Indian universities lead Asia in research output per faculty, but all six of the country’s highest-ranked institutions have slipped in the QS Asia University Rankings 2027, with IIT Delhi falling to 66th.

All six of India’s highest-ranked universities have dropped in the QS World University Rankings: Asia 2027, released on Tuesday, even as Indian institutions recorded the strongest research output per academic in the region.

Top Six Slip in QS Asia University Rankings 2027

IIT Delhi stays at the head of the Indian list at 66th, down from 59th. IISc Bangalore is 79th (64th last year), IIT Bombay 83rd (71st), IIT Madras 85th (70th), IIT Kharagpur 90th (77th) and IIT Kanpur 97th (77th). The University of Delhi fell from 95th to 106th and left the top 100, which now holds six Indian institutions.

The declines came in a larger, more crowded table. QS Quacquarelli Symonds ranked 1,722 universities from 26 higher education systems this year, including 195 new entrants. Of India’s 325 ranked universities, 71 per cent fell, 18 per cent rose and 11 per cent held their position. Japan saw 85 per cent of its universities fall, while Taiwan and Indonesia saw 70 per cent each.

India has the second-largest number of ranked universities in Asia after Mainland China, which has 448. It added 34 this year. In 2016, only 24 Indian institutions were ranked.

Research output stands out

India has more universities in the regional top 10 for papers per faculty member than any other system. IISc Bangalore ranks first on the indicator and IIT Madras third. Twenty-seven Indian universities are in the top 50, up from 10 in 2018.

Doctoral qualifications show a similar pattern. Fifteen Indian universities share first place for the proportion of staff with a PhD, more than double the number from Iran, the next-best system. India also holds 47 of the top 100 places on that indicator.

“India has built remarkable research capacity,” said QS Senior Vice President Ben Sowter. “The challenge now is to translate this strength more consistently into research impact and international recognition.”

Those two indicators carry 5 per cent of the overall score each. The two reputation surveys carry far more: 30 per cent for academic reputation and 20 per cent for employer reputation. QS data show that 76 per cent of Indian universities lost ground on academic reputation and 83 per cent on employer reputation.

Citations and international links lag

Output has not translated into citations at the same rate. Only three Indian universities are in Asia’s top 50 for citations per paper, and one is in the top 10. India also sits below the regional average on international faculty, international students and student exchange, QS said, though the effect differs from one university to another.

Employer reputation is a relative strength. IIT Bombay is seventh in Asia on the indicator and IIT Delhi 10th, the first Indian universities to reach the top 10. Six Indian institutions are in the top 50, up from five. QS noted that this recognition is concentrated in a small group of institutions, in a country where about 45 million students were enrolled in higher education in 2023-24.

Climbers

Seven Indian universities in the regional top 200 moved up: Chandigarh University, Jawaharlal Nehru University, Lovely Professional University, Symbiosis International, Amity University, Jamia Millia Islamia and Manipal Academy of Higher Education.

Chandigarh University reached its best rank, 104th, on gains in international research, citations and papers per faculty. Manipal entered the top 200 at 195th. Further down the table, GLA University rose nearly 300 places to 491st, Jain University climbed 221 places to 374th and Woxsen University gained 195 places to reach 499th.

The wider region

The University of Hong Kong is first for a second year, followed by Peking University. Tsinghua University rose from ninth to third. The National University of Singapore fell from third to sixth and Nanyang Technological University from third to eighth.

QS pointed to India’s policy framework for the next stage. The National Education Policy 2020 placed research at the centre of reform, and the Anusandhan National Research Foundation, set up in 2024, backs efforts to widen research capacity. QS says India has built its output on national R&D spending of around 0.64 per cent of GDP.

“Deeper international research partnerships, more international faculty and students, and closer links with industry can help ensure that research produced on this scale achieves greater impact,” said Ashwin Fernandes, QS Vice President for Strategic and International Engagement.

He added: “With foreign universities opening campuses in India and Indian institutions expanding overseas, the country has an opportunity to become a major global centre for education and research.”

The youth exchange programme seeks to strengthen national integration and highlight the region’s development potential. Its longer-term value will depend on whether cultural encounters translate into lasting connections and more equitable opportunities.

The Indian government is encouraging students from across the country to experience the Northeast firsthand, using cultural exchange to build connections between regions with distinct histories, languages and social experiences. The initiative, Ashtalakshmi Darshan, also reflects a wider ambition to place the eight northeastern states more prominently in India’s economic development and engagement with Southeast Asia.

