Original Research
02 February 2024
DOI 10.3389/frobt.2023.1277635
TYPE

PUBLISHEDOPEN ACCESSEDITED BY

Nicola Döring,Technische Universität Ilmenau, Germany
REVIEWED BY

Casey Bennett,
DePaul University, United States
Rohangis Mohseni,Technische Universität Ilmenau, Germany*CORRESPONDENCE

Benjamin Lebrun,benjamin.lebrun@pg.canterbury.ac.nz

Detecting the corruption of
online questionnaires by artificial
intelligence
Benjamin Lebrun 1*, Sharon Temtsin 2, Andrew Vonasch 1 andChristoph Bartneck 2
1

School of Psychology, Speech, and Hearing, University of Canterbury, Christchurch, New Zealand,
Department of Computer Science and Software Engineering, University of Canterbury, Christchurch,
New Zealand
2

RECEIVED 15 August 2023
ACCEPTED 04 December 2023
PUBLISHED 02 February 2024
CITATION

Lebrun B, Temtsin S, Vonasch A and
Bartneck C (2024), Detecting the corruptionof online questionnaires by artificial
intelligence.
Front. Robot. AI 10:1277635.
doi: 10.3389/frobt.2023.1277635
COPYRIGHT© 2024 Lebrun, Temtsin, Vonasch andBartneck. This is an open-access article
distributed under the terms of the Creative
Commons Attribution License (CC BY). The
use, distribution or reproduction in otherforums is permitted, provided the original
author(s) and the copyright owner(s) are
credited and that the original publication inthis journal is cited, in accordance withaccepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.

Online questionnaires that use crowdsourcing platforms to recruit participants
have become commonplace, due to their ease of use and low costs. Artificial
intelligence (AI)-based large language models (LLMs) have made it easy for bad
actors to automatically fill in online forms, including generating meaningful
text for open-ended tasks. These technological advances threaten the data
quality for studies that use online questionnaires. This study tested whether
text generated by an AI for the purpose of an online study can be detected byboth humans and automatic AI detection systems. While humans were able tocorrectly identify the authorship of such text above chance level (76% accuracy),
their performance was still below what would be required to ensure satisfactorydata quality. Researchers currently have to rely on a lack of interest among bad
actors to successfully use open-ended responses as a useful tool for ensuring
data quality. Automatic AI detection systems are currently completely unusable.
If AI submissions of responses become too prevalent, then the costs associatedwith detecting fraudulent submissions will outweigh the benefits of online
questionnaires. Individual attention checks will no longer be a sufficient tool toensure good data quality. This problem can only be systematically addressed bycrowdsourcing platforms. They cannot rely on automatic AI detection systems
and it is unclear how they can ensure data quality for their paying clients.
KEYWORDS

AI, detection, data quality, imitation game, large language models, online
questionnaires, reliability

1 Introduction
The use of crowdsourcing platforms to recruit participants for online questionnaires has
always been susceptible to abuse. Bad actors could randomly click answers to quickly earnmoney, even at scale. Until recently, a solution to this problem was to ask online participants
to complete open-ended responses that could not be provided through random buttonclicking. However, the development of large language models, such as ChatGPT or Bard,
threatens the viability of this solution. This threat to data quality has to be understood in thewider context of methodological challenges that all add up to what is now famously termed
the “replication crisis”.
The replication crisis, initially observed in the field of psychology and human behavior,
has also been shown to occur in other domains, including computer science, chemistry,
biology, and medicine (Peng, 2011; Baker, 2016). The crisis is based on the difficulty of
replicating the results of previous studies. A 2015 Open Science study attempted to replicate

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an experimenter controls the behavior of the robot. To be able
to replicate such studies, the study process and the protocol
governing the wizard’s behavior need to be documented precisely(Belhassein et al., 2019).
Possibly one of the most debated methodological issues in HRI
is the use of online studies that show videos of HRI to participants.
We must unpack this method, since it consists of several methodical
choices. First, it is important to distinguish between the recruitmentof the participants and the execution of the study. Participants could
be recruited online or in person. Participants can then participate
in the study online from wherever they are or they could be
asked to come to a specific location, such as a university lab.
In both cases, computers will likely be used to play the videos
and to collect survey data. Furthermore, it is also important todistinguish between interacting with a robot and viewing a video.
While interacting with a robot requires in-person experiments,
videos offer some distinct advantages over in-person HRI. Videos
can be viewed at the convenience of the participant at home or in
the laboratory.
There is still a debate dividing scholars as to whether videos
can replace in-person HRI. While some studies indicate a difference
in results in favor of embodied robots, such as more positive
interactions and greater trust (Bainbridge et al., 2011), or in favorof video-displayed robots, other researchers believe that behaviors
depend on the task (Powers et al., 2007). Li (2015) analyzed several
papers and identified reporting of 39 effects in 12 studies comparing
co-presence and telepresence robots. Of these 39 effects, 79%
were in favor of co-presence robots (such as finding them to beperceived more positively and to elicit more positive responses),
while 10% were in favor of telepresence robots. An interaction
between the two groups was observed in 10% of cases. The
authors report roughly similar percentages for improvements inhuman behavior. In their review of prosocial behavior, Oliveira et al.
(2020) demonstrated this variety in responses with studies using
virtual and embodied robots to trigger prosocial behaviors. Witha final sample size of 19 publications presenting 23 studies, theauthors indicated that 22% of these studies showed no association
between physical and virtual social robots and 26% showed mixed
effects. Thellman et al. (2016), in contrast, emphasize that it is
not the physical presence of the robot that matters but thesocial presence.

