By- Amlan Baisya
Assistant Professor , Department of Literature and Languages,
Easwari School of Liberal Arts, SRM University AP
It begins, as many educational shifts do, almost imperceptibly.
A student submits an essay—lucid, well-structured, and stylistically mature. The argument is coherent, the transitions seamless, the conclusion persuasive. Yet, when asked to elaborate on a particular claim, the student falters. There is a pause, followed by a disarmingly candid admission: “I used AI to help me think (and obviously write too).”
The situation is striking not because it is unusual, but because it is increasingly common. It signals a subtle yet profound transformation in the ecology of learning. Artificial intelligence is no longer merely a supplementary tool; it is becoming a cognitive intermediary. The question that emerges from this shift is not whether students use AI—they evidently do—but whether, in the process, the locus of thinking itself is being gradually displaced and, thereby, replaced.
The integration of AI into education has been swift and expansive, particularly in India’s rapidly evolving higher education landscape. Tools such as ChatGPT and a range of generative platforms now assist in drafting essays, summarising texts, solving problems, and even generating research ideas.
This technological momentum aligns closely with the vision articulated in the National Education Policy 2020, which advocates the integration of emerging technologies to enhance accessibility, personalisation, and pedagogic innovation. Policy discourse and institutional practice appear, at least superficially, to be in synchrony. There is, undeniably, much to applaud in this transformation. AI-enabled platforms have begun to democratise access to educational resources, particularly in contexts marked by uneven teacher availability and infrastructural constraints. Adaptive learning systems can personalise instruction, respond to individual learning trajectories, and provide immediate feedback—capabilities that traditional classrooms have long struggled to achieve at scale. Yet, this narrative of technological empowerment is accompanied by a less examined, and arguably more consequential, development. As AI systems become more proficient at generating outputs—essays, solutions, summaries—the intellectual labour traditionally associated with these outputs risks being rendered invisible, or worse, unnecessary. The process of thinking, which education traditionally seeks to cultivate, is increasingly mediated by systems designed to optimise efficiency.
Emerging research lends credence to this concern. Surveys of student behaviour in AI-enabled learning environments indicate a growing perception that such tools make academic tasks “easier,” often at the cost of deeper cognitive engagement. Reports in international media, including The Guardian, have highlighted students’ own anxieties that reliance on AI may erode their capacity for independent study and critical thinking. These concerns are not merely anecdotal; they point to a structural reconfiguration of the relationship between effort and understanding. When answers are instantly available, the necessity of grappling with complexity diminishes.
This tension is not new. Educational theorists have long distinguished between performance and learning, between the ability to produce correct answers and the capacity to understand underlying principles. What is novel in the current moment is the scale and sophistication at which performance can now be simulated. AI systems can generate text that approximates human reasoning, creating the impression of understanding without the corresponding cognitive process.
Hannah Arendt once warned the world of “thoughtlessness” as a condition in which individuals cease to engage critically with their actions and surroundings. While her reflections were situated in a political context, their relevance to contemporary Indian education system is hard to ignore. If thinking can be outsourced—delegated to algorithms that operate with speed and authority—then, I believe, the very purpose of education demands reconsideration.
At this juncture, it would be reductive to cast AI as an adversary of human cognition. Such a position overlooks the genuinely transformative possibilities that these technologies offer. In many Indian classrooms, particularly those grappling with large student populations and limited resources, AI can function as an invaluable pedagogic aid. It can provide supplementary explanations, facilitate multilingual learning, and offer students access to knowledge systems that might otherwise remain inaccessible. To dismiss these advantages would be both impractical and undesirable.
The problem, therefore, is not the presence of AI, but the conditions under which it is integrated. In the Indian higher education system, these conditions are shaped by a complex interplay of policy imperatives, institutional structures, and evaluative metrics. Universities operate within a framework that increasingly emphasises measurable outputs—grades, publications, rankings. In such an environment, efficiency becomes a dominant value. AI, with its capacity to accelerate production, aligns seamlessly with this logic.
However, this alignment produces a paradox. While policies such as the National Education Policy 2020 advocate holistic and critical learning, the metrics through which institutions are evaluated often prioritise quantifiable outcomes over cognitive processes. Global ranking systems, despite their gradual recognition of interdisciplinary and innovative practices, continue to privilege output- oriented indicators. The result is a pedagogic environment in which the use of AI is tacitly encouraged for its efficiency, even as its implications for learning remain insufficiently interrogated.
What, therefore, is at stake is not merely academic integrity or pedagogic effectiveness, but the cultivation of intellectual agency. To think is not simply to arrive at answers; it is to engage in what John Dewey described as “active, persistent, and careful consideration.” It involves uncertainty, deliberation, and the willingness to inhabit ambiguity. These are precisely the dimensions of cognition that are most vulnerable to erosion in an environment oriented towards speed and optimisation.
If higher education is to retain its intellectual integrity in the age of AI, a fundamental recalibration is necessary. This recalibration must begin with a shift in pedagogic emphasis—from the production of answers to the interrogation of processes. Assignments must be designed in ways that foreground interpretation, contextualisation, and critical reflection—capacities that cannot be easily automated. Assessment practices must move beyond polished outputs to include demonstrations of understanding, such as oral examinations, iterative drafts, and reflective engagements with sources.
Equally important is the integration of AI literacy as a core educational objective. Students must be equipped not only to use AI, but to question it—to recognise its affordances and its limitations, to understand the data structures and biases that underpin its outputs, and to situate its responses within broader epistemic frameworks. Such an approach transforms AI from a surrogate thinker into an object of critical inquiry.
Ultimately, the question is not whether AI will become part of education—it already has. The question is whether education will adapt in ways that preserve and enhance the human capacity for thought.
The classroom moment with which this article began offers a point of departure. When a student says, “I used AI to help me think,” the appropriate response is neither prohibition nor uncritical acceptance. It is a more demanding question: What part of the thinking did you do?
That question reasserts the centrality of human cognition. It reminds us that while algorithms may assist, accelerate, and augment, they cannot substitute the intellectual labour that constitutes genuine learning.
India HE ecosystem stands at a critical juncture, where technological ambition and educational reform converge. The promise of the National Education Policy 2020 is expansive, envisioning an education system that is flexible, inclusive, and future-ready. Artificial intelligence can undoubtedly contribute to this vision. But if its integration leads to a quiet abdication of thinking, then the cost may outweigh the benefits.
The danger is not that machines will think for us.
It is that we may gradually forget what it means to think at all.
