New ask Hacker News story: Ask HN: Is using AI tooling for a PhD literature review dishonest?
Ask HN: Is using AI tooling for a PhD literature review dishonest?
4 by latand6 | 1 comments on Hacker News.
I'm a PhD student in structural engineering. My dissertation topic is about using LLM agents in automating FEA calculations on common Ukrainian software that companies use. I'm writing my literature review now and I've vibecoded a personal local dashboard that helps me manage the literature review process. I use LLM agents to fill up the LaTeX template (to automate formatting, also you can use IDE to view diffs) in github repo. Then I run ChatGPT Pro to collect all relevant papers (and how) to my topic. Then I collect the ones available online, where the PDFs are available. I have a special structure of folders with plain files like markdown and JSON. The idea of the dashboard is the following: I run the Codex through a web chat to identify the relevant quotes — relevant for my dissertation topic — and how they are relevant, it combines them into a number of claims connected with each quote with a link. And then I review each quote and each claim manually and tick the boxes. There is also a button that runs the verification script, that validates the exact quote IS really in the PDF. This way I can collect real evidence and collect new insights when reading these. I remember doing this all manually when I was doing my master's degree in the UK. That was a very terrible and tedious experience partially because I've ADHD So my question is, is it dishonest? Because I can defend every claim in the review because I built the verification pipeline and reviewed manually each one. I arguably understand the literature better than if I had read it myself manually and highlighted all. But I know that many universities would consider any AI-generated text as academic misconduct. I really don't quite understand the principle behind this position. Because if you outsource the task of proofreading, nobody would care. When you use Grammarly, the same thing. But if I use an LLM to create text from verified, structured, human-reviewed evidence — it might be considered dishonest.
4 by latand6 | 1 comments on Hacker News.
I'm a PhD student in structural engineering. My dissertation topic is about using LLM agents in automating FEA calculations on common Ukrainian software that companies use. I'm writing my literature review now and I've vibecoded a personal local dashboard that helps me manage the literature review process. I use LLM agents to fill up the LaTeX template (to automate formatting, also you can use IDE to view diffs) in github repo. Then I run ChatGPT Pro to collect all relevant papers (and how) to my topic. Then I collect the ones available online, where the PDFs are available. I have a special structure of folders with plain files like markdown and JSON. The idea of the dashboard is the following: I run the Codex through a web chat to identify the relevant quotes — relevant for my dissertation topic — and how they are relevant, it combines them into a number of claims connected with each quote with a link. And then I review each quote and each claim manually and tick the boxes. There is also a button that runs the verification script, that validates the exact quote IS really in the PDF. This way I can collect real evidence and collect new insights when reading these. I remember doing this all manually when I was doing my master's degree in the UK. That was a very terrible and tedious experience partially because I've ADHD So my question is, is it dishonest? Because I can defend every claim in the review because I built the verification pipeline and reviewed manually each one. I arguably understand the literature better than if I had read it myself manually and highlighted all. But I know that many universities would consider any AI-generated text as academic misconduct. I really don't quite understand the principle behind this position. Because if you outsource the task of proofreading, nobody would care. When you use Grammarly, the same thing. But if I use an LLM to create text from verified, structured, human-reviewed evidence — it might be considered dishonest.
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