100 psychology studies. In this study, 97% of the studies showedsignificant results but the authors only succeeded in replicating
36% of them (Open Science Collaboration, 2015). Human–Robot
Interaction (HRI) is a multidisciplinary field and is no exception
to this crisis (Irfan et al., 2018; Leichtmann et al., 2022; Leichtmann
and Nitsch, 2020a; Leichtmann and Nitsch, 2020b; Strait et al.,2020). Ullman et al. (2021) attempted to replicate their own
underpowered HRI study (Ullman and Malle, 2017) using different
replication methods (i.e., conceptual, direct, and online). Each ofthese attempts, while having a more than acceptable sample size andpower, did not replicate the results of the original study. It seems
that the lack of power in the original study prevented the resultsfrom being reproduced and that the previously observed significant
effect probably does not exist, but represents a Type I error(false positive).
There are several factors that contribute to the replication crisis.
Some are specific to HRI, while others apply to all fields of study.
Several studies have investigated the factors that may contributeto replication difficulties in general, such as sample size, power,
recruitment methods, and publication bias (Peng, 2011; Baxter et al.,2016; Swiatkowski and Dompnier, 2017; Leichtmann and Nitsch,
2020a; Belpaeme, 2020). A Nature survey (Baker, 2016) asked 1,576
researchers the reasons that might contribute to the replication
crisis and most of them cited the pressure to publish, but alsobiases in methods and statistical analyses. For example, researchers
may conduct and report statistical analyses that inappropriately
increase the odds of finding significant results (Kerr, 1998;
Simmons et al., 2011).
Baxter et al. (2016) analyzed papers presented at HRI
conferences and reported that most of the sample sizes were
below 20 participants per condition. This small sample size percondition might lead to under-powered and less sensitive studies,as participants are not used as their own controls and individual
differences might influence results and their interpretation. In
this context, only large effect sizes might be detected. For thesereasons, it is important to check the required sample size a priori
using expected power, alpha, and effect size. The sample size might
therefore depend on the design of the study (a within-participants
design would require fewer participants than a between-participants
design) and the expected effect size. Leichtmann and Nitsch(2020a) argue that replication difficulties are based on a lack oftheory and transparency, and the use of methodologies that are
not powerful enough. They suggest increasing sample sizes andpre-registering studies. Furthermore, computer code should bemade available (Peng, 2011). Following these recommendations,researchers are increasingly making their materials and code
available, pre-registering their studies, and increasing their sample
sizes (Tenney et al., 2021). However, in an effort to increase sample
sizes, researchers are increasingly relying on online data collection,rather than in-person studies, which comes with trade-offs(Baumeister, 2016).
Ullman et al. (2021) and Strait et al. (2020) argue that thedifficulty of replicating results in the field of HRI is due tothe wide variety of robots used. Robots used in HRI areoften expensive and some robots have only ever been built
in small numbers. Such specialist robots can be complicatedto use (Leichtmann et al., 2022). Another specific issue in the
study of HRI concerns the Wizard of Oz technique, in which

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1.1 Crowdsourcing
The combination of recruiting participants online and showing
them videos online streamlines the research process. It is muchquicker and cheaper than running studies with people and robots
in the lab. During the COVID-19 crisis, this was practically the
only way of conducting HRI studies. There are several advantages ofconducting studies this way. First, conducting studies online enables
researchers to recruit more participants with greater demographic
diversity (Buhrmester et al., 2011). Additionally, pre-screening canbe carried out to recruit participants who meet certain demographic
criteria (e.g., by sharing the study only with people aged between 20
and 35). Online studies ensure that all participants get to experience
exactly the same interaction, avoiding some experimenter biases
and leading to consistent presentation of the stimuli (Naglieri et al.,02

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1.2 Quality control

2004). This may prevent the Hawthorne effect, i.e., the fact thathumans modify some aspects of their behavior because they feelobserved (Belpaeme, 2020). Finally, conducting studies online
can provide more diverse participants in terms of demographics
than typical American college samples, and such samples will be
more representative of non-college segments of the population(Buhrmester et al., 2011). The authors of these studies also specify
that, while participation rate is influenced by people’s motivations(compensation and study duration), the data obtained with thismethod remain at least as reliable as those collected using
traditional methods.
Many scholars have examined the use of crowdsourcing
services for online studies and compared them with eachother or with in-person experiments. While some studies
have shown that results obtained online are similar to thoseobtained from in-person experiments (Buhrmester et al., 2011;
Bartneck et al., 2015; Gamblin et al., 2017), other studies haveshown that the responses are different. Douglas et al. (2023)
compared the results of recruiting participants from different
online crowdsourcing sites, including Amazon’s Mechanical
Turk (MTurk), Prolific, CloudResearch, Qualtrics, and SONA.
They found that while Prolific and CloudResearch are themost expensive recruiting platforms, these participants were
keener to pass attention checks than those recruited via MTurk,
Qualtrics, and SONA, therefore providing better-quality data.
High quality was attributed to the same two crowdsourcing
sites; this was calculated according to various factors, including
attention checks, IP address, and completion time. The authors also
highlighted that recruiting participants using the SONA software
took longer.
Although they identified advantages of using Prolific over
MTurk, Peer et al. (2017) observed comparable data quality between
both platforms. However, they highlighted the presence of naivety
among Prolific and CrowdFlower participants over those recruited
via MTurk, with much greater diversity and the lowest rate ofdishonest behaviors also occurring on the former two platforms. The
authors define the concept of naivety as a property of participants
who have not become professional questionnaire-fillers who earnmoney this way on a daily basis. However, the use of CrowdFlower
did not reproduce results that have been replicated on MTurk
and Prolific. They conclude that Prolific is the best alternative to
MTurk, even if the response rate is slightly lower. Adams et al. (2020)
successfully replicated one of Peer et al.’s studies by comparing threesample groups: Prolific, MTurk, and a traditional student group.
They obtained similar results, but support the use of Prolific over
MTurk based on other factors (e.g., naivety, attention). No significant
difference in terms of dishonesty was reported, contrary toPeer et al. (2017).
Gamblin et al. (2017) also compared different participant
recruitment platforms, such as SONA and MTurk. They foundthe same patterns among SONA and in-person participants, while
the results for MTurk participants varied more widely. However,people recruited via the SONA system had stronger attitudes,
including racism, than the other groups, suggesting low levels ofsocial desirability in this sample. Although these crowdsourcing
platform participants seem to be a great alternative to in-person
studies, the quality is not always the best, and quality controls
are important.

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All experiments require a level of quality control. This applies
to the responses received from participants as well as ensuring thatthe technology used, such as robots, shows consistent behavior.
At times, participants might decide to randomly select answers
to reduce the time they have to spend on participation. This isespecially true when they participate in online studies and canchain them together to earn more money more quickly, or even
try to duplicate their participation (Teitcher et al., 2015). People
would therefore not answer in an optimal way. They would interpretthe questions superficially and simply provide reasonable answers
instead of optimizing their response, which would require cognitiveeffort (Krosnick, 1991). According to Krosnick (1991), satisficing
increases as a function of three factors, namely, the difficulty ofthe task, the motivation of the respondent, and the respondent’s
ability to perform the task. Hamby and Taylor (2016) examined
how these factors influence the likelihood of satisficing. They foundthat financially motivated MTurk participants were more likely tosatisfice than an undergraduate sample motivated by course credits
from their university. They also reported in their first study thatthe three factors underlying satisficing behaviors increased theconsistency and validity of responses. Thus, external motivations
seem to be a reason for a drop in data quality (Mao et al., 2013).
Duplicate answers from participants can be detected by checking
the participants’ IP addresses (Teitcher et al., 2015; Godinho et al.,2020; Pozzar et al., 2020). Daniel et al. (2018) proposed several
strategies to improve data quality, such as improving task design
and increasing the participants’ motivations, both external (e.g.,
incentivizing people with a bonus for good performance) andinternal (e.g., comparing their performance with that of other
respondents). It is important to note that incentives do not influence
the response rate of online surveys (Wu et al., 2022) and thateven when compensation is low, data quality does not seem to be
negatively affected (Buhrmester et al., 2011).
A variety of methods are used to avoid bad actors and toelicit valid and reliable data. These range from not overburdening
participants to filtering problematic responses. A common method
is to include attention checks that only ask the participants toselect a specific answer, such as “Select answer number three”. All
participants who failed to respond to this question correctly could
be excluded from further data analysis.

