By Dr Arun Sharma
A student today can generate a polished essay, summarize a research paper, prepare interview responses, and solve coding problems within minutes using AI tools. The real question for universities is no longer whether AI can be controlled. AI is already becoming embedded in academic life. The deeper question is what happens to learning when knowledge can now be instantly generated and presented in polished form by AI systems.
The question is important because higher education has encountered technological disruption before. Search engines, digital libraries, and online learning platforms had already made information widely accessible long before generative AI emerged. Students could retrieve explanations, articles, lecture videos, and solutions online for nearly two decades. But generative AI represents something fundamentally different.
The internet helped students locate information. AI increasingly produces responses that resemble understanding itself. It can generate essays, summaries, and arguments in coherent human-like language. The challenge is therefore no longer access to information, but the possibility of bypassing the cognitive processes through which understanding develops.
The cognitive neuroscientist Stanislas Dehaene, in his influential book How We Learn, argues that durable learning requires attention, active engagement, error correction, and sustained mental effort. Dehaene’s work is particularly relevant in the AI era because it reminds us that understanding is built through effort. Cognitive depth develops not when answers are instantly available, but when learners struggle with ambiguity, make mistakes, and refine their thinking over time.
Outsourcing intellectual effort: What is happening on college campuses
Across campuses today, student and faculty behaviour is already changing in visible ways. Students increasingly use AI tools not only for writing assistance, but also for brainstorming, coding support, summarisation, interview preparation, and reflective writing. Faculty members are redesigning assignments, experimenting with AI-assisted teaching tools, and reconsidering conventional evaluation systems.
The attraction of AI tools is understandable. AI can improve accessibility, support personalised learning, reduce repetitive academic tasks, and help students engage with content more efficiently. For a country like India, with one of the world’s largest and most diverse higher education ecosystems, AI-assisted learning tools may significantly expand educational access and support students from diverse linguistic and academic backgrounds.
However, the risks are equally significant. If every difficulty is instantly resolved through AI-generated responses, students may gradually lose the patience required for deep intellectual engagement. Academic misconduct is not the central issue here. The larger issue is whether students may increasingly consume polished outputs without developing the habits of thinking through which genuine understanding emerges.
AI differs fundamentally from earlier internet technologies. In the era of search engines, students were still expected to compare sources, filter information, synthesise ideas, and structure arguments. AI increasingly performs many of these cognitive tasks itself. The danger, therefore, is not merely misinformation or plagiarism but the gradual outsourcing of intellectual effort.
AI is changing How universities teach and evaluate
Universities across the world are rethinking both teaching methodologies and assessment systems. Large parts of the Indian education system still operate around information transmission and reproduction. Faculty members deliver knowledge, students absorb content, and examinations test retention and structured articulation.
Faculty members across disciplines are noticing subtle shifts in student submissions. Assignments and written analyses are increasingly polished and structurally coherent. However, classroom discussions do not always demonstrate the same depth of understanding. This has prompted many educators to rethink how learning is evaluated.
Many faculty members now recognize that assignments designed primarily around information reproduction no longer work in the same way. This is pushing classrooms toward discussion-led learning, applied problem-solving, and evaluation methods that require students to explain how they arrived at an answer rather than merely submit one.
Paradoxically, AI may force universities to confront pedagogical weaknesses that educators have long discussed. For years, educators have criticised the excessive reliance on rote memorization and formulaic evaluation systems, particularly in the Indian education system. AI is exposing these weaknesses more sharply than earlier technologies did. Institutions may now be compelled to move toward deeper, inquiry-driven, and experiential learning models. The transition will also require significant investment in faculty development.
What makes graduates employable in the AI era
The impact of AI on employability is also more nuanced than simplistic narratives around job replacement suggest. As AI increasingly automates routine analytical tasks, employability may depend less on the ability to produce information and more on the ability to interpret context, exercise judgment, and ask meaningful questions.
Technical competence will remain essential, but it may no longer be sufficient on its own. Rather, professional success will increasingly depend on the ability to work alongside intelligent systems while exercising independent judgment. AI fluency is therefore becoming essential across disciplines, not only in engineering or computer science, but also in management, law, and other social science streams.
For India, this transition presents both opportunity and urgency. India possesses one of the world’s youngest populations and one of its largest education systems. Yet curriculum revision cycles, faculty preparedness, and institutional adaptability often move more slowly than technological change itself. Bridging this gap will require universities to redesign their curricula more dynamically, strengthen industry engagement, and invest significantly in faculty development.
Faster research and harder questions
Research may become one of the areas most transformed by AI because academic work depends heavily on synthesis, interpretation, and articulation. These are the areas where AI increasingly performs well. In an academic environment already shaped by publication pressure and increasing research expectations, AI also raises difficult questions about authorship, originality, and the meaning of scholarship itself.
Researchers increasingly use AI tools for literature reviews, language refinement, and data analysis. These applications significantly improve efficiency, especially for repetitive academic tasks. Universities will need clear ethical frameworks governing the responsible use of AI in academic work. The larger challenge, however, is not only detecting misuse but preserving the intellectual discipline through which original scholarship emerges.
In conclusion, the institutions that succeed will not be the fastest adopters of technology, but those that integrate AI while defending the cognitive effort that real learning requires. AI makes understanding appear effortless. In such a world, the responsibility of universities becomes even more important to ensure that students continue to develop the habits of thinking through which meaningful learning occurs.
Dr Arun Sharma is the Director of NMIMS Navi Mumbai Campus.
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.
