The quality of your survey data is only as high as the quality of the respondents answering your questions. However, even that has become more challenging in recent years. In 2024 and 2025, the scientific community observed “a surge of machine-written responses” to online surveys, exceeding previous levels by a significant margin. This perfect storm of “too-good-to-be-true” responses is now one of the most significant threats to data quality in market research. For market researchers, UX professionals, and digital marketers, quickly and accurately identifying fake responses is becoming a top priority.
AI language models like ChatGPT can churn out grammatically perfect and long-winded responses at an unprecedented rate. As a result, when those responses enter your data, they are likely to influence your findings in unintended ways.
In the following section, we’ve highlighted the most common red flags for AI-generated responses based on interviews with market research professionals and our own recent studies. If you learn to spot these patterns in your survey data, you can substantially reduce the chances of AI biasing your results.
Here are the top 5 patterns
1. Too-Polished Language.
AI bots produce textbook-perfect prose. Responses often show no typos, no slang, no hesitations, and virtually no filler words – just grammatically-flawless, formal sentences. A real person will use contractions (“don’t”), or “um” and “you know” and other “messy” speech markers. In contrast, a bot-generated answer tends to read like it was edited in red ink and then toned down to “please and thank you” mode. In the words of one industry blogger, AI-generated answers can be “clean, structured, grammatically correct…and completely fake”.
Question: “What do you like most about our new website?”
AI-like Answer:
“The website provides a seamless user experience characterized by intuitive navigation, aesthetically pleasing design elements, and a well-structured layout that enhances usability and customer satisfaction.”
2. Essay-Length Responses
Another big one is way-too-detailed write-ups. No normal person is going to handwrite a five-paragraph survey response in a text box. If you have one person writing full paragraphs and other people answering in single sentences, that’s noteworthy. Similarly, if one respondent always details both pros and cons in bulletpoint-like lists, that’s bot-like behavior. AI answers can be much longer than normal, with extra-wordy, vague praise or overly-broad rationales.
Question: “How could we improve our customer service?”
AI-like Answer:
“Customer service can be improved by implementing a proactive approach to issue resolution, enhancing training programs for representatives, and adopting omnichannel support systems. Additionally, developing a customer feedback loop will ensure that user insights are continuously integrated into the improvement process. By doing so, the company can increase customer satisfaction and brand loyalty over time.”
3. Echoing or Mirroring the Question
A key bot trick: just restating the question back. Say your question is “What’s your favorite car brand?” A bot answer might just start with “My favorite car brand is…” and then fill in the blank. Real respondents often launch right into personal preferences or examples, not repeat question words or phrasing. So if an answer just feels like Question: [X]? Answer: [X], it’s a clue. (Question-mirroring is a known giveaway of automation.)
Question: “What’s your favorite car brand and why?”
AI-like Answer:
“My favorite car brand is Toyota because it offers reliable vehicles that provide great value for money.”
The AI survey open-ends coding platform that matches human coder
Complete survey open-ends coding in minutes instead of days or even weeks.
4. Same Structure or Exact Phrasing Across Responses
Bots also tend to fall back on the same sentence patterns. You might see identical first-lines like “In my opinion, …” or “I think that …” in multiple answers, word-for-word (even when from different people). In a data set, these are “Highly consistent or patterned answers”. For instance, one bot may use the same structure of “I always choose xxx because xxx” in many survey responses.” Real people answers are more variable.
Survey responses from multiple “different” respondents:
“In my opinion, the product provides excellent value.”
“In my opinion, the product provides excellent service.”
“In my opinion, the product provides good price.”
5. Contradictions or Illogical Combinations
Another problem: answers that contradict each other or otherwise don’t make sense. Respondents will say “I’ve never used X” then provide suggestions to improve X. Or they’ll pick “No children” then list their kids’ ages in the next question. These logic violations will fail logic checks. Patterns-analysis will also flag them, because no one person should answer such things that are blatantly contradictory or logically impossible. Scanning for such logical inconsistencies can also help weed out fake responses.
Question 1: “How many children do you have?”
Answer: “None.”
Question 2: “What are your kids’ favorite snacks?”
Answer: “They love Goldfish crackers and fruit gummies.”
Other Problematic Patterns or Metadata
Finally, it’s not just the writing style: look at the metadata. Were half the responses received in less than 5 seconds? AI software can fill out an entire survey in mere seconds, while humans take minutes. So if you see 50 replies with completion times of under 5 seconds and another 50 with the same completion time a few minutes later, view those as suspect. The other metadata tip: if a bunch of survey takers picked the same answer in a multiple-choice question (for no obvious reason), that uniformity suggests “robot.” In short, dearth of human variability – in wording, in multiple-choice answers chosen, in response speed – is a tell of automation.
These aren’t foolproof, but they’re warning signs that AI may be biasing your results.
Why AI Answers Appear Too “Perfect”
To understand this, it helps to know why AI text has these common traits. Language models are probability engines: they predict the next best word given all the surrounding text. To maximize those probabilities, they produce very safe, coherent, and general answers. They “play it safe,” choosing common phrasing and avoiding straying off topic. That’s why so many AI replies sound polished but not unique or memorable.
Another reason is that models are designed to avoid offensive or unbelievable statements, which ironically results in bland language.
Finally, AI tools also tend to “mirror” the survey question phrasing, because they’re trying to directly answer the prompt as clearly as possible. In a real conversation, a person might unconsciously rephrase the question, but an AI might extract the most relevant parts of the question and reuse them verbatim.
One last thing to keep in mind: there is no foolproof way to detect fraudulent AI responses with 100% accuracy. The key is to raise the bar so high that it is more difficult and riskier for bots than to answer honestly. Even simple solutions, like asking the user to provide a short explanation along with their rating, or throwing a random open-ended question in the mix, can slow down or catch out automatic responses.
With these vigilance strategies in mind, and by looking out for the patterns mentioned above, you can mitigate the effect of AI-generated responses in your survey data.