The programme came into focus when Union Minister for Communications and Development of North Eastern Region Jyotiraditya M. Scindia interacted with students at Mizoram University during his two-day visit to the state. The group included students from Goa and Chandigarh visiting the region under the initiative.

According to the Ministry of Development of North Eastern Region (MDoNER), more than 590 students had participated in the programme over the preceding year. The ministry says the exchanges are intended to familiarise young people with the Northeast’s cultural heritage and development opportunities while strengthening mutual understanding and national integration.

Why the Northeast is called Ashtalakshmi

The name Ashtalakshmi draws on the Hindu cultural association of Lakshmi with prosperity and abundance. In government messaging, it represents the eight northeastern states as contributors to India’s prosperity and future development.

Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim and Tripura have distinct cultural traditions, languages, ecological landscapes and political histories. The Ashtalakshmi framing brings these states together under a shared narrative of cultural richness and economic potential.

The programme gives students from other parts of India an opportunity to encounter this diversity directly. Visits to universities and local communities can introduce participants to regional histories, social practices and development concerns that may receive limited attention in their own educational environments.

Cultural exchange as National Policy

Ashtalakshmi Darshan is a youth exchange initiative of MDoNER and the North Eastern Council (NEC), aligned with the government’s Ek Bharat Shreshtha Bharat initiative. Launched in November 2025, it was designed to give students immersive educational and cultural experiences in the Northeast.

The programme’s initial design envisaged 1,280 students from 28 states and Union Territories participating in 40 batches, with visits covering all eight northeastern states. The ministry subsequently reported 530 students participating across 15 batches during 2025–26. Its October 2026 announcement put participation over the preceding year at more than 590.

The policy premise is that direct interaction can build familiarity across regional and cultural differences. Students who meet peers from other states can exchange perspectives and establish relationships that extend beyond the visit. However, participation figures establish reach, not impact. The publicly reported information does not show whether students’ attitudes changed or whether the connections endured after they returned home.

Northeast in India’s Development Strategy

The programme’s cultural objectives sit alongside a broader economic and strategic agenda. The Northeast shares international borders with Bangladesh, Bhutan, China, Myanmar and Nepal, making it important to India’s relations with neighbouring countries and its engagement with Southeast Asia.

India’s Act East Policy, announced in 2014, seeks to deepen economic cooperation, cultural ties and strategic relationships across the Asia-Pacific. The government regards the Northeast as an important link in this engagement, with transport infrastructure, border trade and regional connectivity central to its plans.

The India–Myanmar–Thailand Trilateral Highway and the Kaladan Multi-Modal Transit Transport Project illustrate efforts to improve connections between India’s northeastern region and neighbouring countries. Better connectivity could expand access to markets and support trade and tourism, although the benefits depend on project implementation and the opportunities available to local businesses and workers.

The ministry reported that the length of national highways in the Northeast increased from 10,905 km in 2014 to 16,207 km by March 2025. This expansion reflects the scale of the infrastructure agenda, but road construction alone cannot establish whether economic gains are reaching communities equitably.

Development Commitments Under Scrutiny

The region’s development ambitions must be measured against persistent constraints, including difficult terrain, high logistics costs and gaps in transport and industrial infrastructure. Infrastructure expansion has improved connectivity, but implementation, access to markets and the distribution of economic benefits remain important considerations.

Budget figures also reveal the challenges of translating commitments into delivery. In its examination of the Ministry of Development of North Eastern Region’s demands for grants, a parliamentary Standing Committee noted that the ministry’s revised estimate for 2025–26 fell to ₹4,479.20 crore from the original budget estimate of ₹5,915 crore. The allocation for 2026–27 subsequently rose to ₹6,812.30 crore. The committee raised concerns about fluctuations in allocations and revisions, warning of potential consequences for ongoing projects and calling for better financial planning and utilisation.

These figures do not establish that particular projects have failed. They do underline why allocations and announcements must be assessed alongside implementation and outcomes. The government’s ambition to make the Northeast a contributor to Viksit Bharat 2047 will depend on whether investment translates into reliable public services, employment, support for local enterprise and opportunities shaped by the priorities of the region’s residents.

Can Cultural Exchange Move Ahead Symbolism?

