Decoding the Turing Test: Can ChatGPT-4 Really Think Like a Human?

Introduction

Artificial Intelligence has come a long way, and with tools like ChatGPT-4, it’s becoming harder to tell whether we’re chatting with a human or a machine. But can AI truly mimic human thinking, or is it just really good at faking it? This question takes us back to the famous Turing Test, first proposed by computing pioneer Alan Turing in 1950.

A recent debate has sparked within the AI community surrounding whether ChatGPT-4 has “passed” the Turing Test. In particular, a 2025 study by Restrepo Echavarría argues that ChatGPT-4 fails the test—but another researcher, Marco Giunti, isn’t convinced. His paper, ChatGPT-4 in the Turing Test: A Critical Analysis, takes a deep dive into this claim and challenges its validity.

In this blog post, we’ll break down Giunti’s analysis in a way that’s easy to digest. We’ll explain what the Turing Test really measures, why its interpretation matters, and what this means for the future of AI.

What is the Turing Test, and Why Does It Matter?

The Turing Test is a classic method for measuring whether a machine can exhibit intelligent behavior indistinguishable from a human. Originally, it involved a three-player setup:

1. An interrogator who communicates via text with two unseen participants: one human and one machine.
2. The interrogator’s task is to determine which participant is human and which is AI.
3. If the machine is consistently mistaken for a human, it is said to have “passed” the Turing Test.

This test isn’t just about tricking people—it’s about an AI displaying human-like reasoning, conversational ability, and adaptability. However, implementing the test in real-life experiments is trickier than it sounds.

The Debate: Did ChatGPT-4 Fail The Turing Test?

A study by Restrepo Echavarría (2025) presented three bold claims:

1. No serious Turing Test has been performed on ChatGPT-4.
2. Their study provides the first valid Turing Test for ChatGPT-4.
3. Based on their findings, ChatGPT-4 does not pass the Turing Test.

However, Marco Giunti pushes back, arguing that these claims are flawed. Let’s unpack why.

Flawed Assumptions: What Went Wrong with the 2025 Study?

Here’s where things get interesting. Giunti identifies several critical flaws in the 2025 study’s reasoning and methodology.

1. Are There No Serious Turing Tests?

The first claim—that no serious Turing Test had been conducted before—rests on a set of five “essential” criteria defined by Restrepo Echavarría. However, Giunti argues that some of these criteria are too rigid and not universally accepted.

For example, the 2025 study insists that only a three-player Turing Test is valid (one AI and one human being judged together). But many modern studies—including some from 2023 and 2024—have used a two-player format, where participants interact with either an AI or a human and must figure out who they’re talking to.

Giunti highlights that these tests have been widely recognized as legitimate and conducted on a far larger scale than the 2025 study. So, dismissing them entirely seems unfair.

2. Was the 2025 Study “Minimally Valid”?

The second claim—that the 2025 study conducted a valid Turing Test—is also questionable. Here’s why:

• The sample size was tiny (only 10 test sessions with 10 participants). AI behavior is statistical, meaning we need hundreds or even thousands of tests to draw accurate conclusions.
• Only one prompt was used to query ChatGPT-4. AI responses vary depending on the questions asked, so using just one prompt severely limits the test’s credibility.
• The study lacked statistical rigor in analyzing results. Other studies, like those by Jones and Bergen (2024), have conducted tests with thousands of trials for more statistically sound conclusions.

Essentially, Giunti calls this Turing Test into question—not because ChatGPT-4 would necessarily pass—but because the testing conditions weren’t thorough enough.

3. Does the Data Prove ChatGPT-4 Fails?

The third claim—that ChatGPT-4 “fails” the test—rests on the statistical results of the 10 trials:

• In 9 out of 10 cases, human judges correctly identified ChatGPT-4 as a machine.
• The study interprets this as clear evidence that it failed.

But Giunti shows that this conclusion lacks proper statistical justification. When applying standard statistical thresholds (like a 1% or 5% significance level), the results aren’t strong enough to definitively conclude that ChatGPT-4 failed in absolute terms.

Instead, the experiment should have been repeated many more times to provide a clearer picture of how well ChatGPT-4 actually performs.

A Smarter Way to Evaluate AI’s “Humanness”

Giunti’s biggest takeaway is that Turing Test analyses should differentiate between:

1. Absolute criteria – Perfectly mimicking human responses with a 50% or lower guess rate from judges.
2. Relative criteria – Evaluating how close the AI is to human-like responses on a probabilistic scale.

Instead of taking a rigid pass/fail approach, Giunti argues that we should measure how human-like an AI is using statistical models. This is particularly important when AI will be used in customer service, education, and content creation, where its level of “humanness” is more of a sliding scale than a binary result.

What Does This Mean For AI’s Future?

Giunti’s analysis raises important questions about how we define AI intelligence:

• Should we be using only the classic Turing Test, or should we develop alternative evaluation methods?
• How do we account for AI’s rapid improvements in different settings?
• If AI scores highly on a Turing Test under certain conditions but not others, what does that really tell us?

The bigger picture isn’t just “Can AI fool us?”, but “How close is AI to acting human, and where do we set the threshold?”

Key Takeaways

1. ChatGPT-4’s alleged failure of the Turing Test is debatable – The study in question had major methodological flaws.
2. Turing Tests exist in multiple formats – The rigid three-player format isn’t the only valid approach.
3. Size matters in AI testing – With only 10 trials, the 2025 study lacked statistical weight.
4. AI’s “humanness” should be measured probabilistically – Instead of a simple pass/fail, we need gradient scales for AI evaluation.
5. ChatGPT-4 is getting better, but it’s not perfect – While it can appear human-like in many cases, it still isn’t fully indistinguishable from humans—yet.

As AI continues to evolve, so must our standards for evaluating it. Whether or not ChatGPT-4 “passes” the Turing Test isn’t just about tricking people, but about understanding AI’s growing role in human-like interactions.

What’s your take? Should AI be judged by Turing’s classic test, or do we need something new? Share your thoughts in the comments! 🚀

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This blog post is based on the research article “ChatGPT-4 in the Turing Test: A Critical Analysis” by Authors: Marco Giunti. You can find the original article here.