Wait… You’re Really Using ChatGPT for Research?

Can ChatGPT replace thematic analysis tools like Leximancer?

With the rise of large language models, some researchers have started experimenting with ChatGPT for tasks traditionally handled by specialised thematic analysis software. The appeal is understandable - ChatGPT is fast, accessible, and provides fluent, structured responses. But is it really analysing?

More importantly, should you be using a tool that generates text based on probabilities rather than uncovering new knowledge?

If you're in academia, isn’t your mission to propel the knowledge of mankind, not recycle old content or - worse - rely on fabricated information?

Let’s break down why credibility is out the window when using ChatGPT for research.

The Credibility Crisis of LLMs

Academic research demands precision, transparency, and reproducibility: three areas where LLMs like ChatGPT fail.

1. ChatGPT Generates Text, Not Knowledge

At first glance, ChatGPT's responses may appear analytical. You can prompt it to summarise data, rephrase findings, and perhaps mimic the style and tone of an academic paper. But what’s happening behind the scenes?

• ChatGPT doesn’t analyse data. It predicts text that sounds reasonable.

• It has no inherent understanding of qualitative themes, only likely probabilities between words.

• It cannot generate new insights, only repackage existing patterns it has been trained on.

So if ChatGPT is just recycling past information, how can it advance knowledge? Certainly not on its own.

2. LLMs Have No Source Transparency

One of the most critical flaws of ChatGPT in academia is its lack of citation and source traceability.

• It does not disclose where its knowledge comes from.

• It cannot distinguish between peer-reviewed research and misinformation.

• Even when asked for references, it may generate fake citations (a phenomenon known as hallucination).

For a field built on rigorous sourcing and validation, this makes ChatGPT fundamentally incompatible with academic integrity. How can you trust an analysis tool that cannot prove where its conclusions come from?

3. LLMs Introduce Bias and Hallucination

Bias is an unavoidable issue in large language models. ChatGPT’s training data includes internet text, books, and articles, but:

• It reflects cultural, institutional, and algorithmic biases inherent in those sources.

• It can distort findings, producing misleading conclusions based on the way questions are phrased.

• It hallucinates facts, confidently providing inaccurate information as if it were true.

In research, credibility is everything. A tool that fabricates evidence, misrepresents themes, or injects hidden biases is a dangerous shortcut rather than a research asset.

4. Why Would You Want to Use an LLM in Academia?

This is the real question.

If you entered academia, it wasn’t to regurgitate the same ideas. Research is about pushing the boundaries of knowledge - not recycling text from a model trained on yesterday’s internet.

• If you want genuine insight, you need a tool that works with your data, not a pre-trained dataset of unknown origins.

• If you’re committed to academic integrity, you need a tool that provides transparent, reproducible results - not probabilistic guesses.

• If your goal is to advance research, you need rigorous qualitative analysis, not a chatbot.

Leximancer Is A Research Tool Built for Academic Integrity

Unlike ChatGPT, which generates text, Leximancer analyses it.

• Concept Mapping, Not Text Prediction – Leximancer identifies key themes and relationships in your qualitative data, providing an unbiased, evidence-based structure.

• Transparent and Reproducible – Every analysis is backed by clear algorithms that can be tested and verified. There’s no black-box processing—what you see is what your data shows.

• Works Exclusively With Your Data – Leximancer does not rely on pre-existing datasets, meaning your findings are grounded in your research, not internet training data.

• No Hallucinations, No Bias Manipulation – Results are drawn directly from the dataset you provide, eliminating speculative or fabricated responses.

The Verdict: If Research Matters, ChatGPT Isn’t the Right Tool

While ChatGPT may be useful for brainstorming or drafting text, it cannot replace dedicated qualitative analysis software. For serious research, where bias control, transparency, and reproducibility matter, Leximancer is the superior machine learning tool.

Want to see Leximancer in action? Try it today Try it today and experience concept-driven analysis for research that goes beyond surface-level insights.