1.3 Bots
Using crowdsourcing platforms for the recruitment ofparticipants for experiments is big business. MTurk is estimatedto have at least 500,000 active users (Kuek et al., 2015). Bad
actors can use automated form-fillers or bots (Buchanan, 2018;
Pozzar et al., 2020; Griffin et al., 2022) to optimize their profits.
In their study, Pozzar et al. (2020) analyzed low-quality data sets
and respondent indicators to classify responses as suspicious or
fraudulent. Out of 271 responses, none were completely of goodquality. They categorized 94.5% of the responses as fraudulent and5.5% as suspicious. More than sixteen percent could have been bots.
Griffin et al. (2022) estimated that 27.4% of their 709 responses
were possibly from bots. Buchanan (2018) proposed collecting data

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from 15% more participants to compensate for low-quality data andautomated responses. This safety margin is expensive and quality
control requires considerable effort.
Many web pages want to ensure that their users are humans andhence have introduced the use of a Completely Automated Public
Turing test to tell Computers and Humans Apart (CAPTCHA).
Users of the internet are sometimes asked while browsing a web
page, for example, to click on all the pictures in a grid that include a
traffic light, to prove that we are humans.
Modern online questionnaire platforms, such as Qualtrics,
offer a variety of such tools to detect and prevent abuse,1 suchas prevention of ballot box stuffing, CAPTCHAs, bot detection,and designation of some answers as spam by detection of
ambiguous text or unanswered questions. However, bots might
bypass these protective measures (Griffin et al., 2022; Searles et al.,2023). Metadata, such as the IP address and response time, could
help prevent fraudulent respondents after the data have beencollected. If a large number of responses come from the same IP
address and/or the questionnaire is answered within less time thanhumans typically take, then the responses are likely not trustworthy.
Bad actors can then use IP address disguises, such as VPNs, to avoid
detection. This will continue to be a cat-and-mouse game in which
bad actors will continue to come up with ways to work arounddetection methods and the platforms continue to introduce moresophisticated tests.

those generated by AIs were in fact generated by a human, with aprobability of correctly recognizing AI-generated texts of 40.45%.
Human texts, conversely, were correctly detected only 54.45% of thetime. AI-generated responses covered similar subjects to those ofhuman participants but with less diversity and with the presenceof anomalies. While the authors propose that LLMs could be agood way of preparing exploratory or pilot studies, they warn that
their abusive use in crowdfunding services could result in the datacollected being unreliable.
LLMs do have a distinctive characteristic that might promote
their use for abusive purposes. Creating longer texts comes at
no practical increase in cost. Some platforms pay participants inproportion to their efforts: a participant who writes 1,000 words
will earn more than one who only writes 10. Since LLMs can easily
generate long passages of text, this is an ideal environment for abuse.

1.5 Readability
Stylometry could be used to detect AI-generated texts(Kumarage et al., 2023). This method corresponds to writing
style analysis, including, for example, the phraseology, thepunctuation, and the linguistic diversity (Gomez Adorno et al.,2018; Kumarage et al., 2023). According to these authors,phraseology corresponds to the “features which quantify how theauthor organizes words and phrases when creating a piece of text.”
For their linguistic diversity analysis, Kumarage et al. (2023) used
the Flesch Reading Ease score (Flesch, 1948; Kincaid et al., 1975).
Readability is what makes some texts easier to read than others(Dubay, 2004; DuBay, 2007), and consequently this represents anestimation of the difficulty of texts (Si and Callan, 2001) and how
easy it is to read them (Das and Cui, 2019).
DuBay (2007) highlights the fact that prior knowledge andreading skills might impact how easy a text is. Most readability scoresrefer to a ranking of the reading level a person should have in orderto understand the text [see Dubay (2004), DuBay (2007) for a review
on readability]. One of the most common variables used in existing
formulas is the number of words, but according to Si and Callan(2001), these ignore text content. They created a model that theyclaim is more accurate than the Flesch Reading Ease score and moreaccurate in judging K-8 science web pages.
LLMs can be used to create texts about any topic on which
they are prompted. For this reason, we need to know whether theirrequired reading levels, i.e., their readability scores, are similar to
those of texts written by humans. If texts generated by AI were very
easy or quite difficult to read compared to their human counterparts,
guessing the author of a text might be easier than if they were similar.
This leads to our first research question:

1.4 Large language models
One way of determining whether data come from a human being
is to ask the participant to write a few sentences that justify his or her
response to a previous question (Yarrish et al., 2019). This approach,and for that matter all of the abuse detection methods discussed
above, are now being challenged by the arrival of large language
models (LLMs), such as ChatGPT from OpenAI,2 BERT from
Google (Devlin et al., 2019), or LLaMA from Meta (Touvron et al.,2023).
Several scholars have claimed that texts generated by these
LLMs, including ChatGPT, are similar to or even indistinguishable
from human-generated text (Susnjak, 2022; Lund et al., 2023;
Mitrovic et al., 2023; Rahman and Watanobe, 2023). Not only can
ChatGPT-43 bypass a CAPTCHA by pretending to be blind,4 but
it can also answer open-ended questions (Hämäläinen et al., 2023).
The same authors showed that LLMs can be used to generate humanlike synthetic data for HCI tests. In their study, they asked their
participants to judge whether different texts had been generated
by a human or an AI, and the participants tended to think that

RQ1: Is the readability of stimuli written by an AI significantly
different from that of those written by a human?
1 https://www.qualtrics.com/support/survey-platform/survey-module/survey-

While readability scores are meaningful in this context, the qualitythat people attribute to a text is also of great value. People mightjudge the quality of a text according to its required reading level,
where a text that is too difficult might be judged as being of poor
quality, or on the contrary, as being of very high quality. We planned
to use participants’ justifications (or attention checks) provided
during study participation as our stimuli. As discussed earlier, their
quality is of prime importance, as they determine whether or not

checker/response-quality

2 https://openai.com/blog/chatgpt
3 ChatGPT based on OpenAI’s GPT-4 architecture; for ease of reading,“ChatGPT” will be followed by the number of the GPT model on which
it is based.

