AI Research Question Generator vs Questions From Your Papers

What is an AI research question generator?

An AI research question generator is a web tool that asks for a topic and returns a list of candidate research questions produced by a large language model. The two free generators checked for this guide do not ask for the papers you have read, so the questions come from the model’s general training, not from your literature.

Two generator pages that rank for the query in a web search, checked in November 2026, show the pattern. The first offers one input box with a 255-character counter, asks for a topic or search term, and promises several focused, actionable questions. The second asks the user to describe a research area, a target audience and constraints such as timeline, data and budget, and returns a table sorted by question type and a feasibility score. Neither page accepts a PDF.

A research question generator is a brainstorming tool: it cannot confirm that a question is open, because it has not read the field you are writing about.

Are AI-generated research questions any good?

AI-generated research ideas can be judged more novel than ideas written by experts, but they tend to be weaker on feasibility, and novelty without a literature check is not the same as a gap. Two published studies give numbers, one on research ideas in general and one on research questions specifically.

Si, Yang and Hashimoto (2024) recruited over 100 NLP researchers to write research ideas and to review ideas blind, comparing human ideas with ideas from an LLM agent. The authors report that “LLM-generated ideas are judged as more novel (p < 0.05) than human expert ideas”, while the LLM ideas were “slightly weaker on feasibility”. The same paper lists “failures of LLM self-evaluation” and a “lack of diversity in generation” as open problems. For a student, the practical reading is that a long generated list can hold many near-duplicates, and the model’s own rating of its ideas is not a reliable filter.

CoQuest, a research prototype presented at CHI 2024 by Liu and colleagues, tested research question co-creation with an LLM agent that drew on a pool of 2,043 HCI papers. Twenty HCI researchers from eight institutions, all graduate students or just graduated, created 504 research questions in total. Participants rated breadth-first generation (three questions at once) higher on creativity (3.78 against 3.18) and trust (4.15 against 3.50), while depth-first generation (each question building on the last) produced questions rated more novel.

How is a generator different from building research questions from your own papers?

A generator starts from a topic and returns plausible questions; building questions from your own papers starts from what those studies found, missed or disagreed about, so each question arrives with the evidence that it is open. The difference matters most in a proposal, where an examiner asks why the question needs answering.

Farrugia and colleagues (2010) make the same point from a research-methods angle: research questions “arise out of a perceived knowledge deficit within a subject area or field of study”, and “it is imperative to understand what has been studied about a topic to date”.

How do you test an AI-generated research question?

Test an AI-generated research question in four steps: check whether your papers already answer it, name the study that shows the gap, score it against the FINER criteria, and write your own version with your method in mind. A question that fails the first two steps is a topic, not a research question.

1. Search your own papers first. Ask whether any paper you hold reports on the same population, setting and outcome. If one does, the generator question is either answered or needs narrowing. Litrevu’s guide to checking whether a research question has already been answered walks through the search.
2. Name the gap. Point to the limitation, conflict or missing population in a specific study. The research gap guide lists where gaps usually appear in a paper.
3. Score the question against FINER. The FINER criteria come from Hulley and colleagues and are summarised by Farrugia et al. (2010) in the Canadian Journal of Surgery.
4. Rewrite it yourself. Fit the wording to your method and level, then align it with your aims and objectives.

The letters are from Hulley and colleagues as cited by Farrugia et al.; the one-line test questions in the right-hand column are Litrevu’s wording. The Novel row is where generator questions need the most care: Si, Yang and Hashimoto measured novelty as judged by expert reviewers, and a reviewer’s judgement is not the same as a search of the literature. The research question guide covers FINER, PICO and the other frameworks in more depth.

Is it allowed to use AI to generate research questions?

Universities that publish a rule on this, such as the School of Advanced Study (University of London), the University of Alabama and Stellenbosch University, allow AI for brainstorming research questions with conditions: agreement with a supervisor first, a declaration of the use, or both. The rule belongs to your university and often to your department, so read the current policy and declare any use.

In the US, the Alabama guidance covers master’s theses and doctoral dissertations, with each program setting its own parameters; the UK SAS rule covers doctoral researchers. All three rules leave the final decision with the supervisor or the program, and all three expect AI use for ideas to be visible, through a prior agreement, a written disclosure or a signed form. The same pattern holds for the rest of a proposal, covered section by section in how to use AI for a research proposal. Litrevu’s AI disclosure statement generator and the guide to writing an AI use disclosure statement help with the wording.

How do I turn my own papers into research questions with an AI assistant for writing backed by sources?

Litrevu is an AI assistant for writing backed by sources. It reads every paper, report and PDF you add, answers your questions with a citation to the exact passage behind each claim, compares sources side by side, and checks your own draft against them.

On the research question stage, the work looks like this:

1. Add your papers. Upload the PDFs from your reading, paste a PDF link, a DOI or an arXiv link, or let Litrevu find open-access papers on your topic and pick the ones you want.
2. Ask what the papers cover. Ask questions such as “Which populations and settings do these studies use?” or “What limitations do the authors state?” Every answer cites the passage in your papers behind it, and Litrevu says when your papers do not cover a question.
3. Compare the studies side by side. Line the studies up in a cited table (population, setting, method, finding) and look for rows where findings disagree or where no study covers a group.
4. Open each citation. Every citation opens the highlighted passage on the PDF page, so you read the evidence for a possible gap yourself.
5. Write your candidate question, then test it. Ask your papers whether any of them already answers your question. Paste the paragraph that justifies the question and get each claim back as Supported, Partly supported, Contradicted or Not in your papers.

“Not in your papers” means your library does not cover a claim. It is a cue to search wider databases, never proof that the gap exists in the literature. The student decides which gap is real and writes the question; Litrevu lines up and checks the evidence in the student’s papers.

What does a generator question look like next to a paper-grounded question?

A generator question is usually broad and unanchored, while a question built from your papers names a population, a setting and the study that leaves it open. The table below is a format illustration with placeholders, not real findings.

Frequently asked questions

What should you do next with your candidate research questions?