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Thoughts on the new NEP 2020 impact on postgraduate research programs (78)

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Starting 5 pts 0 followers
Yenepoya (Deemed to be University) · Posted
Genomics & Bioinformatics Astronomy & Astrophysics Earth & Climate Science

My Setup

I run a machine learning lab with 8 PhD students. Over the past 18 months, I have noticed a significant shift in how my students (and I) work, largely driven by large language models.

Things that have genuinely improved:

  • Literature search: I use LLMs to get a quick conceptual map before diving into papers
  • Code debugging: GitHub Copilot has reduced boilerplate time by ~30%
  • Writing: Grammar and clarity checks are now instant

Things I am cautious about:

  • Students are sometimes getting confidently wrong answers about recent results
  • Risk of AI-generated text without proper attribution
  • I worry that relying on AI for literature summarization may weaken reading comprehension skills

The question: How are others balancing the productivity benefits against these risks? And what explicit policies has your department adopted for AI use in research?

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13 Replies

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Eshan Patil Growing 70 pts · Accepted answer

This is really helpful context. One more question: for the ethics committee approval, does the institution where the data is collected or the institution where the analysis is performed need to be the primary ethics approver?

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Replying to Eshan Patil
Rajesh Kumar · Accepted answer

Thank you! The reference to MuRIL is particularly useful.I had not considered it as an alternative to XLM-R for Indic languages.

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Uday Bose Starting 5 pts · Accepted answer

This is really helpful context. One more question: for the ethics committee approval, does the institution where the data is collected or the institution where the analysis is performed need to be the primary ethics approver?

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Ashwin Murthy Distinguished 560 pts · Accepted answer

If you are not already using a reading group format with your students, I would strongly recommend it. Forces everyone to articulate what they read.

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Replying to Ashwin Murthy
Preethi Anand Distinguished 690 pts · Accepted answer

Overleaf + GitHub integration is underrated. You get version control and collaboration in one place.

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Replying to Ashwin Murthy
Indira Balan · Accepted answer

Marking this as the accepted answer. Incredibly helpful and specific. Really appreciate you taking the time.

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Replying to Ashwin Murthy
Ishaan Verma Growing 60 pts · Accepted answer

If you are not already using a reading group format with your students, I would strongly recommend it. Forces everyone to articulate what they read.

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Asha Pillai Distinguished 880 pts · Accepted answer

This is a known issue with XLM-R on code-mixed data. The MuRIL preprint has a section specifically comparing these.

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Fatima Sheikh Growing 95 pts · Accepted answer

I can answer your question about IEEE Transactions review timelines from my experience submitting to IEEE TNNLS.

For Q1 IEEE journals, expect:

  • Initial desk review: 2–4 weeks
  • Full peer review: 3–6 months
  • Major revision decision: another 2–3 months for re-review

As for code and data, most IEEE journals now request code upon acceptance, not submission. Regarding your 2.3% improvement.that is borderline. You will need very strong experimental rigor: multiple runs, confidence intervals, and significance tests.

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Lakshmi Devi Growing 75 pts · Accepted answer

I think you are conflating two separate problems here. The publish-or-perish culture creates pressure, yes.but the solution is not to criticize researchers who are navigating an unfair system. The real problem is with evaluation committees who treat publication count as a proxy for research quality. Fix the evaluation, and the incentives change.

Also worth noting: open review has been implemented in some fields and has its own problems. It can disadvantage early-career researchers who fear retaliation from senior colleagues they have reviewed critically.

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Amit Joshi Starting 15 pts · Accepted answer

Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.

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Kiran Yadav Growing 130 pts · Accepted answer

For longitudinal data with missing values, mixed-effects models handle this more gracefully than imputation in most cases.

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Arjun Venkatesan Growing 195 pts · Accepted answer

I think you are conflating two separate problems here. The publish-or-perish culture creates pressure, yes.but the solution is not to criticize researchers who are navigating an unfair system. The real problem is with evaluation committees who treat publication count as a proxy for research quality. Fix the evaluation, and the incentives change.

Also worth noting: open review has been implemented in some fields and has its own problems. It can disadvantage early-career researchers who fear retaliation from senior colleagues they have reviewed critically.