4 https://nypost.com/2023/03/17/the-manipulative-way-chatgpt-gamed-thecaptcha-test/

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researchers include data from the corresponding participant. Our
two next research questions were thus:

RQ4: How accurately can participants identify the authors of
the stimuli?
RQ5: How accurate and reliable are automatic AI detection
systems?
RQ6: What is the relationship between the accuracy of AI
detection systems and the accuracy of humanparticipants?

RQ2: What is the correlation between readability and userperceived quality of the stimuli?
RQ3: Do participants rate the quality of text written by an AI
differently from that of text written by a human?

Interestingly, scholars have attempted to discern the criteriathat participants use to make their decisions (Guo et al., 2023;
Hämäläinen et al., 2023). However, to our knowledge, no study so
far has combined LLMs with AI obfuscation systems, which aresupposed to make AI-generated texts undetectable, in the context ofscientific experiments. Thus, our final two research questions were:

1.6 The imitation game
Testing whether humans can tell the difference between a humanand a machine is often referred to as a Turing Test. Alan Turing
proposed the Imitation Game method in 1950 to test whether
machines can think (Turing, 1950). In this test, a machine (Player A)
and a human (Player B) are behind a door. An interrogator (Player C)
communicates with them by slipping a piece of paper under the door.
The goal of the machine is to imitate the human. The human has
the goal of helping the interrogator. The conversations the players
may have are unconstrained and can take as long as the interrogatorwants. This test is repeated with many interrogators, and if they areunable to reliably tell the human player apart from the machine, thenTuring argues that the machine is able to think. Turing proposedseveral variations on the test (Turing, 2004; Turing et al., 2004),
which go beyond the scope of this paper. The interested reader might
consult Copeland and Proudfoot (2009) for an extended discussion.
The Turing Test was designed to test a machine’s ability tothink. However, as time has passed, the terms “Turing Test” and“imitation game” have often been used interchangeably in various
research contexts. Such imitation games are commonly referred toas “Turing-style tests.” One of the best-known Turing-style tests is
the CAPTCHA (Ahn et al., 2003). This test is often referred to as a“reverse Turing Test,” since the role of the interrogator is carried out
by a machine rather than a human. The Loebner Prize Competition
used another variation on Turing’s test to identify the best chatbot(Moor, 2001). The Feigenbaum Test (Feigenbaum, 2003) represents
yet another Turing-style test. Feigenbaum suggests employing theImitation Game mechanism to assess whether a machine possesses
professional-level quality. The judge’s challenge in the Feigenbaum
Test is to determine whether the machine shows the appropriate levelof expertise in a specific domain. The judge must of course be an
expert in the field. The quality of text is still an important criterion
for distinguishing between humans and machines.
Initial results indicated that GPT-3 could pass a variation on
Turing’s test (Argyle et al., 2023). The participants in their study
correctly recognized 61.7% of a human-generated list and 61.2% of a
GPT-3-generated list. Scholars do not always obtain similar results.
Gao et al. (2023) used not only human participants but also the
GPT-2 Output Detector to identify the authors of human- and AIgenerated scientific abstracts. While participants correctly identified
68% of AI-generated abstracts and misidentified 14% of humanwritten abstracts as AI-generated, the automatic AI detector had
an accuracy of 99% in identifying AI-generated abstracts as AI.
However, not only have all studies used stimuli of different origins,
but LLM skills also progress quickly, and recent results might alreadybe obsolete. Here, we are interested in the ways in which people candetect the authorship of texts that people present as justifications
for their answers and as data quality checks. This leads to our next
research questions:

Frontiers in Robotics and AI

RQ7: What criteria are participants using to distinguish text
generated by an AI from that written by a human?
RQ8: Is Undetectable.AI able to overcome AI detection
systems?

1.7 Research objectives
Participants have many legitimate reasons for participating in
online studies, which include earning money, satisfying curiosity,and even for entertainment. However, it only takes a handfulof bad actors with some programming skills to compromise thewhole system. With the rise of LLMs and other forms of artificial
intelligence (AI), we need to know how good our fraud detection
systems are. The considerable progress in AI and LLMs in recent
months, and in systems aiming to make their detection moredifficult, highlights the potential threat posed. Their actions are nolonger limited to the generation of responses in questionnaires; theircapabilities extend to the automation of many tasks, such as throughAuto-GPT.5 It is of utmost importance that we can continue tocontrol the quality of responses in online studies. This includes theuse of automatic detection systems and people’s ability to identify theauthor of a text.

2 Methods
We conducted a within-participants study in which the authorof textual stimuli was either an AI or a human.

2.1 Participants
Forty-two students from the University of Canterbury wererecruited for this study. Twenty-one of them were male and 20 werefemale, while one person declared their gender to be different. Theirages ranged from 18 to 31, with an average of 20.9 years. The study
was advertised on a news forum of the Department of Computer
Science and Software Engineering; hence, many students reported
that they studied this subject.

5 https://github.com/Significant-Gravitas/Auto-GPT

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2.2 Setup

The example above shows that the participant clearly considered
the interaction. If a participant had written nonsense or just a single
word, their data would normally be considered unreliable and hence
excluded from further analysis. Again, the exact details of the source
study are only of tangential relevance to the study at hand.
The source study used for sampling of our human stimuli
collected 401 responses across its four conditions. We focused on thejustifications written by the participants for the purpose of quality
control. The lengths of the 401 justifications did not follow a normal
distribution (see Figure 1).
In order to select our ten human stimuli, it was necessary
to determine whether a specific condition led to more elaborate
justifications by the participants, since controlling the length to be
similar to that of the AI stimuli would improve validity. To doso, we conducted an ANOVA to test whether the lengths of thejustifications written by the participants differed across the four
conditions of the source experiment. The results indicated an effectof discount (p = 0.003) but no effect of agent (p = 0.102). Pairwise
comparisons indicated that the discount effect was in favor of the
low discount, with more words written in this condition (M = 20.18)
compared to the high-discount condition (M = 16.88), p = 0.003.
However, the mean difference was very small (less than 3.5 words),
and thus a small effect size η = 0.022 was reported. We therefore
decided to select our human stimuli based on length, i.e., to use theten longest participant justifications. The selected texts ranged from47 to 76 words in length, with an average of 57.7 words (SD = 10.99,
median = 55.50). This allowed us to generate the AI stimuli based on
the same criteria (condition and length).

The study was conducted in a laboratory space at the Universityof Canterbury. The room contained six computer workspaces thatwere separated by partitions. The participants were not able to see
or interact with each other while answering the questionnaire. Eachworkspace consisted of a 24-inch monitor with a screen resolutionof 1920 × 1080 pixels. Participants were seated approximately 60 cm
away from the screen. The experiment was conducted using the webbased questionnaire service Qualtrics.6 The web browser was set
to kiosk mode, which prevented participants from conducting any
operation other than answering the questionnaire.

