UGC NET Exams Cancelled After Experts Flag AI-Generated Questions and Repeated Content

National Testing Agency Cancels Three UGC NET Papers

The National Testing Agency has cancelled and ordered fresh UGC-NET papers in English, Commerce and Sociology after widespread complaints from candidates and subject experts exposed serious irregularities. The exams, conducted during the June 2026 cycle, are used to determine eligibility for Assistant Professor positions and Junior Research Fellowship (JRF) in Indian universities and colleges.2

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Fresh examinations have been scheduled for September 9 and 10.2

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Experts Claim Generative AI Was Used to Draft Questions

Subject experts speaking anonymously alleged that generative AI was used extensively during paper preparation and translation, contributing to errors that escaped adequate human review. A senior Education Ministry source reported that National Testing Agency officials admitted the papers were designed in an "ad hoc manner" and were "unable to declare the answer keys" because of this process.2

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However, NTA director general Abhishek Singh denied that generative AI was used to author the disputed papers, stating there was a "human review" after the papers were set.1

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Sociology Paper Riddled With Misspelled Scholar Names

The Sociology paper drew the sharpest reaction from candidates who spotted names of major scholars twisted almost beyond recognition. George Ritzer appeared as "Putzer", Talcott Parsons became "Parsow", and G S Ghurye was rendered "Ghunye".1

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Martha Nussbaum's name did not survive the process either. Students said the terminology felt garbled and the questions barely matched what they had studied. Another student who answered using the Hindi version of the Sociology paper claimed that multiple questions were difficult to understand in the translated paper, pointing to translation errors in AI-assisted paper creation.2

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Repeated Questions From Prior Exams in English and Commerce

The English paper had a different problem: repetition. Candidates alleged that around 67 of its 150 questions had appeared in an earlier UGC-NET exam, with some claiming even the order of answer choices matched.1

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Students told MediaNama that the English paper reportedly repeated more than 50 questions from an earlier paper, with some passages and answer options appearing unchanged.2

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Commerce candidates reported an even larger overlap, with one candidate on Reddit claiming around 80 questions were "literally copy pasted" from December 2024.2

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Commerce candidates also pointed to repeated questions on taxation, break-even analysis, capital structure and services marketing.1

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Six-Week Delay Raises Questions About Institutional Accountability

What puzzled the education ministry as much as the errors themselves was the gap before anything was done. Ministry officials reportedly wanted the flawed papers cancelled and retests scheduled by the end of July or the first week of August, but the National Testing Agency waited nearly six weeks and only announced the cancellations when it released provisional answer keys for 84 of the 87 subjects.1

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Paper Preparation Process Under Scrutiny

Another Education Ministry source questioned how the examiners did not have answers to the questions they had designed, asking "A panel of four to five experts sets a question paper. If there was indeed a panel, how were so many questions from the previous year's question papers picked?"2

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A JNU faculty member described the broader concern as "the degree to which automated systems were used to translate, organise and assemble high-stakes national examinations" and whether "adequate human academic review" followed that process.2

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The incident has raised fundamental questions about how much of India's high-stakes exams are now being built by automated systems in high-stakes national examinations, and how little human checking may have happened afterward. The UGC NET exam decides who is eligible to teach and pursue research at universities across the country, making factual inaccuracies and inadequate human review particularly concerning for researchers and future academics whose careers depend on AI in exams being properly vetted.