is standing at a pivotal crossroads. As artificial intelligence moves from the periphery to the core of how knowledge is created, delivered and validated, universities are being forced to rethink long-held assumptions about teaching, learning and assessment. The next decade will not merely digitise education; it will redefine its architecture. From personalised learning pathways and continuous assessment to ethical governance and global collaboration, intelligent systems are set to become the backbone of academic institutions.
In this exclusive interaction with ETEducation, Prof Vijaysekhar Chellaboina, Vice Chancellor of JK Lakshmipat University (JKLU), shares a deeply considered view of how AI, Machine Learning and Reinforcement Learning will transform universities — not as optional tools, but as foundational academic infrastructure shaping the university of the future.
Prof Chellaboina is a globally respected academic leader with over 28 years of experience across premier institutions in India and the United States. A distinguished mathematician and aerospace engineer, he has held senior academic and leadership roles at UPES, GITAM University, SRM University and Mahindra University, and has served on the faculty of institutions such as Georgia Institute of Technology and the University of Tennessee. With over 200 publications, multiple books and international research accolades, he brings a rare blend of academic depth and systems-level thinking to the future of higher education.Here are the edited excerpts from the interview.
How do you see AI, Machine Learning and Reinforcement Learning transforming university teaching, assessments and learning pathways over the next decade?
AI, Machine Learning and Reinforcement Learning are poised to fundamentally reshape the university landscape. At the heart of this transformation is a shift from passive, one-size-fits-all instruction to highly personalised and adaptive learning experiences. AI systems can analyse student performance in real time, allowing curricula and teaching methods to be dynamically adjusted to suit different learning paces and styles. This enables truly learner-centric education, supported by continuous, individualised feedback.
Machine Learning will also allow educators to anticipate learning challenges before they become critical, enabling timely intervention and deeper student engagement. When it comes to assessment, traditional examinations will gradually lose relevance. Instead, AI-driven platforms will support continuous and formative evaluation, tracking progress over time rather than testing memory at a single point.
Reinforcement Learning, in particular, opens up powerful possibilities by immersing students in real-world simulations that require decision-making, experimentation and problem-solving without fixed instructions. As AI increasingly takes over routine academic tasks, the role of teaching will shift decisively towards nurturing critical thinking, creativity and collaboration.
As AI tools become integral to learning, how must assessment frameworks evolve to evaluate critical thinking, judgement and creativity rather than content reproduction?
The growing presence of AI in learning environments challenges assessment systems that prioritise recall and reproduction of information. Future frameworks must place greater emphasis on higher-order cognitive abilities such as critical thinking, judgement and creativity. This will require a move away from traditional exams towards performance-based and project-driven assessments that evaluate how students synthesise knowledge and apply it in unfamiliar contexts.
AI itself can support this evolution by analysing patterns of reasoning, originality of ideas and clarity of communication. Importantly, assessment must also examine how students use AI tools responsibly — not just to arrive at answers, but to question assumptions, explore alternatives and solve complex problems independently.
At the same time, institutions must ensure fairness and transparency. Guarding against algorithmic bias and maintaining explainability in AI-assisted evaluation will be critical. In a world where answers are readily available, the true measure of learning lies in the thinking process behind those answers.
What structural and curricular shifts are essential for institutions to embed AI and ML as foundational academic infrastructure rather than optional specialisations?
Embedding AI and ML as core academic infrastructure requires a fundamental rethinking of both curriculum design and institutional systems. Curricula must move away from rigid disciplinary silos towards interdisciplinary models where AI literacy is integrated across all fields — from engineering and business to the social sciences and humanities.
On the institutional side, universities need to invest in open-access data ecosystems, high-performance computing infrastructure and scalable AI platforms that support teaching, research and experimentation across disciplines. AI is no longer a niche specialisation; it is a universal capability essential to innovation in every domain.
Creating AI-ready campuses also means building environments where students and faculty can safely experiment with these technologies, supported by access to data, computational resources and mentorship. This shift positions AI not as an add-on, but as a shared academic language.
How do you anticipate the role of educators changing as intelligent systems increasingly support teaching, mentoring and evaluation?
The educator’s role will undergo a profound shift. Faculty will move from being primary transmitters of knowledge to facilitators of learning, mentors and ethical guides. As AI handles administrative tasks such as grading and routine feedback, educators will gain the time and space to focus on deeper intellectual engagement with students.
Mentorship will become more central than ever. Students will need guidance on how to use AI tools thoughtfully, creatively and ethically. Faculty will also play a critical role in shaping institutional policies around AI governance and ensuring that technological adoption aligns with educational values.
In research, educators will push the boundaries of AI applications across disciplines, contributing to knowledge creation that addresses real societal challenges. The human role in education will not diminish — it will become more strategic and impactful.
What safeguards and governance models should universities put in place to ensure responsible, transparent and ethical use of AI in education?
Strong governance frameworks are essential. Universities must establish clear policies around data privacy, algorithmic fairness and transparency. AI systems should be regularly audited to identify and mitigate bias, and their decision-making processes must be explainable to both students and faculty.
Student data governance is particularly critical. Institutions must ensure that data used for personalised learning is secure, used responsibly and compliant with legal frameworks. Students should be informed about how their data is collected and used, and retain meaningful control over it.
Ethical considerations must also guide system design, ensuring inclusivity and preventing over-reliance on automation. AI should enhance human judgement, not replace it or create new inequities within education systems.
What will differentiate globally competitive universities in the AI era from those that struggle to keep pace?
Globally competitive universities will be defined by their ability to adapt, innovate and collaborate. Success will depend on how effectively institutions integrate AI across teaching, research and administration — not just by offering advanced courses, but by embedding intelligent systems across disciplines.
Another critical differentiator will be global collaboration. AI development is inherently international, and universities with strong global partnerships will be better positioned to lead in research, talent exchange and innovation. These collaborations enable shared resources, joint research initiatives and exposure to diverse perspectives, all of which are vital in a rapidly evolving technological landscape.
If you were to envision the university of 2035, what would be the most radical yet necessary shift in how learning is structured, delivered and validated?
The university of 2035 will be defined by flexibility and personalisation. Students will increasingly curate their own learning journeys, tailored to their interests, pace and career aspirations. Learning pathways will be modular, interdisciplinary and continuously evolving.
Assessment will be ongoing and impact-driven, focusing on a student’s ability to solve complex problems, collaborate across domains and apply AI tools creatively. Universities themselves will function as global networks rather than physical campuses alone, with virtual classrooms and international learning ecosystems.
Lifelong learning will become the norm, with alumni returning regularly to upskill and reskill as industries evolve. Ultimately, the university of 2035 will be a dynamic, ethical and globally connected institution — one that harnesses AI not just to improve efficiency, but to reimagine the very purpose of education.