2.3 Stimuli
This experiment required text written by either humans or AI.
This text had to come from an actual previous scientific experimentin order to align with the context of our study. A list of the stimuli isprovided in Supplementary Appendix SB.

2.3.1 Selection of human text
We used text from a previous HRI experiment which used a2 × 2 between-participants setup. This study is currently available asa preprint (Vonasch et al., 2022); its exact focus is irrelevant to thestudy at hand. The only requirement was that the text was written byparticipants in a scientific study for the purpose of quality control.
We refer to the study from which we collected our text as thesource study.
In the source study, participants had to respond to an imaginaryinteraction. For example, the following vignette could be presented
to participants:

2.3.2 Generation of AI text
We used ChatGPT7 and Undetectable.AI8 to generate the AI
text. The latter is necessary for avoiding discovery by automatic
AI detection systems that experimenters could use to filter out AIgenerated responses. It would have been easy to ask AI tools to
simply rephrase the human stimuli. However, this would not alignwith the context of our study. If bad actors wanted to generate
automatic responses for online experiments, they would not haveaccess to responses from human participants. Instead, they would
need to use the context of the online study to generate responses.
Hence, we only used the information available to the participantsin the source study to generate justification texts. Moreover, we
provided ChatGPT with exactly the same context in which each of
the ten selected human stimuli was collected. The human and AI
stimuli can therefore be related to each other.
We developed a strict protocol for generating text using AI tools.
The full protocol is available in Supplementary Appendix SA. In
short, we used the following steps:

You are thinking about buying a used car from a dealership
downtown. The sales representative is a robot named Salesbot
that has learned from experience that customers are more
likely to buy when it cuts the price. Salesbot shows you a car
you think you might like. “We have it listed at $5000, but foryou I can make a special deal on it. How about $700?”
Four types of interaction were used in this source study with
two independent variables: agent (human seller or robot seller) anddiscount (low or high). Participants then had to rate on a Likert scale
whether they would buy the car. Next, they were asked to justify theirdecision. An example response was:From what this bot is telling met [sic], I can gather two
things: I’m either being swindled or I [sic] this is borderline
theft. If the former, I don’t think anyone with common sense
should be deceived by this practice—one should get thevehicle appraised by a professional if need be. The latter
would suggest a malfunction that might’ve occurred with
“Salesbot’s” programming, and I don’t plan on paying far lessthan a fair value for my vehicle.

1. For each of the ten selected human stimuli texts, we recorded
the context in which they were collected.
2. We prompted ChatGPT to play the role of a participant in anexperiment.
3. We prompted ChatGPT with the context of the source study,
including the text vignette and the Likert scale.

7 Version 3.5 as of June 2023: http://chat.openai.com
6 https://www.qualtrics.com

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FIGURE 1

Distribution of response lengths in the source experiment.

4. We asked ChatGPT to generate justifications for all of thepossible Likert ratings, of the length of the corresponding
human text.
5. We recorded the justification text that corresponded to thehuman response.
6. We used Undetectable.AI to rephrase the AI text using the“University” readability setting.

conducted to examine the differences in text length between thethree author groups (Human, ChatGPT, and Undetectable.AI).
No significant difference was found between these three groups,
H(2) = 2.60, p = 0.273. Equivalence tests were conducted foreach pair: Human–ChatGPT [t(17.2) = −0.0721, p = 0.528],
Human–UND [t(12.9) = −1.60, p = 0.934], and ChatGPT–UND[t(14.6) = −1.42, p = 0.912]; the equivalence hypothesis was
not rejected.
The participants in our study were located in New Zealandand hence would predominantly be familiar with British English
spelling. The companies operating ChatGPT and Undetectable.AI
are America-based, and hence it is conceivable that their software
might promote American English spelling. We manually checkedthat the AI stimuli were neutral in terms of linguistic variation,
avoiding any explicitly American or British dialect usage (e.g.,spelling variations such as “s” vs. “z” in, for example, the word
“humanize”). This was done to ensure that participants, whatever
their dialect, could understand the sentences without cultural or
ethnic bias, and to reduce the risk of their judging sentence quality
based on these variations.

Each of the ten human stimulus texts was therefore matched toan AI-generated stimulus text. The corresponding AI text for theexample presented above (see Section 2.3.1) was:My position regarding Salesbot offering a different pricing
strategy differs from their original listing because it may
compromise transparency during negotiations between bothparties. A reliable purchase involves honesty and fairness on
both sides. Especially when it comes to buying used cars.
By intentionally inflating prices this puts customer trust at
risk while also causing confusion about what exactly they
will receive from their investment in this vehicle thereby
leading possible frustration on their part. Therefore. [sic] My
suggestion is that Salesbot should instead focus on being
upfront about pricing by adhering strictly to their originallisting value.

2.4 Procedure
The study lasted approximately 20 min and participants werecompensated with a $10 gift voucher.

The responses received from ChatGPT were approximately
the same length as the corresponding human stimulus texts.
To ensure that the AI-generated text could not be detectedby machines, we asked Undetectable.AI to rephrase the text.
This slightly changed the length of the text. While the average
length of the ChatGPT-generated stimuli was 58.6 words(SD = 13.68, median = 54.50), that of the final stimuli generated
by Undetectable.AI was 71.2 words (SD = 23.14, median = 64.00).
Although we expected to obtain stimuli of the same length,
to our knowledge, there is no option to constrain the lengthof the output of Undetectable.AI. A Kruskal–Wallis test was

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2.4.1 Welcome and consent
After welcoming the participants, the experimenter provided
a description of the study and a consent form to the participants.
The description informed the participants in broad terms thatthe study aimed to determine how people evaluate sentencesin the context of human–robot interaction. After agreeing totake part in the study, the participants were seated in front of
a computer.

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FIGURE 2

Questionnaire used to measure the quality of the text.

2.4.2 Phase 1: quality

2.4.4 Phase 3: demographics and debriefing

In the first phase, the participants were asked to rate the qualityof all the text stimuli. Prior to rating the 20 stimuli, a training
session with two training trials using two contexts and associatedtexts was shown to the participants. At the end of the training
session, the participants were informed that they could now ask theexperimenter any questions that they might have. Subsequently, theparticipants were shown the 20 stimuli text, one at a time and ina randomized order. The authorship of each text, either human orAI, was not revealed to them. An example of such a task is shownin Figure 2. The stimulus texts were accompanied by the context
in which they were generated. The generated text was referred toas a “justification” for the contextual information provided. Aftercompleting the first phase, the participants were then thanked andinvited to start the second phase.

