If AI interview systems are evaluating your responses, the method they use to compare your language can shape your score. Understanding the difference between surface overlap and meaning overlap is critical to making better responses.

That’s what a new research paper investigates.

The Core Finding

Researchers reviewed 58 studies and described three different categories of research. Here is a demonstration.

For example, if two job candidates both say, “I led a team through a difficult transition,” they may use very different words to describe it.

One says: “I coordinated stakeholders and clarified expectations.”

The other says: “I got everyone aligned and moving in the same direction.”

Here are three possible ways to analyze their responses.

1. Word Counts measure content similarity
To a system that does a word analysis, this is surface-level overlap. Do the same words appear between a model and the person being evaluated?

An example might be: “deadline,” “project,” “team.” This is the simplest method.

2. Linguistic style similarity
This approach looks at how language is used.

Are you reflective? Causal? Analytical? Emotional? It measures psychological states, social orientation, and cognitive processes.

This type of evaluation is typically based on Linguistic Inquiry and Word Count (LIWC). LIWC was begun as a tool in the 1990s to help social psychologists analyze word choices.

3. Semantic similarity
This measures actual meaning, even if the words differ. This is where transformer models like BERT and RoBERTa come into the picture.

And here’s the major finding in the research: Transformer-based models were highly consistent with each other. Older word-count methods were not.

That means modern AI models measure meaning and give more stable results.

What This Means for AI Interviews

Most candidates assume AI is “looking for keywords”, but this paper suggests that’s only partly true and increasingly outdated.

Modern semantic similarity systems are better at detecting:

• Whether your answer actually matches the question

• Whether your story stays coherent

• Whether your explanation follows a logical sequence

• Whether your examples carry meaningful context

That means

• Specificity matters. Not generalized responses.

• Keywords should not be your focus

• Verbs that indicate behavior make for better responses

-Inadequate answer: “I’m good at problem-solving.”

-Better answer: “When our vendor failed two days before launch, I reorganized the rollout into phases and kept the deadline.”

The Takeaway

• Tell real stories.

• Use concrete actions.

• Show sequence, cause, and consequence.

• Explain why it mattered and what it means to you.

Meaning-making is where your real advantage in an interview lives.

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To reference the paper

Title: Textual Similarity in Organizational Research: Review of Applications, Consistency of Methods, and Best Practice Recommendations
Authors: Siyi Liu, Louis Hickman, Linus Dahlander, Henning Piezunka
Journal: Organizational Research Methods
Year: 2026
DOI: 10.1177/10944281261432629
Google Scholar: Search for the full title to locate the article

 

Better stories are possible — and they start with preparation.

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