Ashtalakshmi Darshan addresses a narrower but important challenge: limited firsthand familiarity with the Northeast can leave people elsewhere dependent on secondhand accounts of its communities and cultures. Direct interaction offers an opportunity to develop a more informed understanding of the region.

Yet presenting eight states as a collective source of cultural richness and economic opportunity can obscure their distinct identities, political histories and development priorities. A visit centred on landscapes, festivals and hospitality may introduce students to the region without exposing them to the questions shaping everyday life, from employment and education to environmental pressures and local governance.

The programme’s public reporting also leaves questions about reciprocity and evaluation. It does not establish how extensively students from northeastern states help design the exchanges, whether comparable opportunities exist for them to visit other parts of India, or whether the ministry tracks outcomes after participants return home.

These are not peripheral details. Host communities should have an active role in determining how their region is represented, while local students should be participants in the exchange rather than simply its guides or subjects. Published evaluations of changes in participants’ knowledge, alongside evidence of continuing academic and community partnerships, would help establish whether the programme is producing lasting engagement.

Test After Visit

Ashtalakshmi Darshan gives the government a means of building people-to-people connections alongside its wider development and connectivity agenda. Its value will depend on the quality of the exchanges and whether they lead to sustained relationships and a deeper understanding of the Northeast’s diversity.

The more consequential test lies in the relationship between the government’s language of prosperity and the experiences of the people who live in the region. Cultural visits cannot compensate for delayed projects, unequal access to economic opportunities or development decisions that fail to reflect local priorities.

The Northeast cannot be meaningfully integrated into India’s development story merely by inviting the rest of the country to see it. Its people must have an equal role in defining that story—and a demonstrable share in the prosperity it promises.

Every learner knows the moment when a problem stops being easy. The first attempt does not work, the next step is unclear, and a small discomfort sets in. For generations, that discomfort was where thinking began. Today, a chatbot increasingly fills that space.

As a career architect, I keep researching what students aspire to, how aware they are of their own paths, and where their motivation comes from. Between July and September 2025, I worked with 42 high school students, and about four in five of them used AI regularly. Four students described a habit that has stayed with me.

Whenever a homework problem felt a little tough, they turned to AI. They did not wait until they were stuck. The first sense of difficulty was itself the cue to hand the problem over, and to them this felt entirely reasonable. That moment, when difficulty arrives and is passed on before it can do its work, is what this article is about.

How AI Changes the Way We Judge Answers

Daniel Kahneman taught us to see the mind as working in two modes. System 1 is fast, associative and intuitive. It lets us read a face, finish a sentence or sense danger without deliberate effort, and it is also where anchoring, overconfidence and other familiar biases live. System 2 is slow and effortful. It questions a first impression, checks the evidence and revises a judgment.

System 2’s corrective work does not continuously scrutinise System 1. It is recruited when circumstances demand additional attention, for example, when something is difficult, surprising, or signals a possible error. Our own thinking can sometimes provide signals, such as difficulty, surprise, hesitation or contradiction, that prompt further scrutiny. The four students I spoke with seemed to have developed a habit of treating that very signal as a reason to stop thinking and ask the machine.

An answer produced by AI arrives without these signals. Whatever happens inside the machine, including the step by step reasoning that newer models display, the output reaches the student as a finished intuition. It is fast, fluent and confident, and fluency is persuasive in its own right. Decades of research on processing fluency show that people treat ease of reading as a cue to truth. In a classic experiment, statements that were easier to perceive were judged as true more often than the same statements presented with lower perceptual fluency.

Before generative AI, polished prose usually required education and editorial effort, so fluency carried some information about its source. Generative AI makes fluency nearly free, while readers continue to trust it as though it were costly to produce.

The result is an asymmetry. When a student’s own intuition is wrong, she at least has a chance of feeling it. When a judgment generated by AI is wrong, the burden of noticing falls entirely on her, and she is likely to process the machine’s fluent answer through her own System 1. Plausibility takes the place of scrutiny.

The Evidence Behind “Cognitive Surrender”

Researchers have begun to measure this. Steven Shaw and Gideon Nave at Wharton describe AI as a third system of cognition operating outside the brain, and they call the tendency to adopt AI outputs with minimal scrutiny “cognitive surrender”. Across three preregistered experiments with 1,372 participants, accuracy rose by 25 percentage points when the AI was correct and fell by 15 points when it was wrong.