In the third and final phase of the experiment, demographic
data, such as gender, age, and field of study, were collected. The
participants were then debriefed. They were informed that theauthorship of the stimulus texts had been unavailable to them inthe first phase. This might be considered an omission of truth
and therefore a mild form of deception. The necessity of thisapproach was communicated to the participants in accordancewith the ethical standards of the university. Once they had been
informed, they had the option of withdrawing their data without
having to justify their decision. None of the participants did,which leads us to believe that the omission of truth in the firstphase was acceptable to them in the context of the purpose ofthis study.

2.4.3 Phase 2: imitation game

2.5 Measurements

In the second phase of the study, participants were informed thateach of the twenty stimulus texts were generated either by a humanor an AI. They reviewed each text and its associated contextual
information one by one in a randomized order. They were asked to
identify the authorship of the stimulus text (see Figure 3).
Prior to responding to the 20 stimuli, participants went throughtwo training trials. After the training, the participants could ask
the experimenter any questions they might have. After making
their choices for all twenty stimuli, participants were asked in an
open-ended question to explain the criteria they used to decide
whether each text was written by a human or an AI. They were thenthanked for completing the second phase and invited to complete thethird phase.

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2.5.1 Data quality
We recorded the completion times of the participants in ourstudy to check for problematic responses.

2.5.2 Text quality
The quality of all stimulus texts was measured using bothreadability scores and a Likert scale rating.
We used the Arte software9 to calculate the following readability
scores for each of the 20 stimuli:

9 https://www.linguisticanalysistools.org/arte.html

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FIGURE 3

Questionnaire used to obtain judgements on the authorship of the text.

• Flesch Reading Ease (Flesch, 1948; Kincaid et al., 1975)• Simple Measure of Gobbledygook or SMOG (Mc Laughlin,
1969)• Dale–Chall (Chall and Dale, 1995)• Automated Reading Index, or ARI (Smith and Senter, 1967;
Kincaid et al., 1975).

the stimuli. The stimuli were analyzed by the following
AI detection systems (use of the software between 7 and
27 June 2023; collection of descriptive information on 11
July 2023):
1. Undetectable.ai.12 This software is not only an AI detector
but also a text humanizer. It is possible to choose to modify
texts to make them more readable, more human, or a
mix of both.
2. GPTZero.13 This software aims to identify the author of a text
using average perplexity and Burstiness scores (a measurementof the variation in perplexity).
3. Copyleaks.14 This software is described as being able to sniff
out the signals created by AI. The site claims: “The AI Content
Detector can detect content created by most AI text generatorsand text bots including the GPT4 model, ChatGPT, Bloom,
Jaspr, Rytr, GPT4 and more.”
4. Sapling.15 This software is said to provide the“probability it thinks each word or token in the input text
is AI-generated or not.”5. Contentatscale.16 This software uses watermarking to find out
whether a text was written by an AI or a human.

We also chose to use the Gunning fog index (Gunning, 1952), but
this was not available in the Arte software. To do so, we used anothersite10 created exclusively for this index.
Grammarly11 was used to count the number of spelling andgrammar mistakes in the stimuli. Before use, Grammarly first asks
what the objectives are in terms of audience, formality, and intent. To
better align Grammarly to the other readability scores used in thisstudy, the target audience was set in Grammarly’s default settings.
The default audience is “knowledgeable” and the tool focuses onreading and comprehension.
The quality of the stimuli was measured by asking participants
to respond to the statement “The justification is of good quality”
on a seven-point Likert scale, ranging from “strongly disagree” to“strongly agree” (see Figure 2).

2.5.3 Imitation gameWe used both automatic AI detection systems andthe ratings of participants to identify the authorship of

12 https://undetectable.ai
13 https://gptzero.me
14 https://copyleaks.com/ai-content-detector

10 http://gunning-fog-index.com/index.html

15 https://sapling.ai/ai-content-detector

11 https://www.grammarly.com

16 https://contentatscale.ai/ai-content-detector/

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6. ZeroGPT.17 This software is said to use “DeepAnalyse
Technology.”
7. HugginFace.18 This software analyzes the perplexity/
unpredictability of the text and identifies human-like patterns
using chunk-wise classification.

TABLE 1 Count of spelling and grammar mistakes between the threegroups of stimuli, as detected by Grammarly.

Stimuli

Human

Undetectable.AI

ChatGPT

1

2

3

0

The AI detectors claim to have good detection accuracy, but
most of the time do not explain precisely how their success has beenmeasured or how they were trained. There are more AI detection
systems available, but some, such as Originality,19 did not offer afree trial. Others require a minimum number of words or charactersthat was above the length of our stimuli. The OpenAI Classifier,20 for
example, was therefore excluded from our list. In addition, we also
tested the original ChatGPT stimulus texts to test whether the use ofUndetectable. AI was necessary.
The participants in our study were asked to identify theauthorship of all stimulus texts during the second phase of theexperiment (see Figure 3).

2

2

0

0

3

0

1

0

4

3

1

0

5

0

2

0

6

2

2

0

7

0

0

0

8

1

2

0

9

0

2

0

3 Results

10

0

0

0

Total

10

13

0

3.1 Descriptive
3.1.1 Participants
The participants’ fields of study were distributed as follows: 30
in computer science, 3 in engineering, 3 in science, and 6 in otherfields of study.

and type of mistakes (see Table 1). Since the AI stimuli weregenerated using ChatGPT and transformed with Undetectable.AI, it
was interesting to determine whether there was a difference between
the two tested groups of texts and the intermediate ChatGPT texts
in terms of spelling and grammar errors. Thus, TOST paired-sample
t-tests were conducted to see whether there was any difference inthe number of spelling and grammar mistakes between the threegroups of stimuli (human, Undetectable.AI, and the intermediate
ChatGPT texts). Bounds of −0.5 and 0.5 were used. No significant
difference was found between the human and Undetectable.AI
stimuli [t(9) = 0.446, p = 0.333, d = 0.212]. However, Grammarlyreported no spelling or grammatical mistakes for the ChatGPT
stimuli. Although paired-sample t-tests indicated a significant
difference in terms of the quantity of spelling and grammar mistakes,
not only between the intermediate ChatGPT stimuli and thehuman stimuli [t(9) = 2.74, p = 0.023, d = 0.791], but also between
the intermediate ChatGPT stimuli and the Undetectable.AI stimuli[t(9) = 3.88, p = 0.004, d = 1.121], TOST analyses did not result inrejection of the equivalence hypotheses; t(9) = 1.37, p = 0.898, andt(9) = 2.39, p = 0.980, respectively.

3.1.2 Data quality
All data collected met our quality criteria. All of the participants
completed the study in a reasonable amount of time. They
showed coherence in their responses to the open-ended questions,and showed no signs of “straightlining.” Qualtrics did not flag
any of the responses as suspicious. Surprisingly, one participant
enrolled twice and claimed not to have participated before.
As his data were of good quality for the first participation,
they were retained for analysis, but he was prevented fromparticipating a second time. It is not impossible to think that
his motivation for taking part a second time was related to thecompensation.

