By Dr. D.C. Kiran, Associate Professor, School of Engineering and Technology, Vidyashilp University
The past decade has seen a notable shift in how students, particularly those outside quantitative or computational disciplines, engage with analytical reasoning. Learners from psychology, media studies, design, and the humanities now routinely interact with datasets, elementary models, and systems-level representations. This trend is not merely anecdotal; several studies of student engagement patterns in OECD and G20 contexts suggest that early exposure to interactive digital tools increases the likelihood of cross-disciplinary exploration, even in non-technical domains. A psychology learner interpreting economic indicators or a journalism student mapping narrative structures through automated text analysis is, therefore, no longer atypical. It reflects a reconfiguration in the accessibility of analytical tasks.
This shift does not imply that disciplinary boundaries have dissolved. Rather, the proliferation of AI-assisted learning platforms has altered both the threshold and sequencing of learning. These platforms allow students to visualise data, manipulate simple models, and receive graduated feedback before committing to formal methodological training. The National Education Policy (NEP) 2020 envisages a system that supports multidisciplinary exploration, learner choice, and flexible progression. AI technologies provide an enabling layer for these priorities by allowing students to move into unfamiliar domains without the cognitive or procedural burden that earlier deterred such movement.
AI-enabled learning also challenges the conventional temporal structure of higher education. Historically, students were required to adapt themselves to institutional rhythms—linear timetables, standardised pacing, and fixed assessment cycles. AI reverses this dynamic by permitting instruction to respond to learner variability. Evidence from adaptive learning studies indicates that self-paced review, the ability to pause or revisit concepts, and opportunities for non-linear exploration significantly influence conceptual retention, particularly in complex or abstract domains. For students in law, journalism, design, or public policy, this flexibility allows them to engage with doctrinal evolution, narrative shifts, or iterative prototyping without being constrained by classroom tempo.
AI’s relevance is not limited to students; it has implications for academic labour and governance. A substantial proportion of faculty time across Indian institutions is spent on repetitive tasks—constructing objective assessments, grading routine submissions, and developing foundational learning materials. Empirical analyses from universities that have adopted AI-supported assessment indicate that automating these tasks frees faculty for higher-order pedagogical work: conceptual clarification, critical debate, and sustained mentorship. A seminar that begins with an AI-generated dataset but culminates in a discussion on model assumptions, data provenance, and ethical implications exemplifies enriched pedagogy rather than mechanised instruction.
The Academic Bank of Credits (ABC), a structural reform central to NEP 2020, is strengthened by such technologies. When learners can accumulate credits across institutions and disciplines, they require guidance in understanding how disparate choices align with academic or professional trajectories. AI-enabled advising systems can assist by modelling how combinations—for instance, psychology, policy analysis, and computational methods—map onto domains such as behavioural economics, digital governance, or public-system design. This transforms interdisciplinarity from an incidental outcome into a planned developmental pathway.
AI also supports new forms of academic collaboration. Cloud-based research environments, shared repositories, and virtual laboratories allow students and faculty to participate in projects that extend beyond institutional and geographical boundaries. Instances of students in Bengaluru analysing climate models produced by European research consortia, or engaging with multilingual media datasets through AI-supported tools, demonstrate how global academic ecosystems can be accessed without diminishing local relevance. Such collaborations shape the dispositions required for research in the contemporary world: contextual sensitivity paired with global awareness.
However, the widespread adoption of AI requires regulatory and ethical vigilance. Concerns relating to algorithmic bias, opaque data pipelines, privacy, and automated decision-making have been well-documented in policy literature. NEP 2020’s insistence on ethical reasoning, constitutional values, and critical inquiry assumes heightened significance. In many classrooms, discussions now focus not only on how to use AI tools but on whether a given tool’s assumptions, training data, or inferential patterns are defensible. Such critical interrogation is essential if AI is to serve as an instrument of public good rather than an unexamined technological layer.
When integrated responsibly, AI-driven learning platforms can strengthen NEP 2020’s central objectives: flexibility, learner agency, interdisciplinary exploration, and reflective engagement with knowledge. Yet, technology cannot displace the human dimensions of education. Intellectual curiosity, ethical judgment, empathy, and a commitment to societal improvement remain the core of the higher-education mission.
As technological systems evolve more rapidly than curricular structures, institutions will need to design environments that support exploration, analytical confidence, and critical maturity. AI can facilitate this transition, but the responsibility for shaping thoughtful and responsible learners continues to rest with educators and academic leadership.