People were not evaluating the answers so much as inheriting them. A survey of 319 knowledge workers by Microsoft Research and Carnegie Mellon points the same way. Workers who placed more confidence in AI engaged in less critical thinking, while those more confident in their own abilities engaged in more.

Why Discernment Matters More Than Ever

This is why the capacity that matters most now is discernment. Discernment is different from simply thinking more slowly. It begins with recognising that a judgment deserves examination in the first place, especially when nothing feels wrong. It continues by asking why an answer seems convincing, and whether that conviction comes from evidence or only from polish. It includes noticing what the answer leaves out.

It ends with a decision to accept the output, modify it, seek another view, or set it aside. Discernment is therefore not only the capacity to evaluate an answer. It is the capacity to recognise when accepting an answer without independent examination is itself a decision that requires scrutiny.

System 2 remains necessary, but what it scrutinises has changed. Students are no longer examining only the products of their own intuition. Increasingly, they are examining judgments produced by another cognitive system, one that operates continuously, rapidly and at scale.

Developmental Offloading: What Students May Lose

For adults, relying on AI is often a matter of cognitive offloading, handing a task to a tool while keeping the underlying capacity intact. For students, the issue may be different, because many of the capacities involved in independent judgment are still developing. When the environment continuously supplies ready-made interpretations, a young person has fewer occasions to generate an interpretation independently, tolerate uncertainty, compare alternatives and discover that a first impression was wrong.

The concern for education, then, is what I call developmental offloading. Capacities that are repeatedly delegated are exercised less often, and so they may not develop fully.

The evidence from classrooms supports this concern. In a field experiment with nearly a thousand high school mathematics students, Bastani and colleagues found that access to GPT‑4 improved performance while the tool was available, but students given unrestricted access performed worse when AI access was removed than students who had never had access.

A study of university learners published in the British Journal of Educational Technology found that ChatGPT significantly improved short term task performance without boosting intrinsic motivation, knowledge gain or transfer, and the authors named the pattern “metacognitive laziness”. The work looked better while the learning underneath it grew thinner.

The deepest risk goes beyond whether a student follows a particular answer. Over time, repeated interaction with AI may shape what the student notices, what she considers plausible and what she regards as worth questioning. At that point the machine is no longer only answering questions. It is quietly shaping the learner’s sense of which questions need asking.

How Schools Can Use AI Without Weakening Independent Thinking

None of this is an argument for keeping AI out of classrooms. The same Bastani study offers the most hopeful finding in this field. Safeguards made a real difference, and a tutor instructed to give teacher designed hints rather than hand over answers largely mitigated the harm. The technology did not decide the outcome. The design of its use did. AI augments discernment when its outputs provoke inquiry, comparison and reflection, and it erodes discernment when its outputs routinely replace them.

Schools can act on this. Teachers can ask students to stay with a difficult problem for a set time and form their own answer before consulting AI, and then to compare the two and explain the difference. They can set tasks where the question is not whether the AI’s answer is correct but why it sounds convincing and what it leaves out.

They can reward students for catching errors in machine output, which turns scepticism into a skill that earns recognition. Periodic assessments without AI can reveal what a student can do alone, so that neither the student nor the teacher mistakes assisted performance for capacity. And teachers can model doubt openly, showing students what it looks like to pause before a fluent answer and ask whether it has earned trust.

Helping Students Stay With Difficulty

Perhaps the most important support is also the simplest. A student turns to AI at the first sign of difficulty partly because the difficulty feels like evidence that she cannot do the problem. Teachers can change what that difficulty means. I saw this directly in my own work with these students. When a student hesitated during our sessions and was prompted with “think again”, “you know it” or “you can do it”, that prompt was enough to keep most of the students engaged with the problem. Instead of reaching for AI, they stayed with the problem and drew on what they already knew.

This is scaffolding in its truest sense, support that holds the student within the struggle long enough for her own capacity to take shape, and then steps back. The wider research points the same way. People who were more confident in their own abilities engaged in more critical thinking, while those more confident in AI engaged in less. A student’s belief that she can do it is not a soft extra. It is part of what keeps discernment alive.

The Question Education Must Answer

The four students I spoke with were not refusing to think. They knew they were turning to AI, and it seemed entirely reasonable to them. What they could not see was what they were handing over with it, the moment of difficulty in which discernment grows. The question facing education is not whether AI can think for our students. It is whether, through repeated interaction with AI, they continue to develop the capacity to decide when they should think for themselves.