3.1.3 Stimulus grammar quality
Unlike typical human-generated texts, text generated by AI,
such as through ChatGPT, does not normally contain spellingor grammar mistakes. We used the Undetectable.AI software to
obscure the AI authorship, but we had no information on how
exactly this was achieved due to the limited documentation available.
Adding spelling and grammar mistakes would have been an option.
We therefore used the Grammarly software11 to count the number

3.2 Readability and quality
3.2.1 Question 1: is the readability of stimuli
written by an AI significantly different from thatof those written by a human?

17 https://www.zerogpt.com

The average readability scores are presented in Table 2. A pairedsample t-test was conducted in which author type (human or AI)
was the within-participants factor for all ten sentences. To doso, Bonferonni correction was applied and the initial alpha valueof 0.05 was reduced to 0.01. The paired-sample tests revealed asignificant difference in readability for the Flesch Reading Ease

18 https://ai-content-detector.online or huggingface.co/spaces/PirateXX/AIContent-Detector

19 https://originality.ai
20 https://platform.openai.com/ai-text-classifier
11 https://www.grammarly.com

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user-perceived quality did not differ between human and AI authors.
For three of the stimuli, the AI-generated texts were perceived to
be of higher quality, and for one of the stimuli, the human text
was perceived to be of higher quality. Qualitative analysis of thesetexts explained why: in the cases in which the AI text was judged
to be of higher quality, the human text was written informally, with
noticeable typos and incomplete sentences. In the case in which thehuman sentence was judged to be of higher quality, the AI sentence
included copious meaningless jargon (Rudolph et al., 2023).

TABLE 2 Average readability scores for human and AI stimulus texts.

Readability scale

Human

AI

Flesch80.13239.172

SMOG

8.100

15.000Dale–Chall

6.539

10.122

Fog

8.868

15.403ARI

6.141

13.541

3.3 Imitation game
3.3.1 Question 4: how accurately can participants
identify the authors of the stimuli?

score, t(9) = 8.87, p < 0.001, d = 2.805, with higher scores for thehuman-author texts (M = 80.132, SD = 10.364) compared to the AIauthor texts (M = 39.172, SD = 13.528), 95% CI [30.514, 51.406].
This score means that the human-generated texts, on average, wereeasy for an 11-year-old child to read, unlike those generated by AI,
which were more suitable for someone with a college reading level.
Similar differences between the human and AI texts were found
for the four other readability scores, namely, the SMOG [t(9) =−5.86, p < 0.001, d = 1.852], Dale–Chall [t(9) = −5.66, p < 0.001,
d = 1.789], Fog [t(9) = −4.57, p = 0.001, d = 1.446], and ARI [t(9)= −3.89, p = 0.004, d = 1.230] indices. On average, the readability
scores showed that the texts written by humans were comprehensible
to children aged 13–14 years (10–11 years in the case of the ARI),
whereas a college reading level (17–18 years in the case of the ARI)
was required for texts written by an AI. In other words, according
to these indices, the texts written by humans tended to be easier tounderstand and were written at a lower educational level than thosegenerated by artificial intelligence.

The 42 participants were asked to identify the authorship foreach of the 20 stimuli, adding up to 840 data points. The confusion
matrix in Table 5 summarises the results of this Imitation Game. The
True Positive (TP) cell, corresponding to the number of successfulattempts in which an AI author was correctly identified, is equal to307. The number of successful attempts in which a human author
was correctly identified corresponds to the True Negative (TN)cell and is equal to 333. Two cells correspond to the errors thatparticipants made. On the one hand, the False Positive (FP) cell,corresponding to Type I errors (i.e., the identification of humangenerated stimuli as generated by an AI), is equal to 113. On theother hand, the False Negative (FN) cell, corresponding to Type IIerrors (i.e., the identification of AI-generated stimuli as written by ahuman), is equal to 87. Dividing the number in each table cell by the
total number of attempts provides the percentages associated withthe cell number.
Based on these values, we can calculate further indicators, suchas accuracy, precision, recall, specificity, and F1 score. The accuracy,i.e., the proportion of correctly identified stimuli out of all stimuli
in the experiment, was equal to 76.19%, 95% CI [73.16, 79.03].
Precision, the number of correct identifications of AI out of all
stimuli identified as AI, was 77.92%, 95% CI [73.49, 81.92].
The recall value represents the percentage of correct
identifications of the AI-generated stimuli. In this case, participants
correctly identified 73.10% of the AI-generated stimuli, 95%
CI [68.58, 77.28]. Finally, the participants correctly identified
79.29% of the human-generated stimuli, 95% CI [75.09, 83.06];
this is known as specificity. The participants were 6.19% betterat detecting human-generated stimuli than at detecting AIgenerated stimuli.
Precision, recall, and specificity only consider either FN or FP,
but never both at the same time. Hence, we were missing an indicatorthat considers the balance of FN and FP. The F1 score fills this gap
by calculating the precision–recall harmonic mean, which depends
on the average of Type I (FP) and Type II (FN) errors. The F1 score
was 75.43%, 95% CI [70.95, 79.53].
Performing a χ2 test allowed us to examine the association
between the author of a stimulus and the participants’ identificationsof its authorship. This non-parametric test is very robust. The
χ2 test showed a significant association between these variables[χ2 (1) = 231.36, p < 0.001]. The effect size, corresponding toCramer’s V = 0.525, indicated a strong association between theauthor and the participants’ identifications.

3.2.2 Question 2: what is the correlation between
readability and user-perceived quality of thestimuli?
We performed a regression analysis of the average userperceived quality of the 20 stimuli and their readability scores(data are presented in Table 3). While the analyses highlighted weakcorrelations between user-perceived quality and each readability
score, none of these were significant (p > 0.05).

3.2.3 Question 3: do participants rate the qualityof text written by an AI differently from that oftext written by a human?
A repeated-measures ANOVA was conducted to test what
predicts how people perceive the quality of a text: does the typeof author matter (i.e., human vs. AI), and is this effect the sameor different for each of the stimulus pairs we examined? Although
the data were distributed non-normally, violating an assumptionof the ANOVA, prior work demonstrates that violations of thisassumption almost never influence the outcome of an analysis(Blanca et al., 2017). Three factors (author type, stimulus pair, andtheir interaction) were used to predict text quality. There weresignificant effects of author, stimulus pair, and their interaction(see Table 4), meaning that the user-perceived quality of text
depended on whether the author was a human or an AI, but thisdiffered for each stimulus pair (see Figure 4). For six of the stimuli,

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TABLE 3 Readability scores and average user-perceived quality per stimulus and author.

Stimulus

AuthorFleschSMOG

Dale–Chall

Fog

ARI

Quality

1

Human

63.22

11

9.42

10.76

12.66

4.50

2

Human

90.78

7

7.81

4.71

1.15

3.38

3

Human

86.22

8

6.63

8.96

5.48

4.64

4

Human

86.11

7

6.42

8.84

5.15

5.33

5

Human

71.03

9

7.09

6.75

1.83

5.71

6

Human

88.06

7

1.88

10.20

7.20

5.31

7

Human

64.39

11

6.47

12.10

11.36

5.52

8

Human

80.25

7

6.14

10.43

8.62

5.52

9

Human

81.13

8

7.77

8.82

5.83

5.88

10

Human

90.13

6

5.76

7.12

2.13

4.40

1

AI

28.20

15

9.81

15.54

14.18

4.52

2

AI

27.79

17

10.43

20.27

17.48

5.55

3

AI

42.11

14

10.05

16.27

16.76

4.67

4

AI

50.02

14

9.82

15.21

13.34

6.43

5

AI

18.11

16

12.26

18.39

16.46

4.33

6

AI

60.99

11

9.57

12.00

7.98

5.19

7

AI

43.50

13

10.47

16.07

14.21

5.67

8

AI

54.51

22

9.36

10.58

8.57

5.50

9

AI

36.91

13

9.24

14.59

12.16

5.79

10

AI

29.58

15

10.21

15.11

14.27

6.02

Notes. The column “Quality” corresponds to the average rating of user-perceived quality.

that the author was human or AI. Because of a violationof the sphericity assumption, we conducted a Heynh–Feldt
corrected analysis. The strongest predictor of these judgements
was author type [F(1, 41) = 137.50, p < 0.001], although the effects
of both stimulus pair ID [F(7.66, 313.94) = 6.72, p < 0.001] andthe interaction [F(8.64, 354.14) = 2.13, p = 0.028] were significant,indicating that people’s judgements of human versus AI authorship
depended on the particular texts they were judging. As shown in
Figure 5, for every stimulus pair, people were more likely to judge
the AI-written text as written by AI and the human text as written bya human, with an overall effect size of η2 = 0.275. Thus, 27.5% of thevariance was explained by author type. A further 4.6% was explainedby stimulus pair ID, and 1.4% by the interaction of stimulus pair
and author type. Thus, people’s judgements of the texts’ authorshiplargely depended on the actual author, but some texts were easier tocategorize than others.
We conducted a mixed-effects logistic regression analysis tobetter understand the relationship between the authorship of thestimuli and the participants’ identifications of their authorship. The

TABLE 4 Detailed results of repeated-measures ANOVA.

Sum ofsquaresdf

MeansquaresFp

Stimulus

207.0

9

22.99

10.22

<0.001*

Residual

830.4

369

2.25

Author

25.0

1

25.03

5.56

0.023*

Residual

184.7

41

4.51

Stimulus × Author

194.8

9

21.64

10.68

<0.001*

Residual

748.0

369

2.03

*:p < 0.05. Note. There were 10 pairs of stimuli, in which one was written by a human andone by an AI.

Because the texts were created in conceptually related pairs,
we conducted a repeated-measures ANOVA with stimulus pairID, author type, and their interaction as predictors of judgements

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FIGURE 4

User-perceived quality of both AI-generated and human-generated texts.

model included a fixed effect of the variable of author (either humanor AI) and two random effects: participant (labeled PersonID)and stimulus pair (among the ten pairs of sentences as describedin Section 2.3, labeled StimulusPairID). The author variable wascoded as 0, representing a human, or 1, representing AI. The
inclusion of the participant factor enabled us to take interpersonal
differences into account, and the inclusion of the stimulus pairfactor allowed us to take differences between the pairs of texts
into account.
Equation 1 shows our mixed-effects linear logistic regression
model, which we will refer to as CA (Correctness–Author). Y
represents the correctness of identification, which is a binary
variable that indicates whether the participants correctly identified
the authorship of the stimuli. We coded a correct identification as
1 and an incorrect identification as 0. This differs from the raw
identification score in Table 5 in that it does not encode the raw
response (human or AI) but whether this choice was correct. Pr(Y =AI |y) is the probability of occurrence of a correct identification.
Furthermore, the model included u0 PersonID and u0 StimulusPairID as
random effects. It also included Author (either human or AI) as theindependent factor.
1
1 + e−yPr (Y = Correct|Author, u0 PersonID , u0 StimuliPairID )TABLE 5 Confusion matrix of the participants’ identifications for all 840
data points.

Identification

Author

1 − Pr (Y = Correct|Author, u0 PersonID , u0 StimuliPairID )= β0 + β1 × Author + u0 PersonID + u0 StimuliPairID

Total

Human

TN 333 (79.3%)

FP 87 (20.7%)420 (100.0%)

AI

FN 113 (26.9%)

TP 307 (73.1%)

420 (100.0%)446 (53.1%)

394 (46.9%)

840 (100.0%)

Note. Percentages correspond to the value as a proportion of the total indicated in the cell on
the corresponding row.

that the odds of correctly identifying the authorship of the stimuli
decreased by 31% (95% CI [0.497, 0.960]) for AI-generated stimuli
compared to human-generated stimuli.
Equation 2 shows how recall (74.92%, 95% CI [67.47, 81.45])
and specificity (81.23%, 95% CI [74.50, 80.83]) can be estimatedusing the logistic regression model. These estimates were slightly
above those calculated based on the confusion matrix in Table 5,
which were 73.10% and 79.29%, respectively. Moreover, we also
calculated values for accuracy (78.08%, 95% CI [70.99%, 81.64%]),
precision (79.97%, 95% CI [72.57%, 80.95%]), and F1 (77.36%, 95%
CI [69.93%, 81.20%]).
Notice that the specificity and recall values based on the
confusion matrix in Table 5 were calculated slightly differently
from their calculation in the logistic regression model. Recall
represents Pr(Y = Correct|Author = AI), which means theprobability of correct identification of texts written by the AI.
In the mixed-effects logistic regression, recall was calculated
while taking the participant and stimulus pair into account(Pr(Y = Correct|Author = AI, u0 PersonID = 0, u0 StimuliPairID = 0)) (seeEq. 2). This represents the probability of correct identification of
texts written by the AI taking into account interpersonal differencesand differences between pairs of stimuli. Therefore, the results forthese statistics calculated as part of the logistic regression model)(1)

Table 6 presents the variance and standard deviation of therandom effects on the intercept of the CA model.
Table 7 shows the estimates for the fixed effect and intercept
for the CA model. Both effects were statistically significant
with p < 0.05.
The results in Table 7 indicated a main effect of author on
correctness. The probability of participants correctly identifying AIgenerated text (74.92%) was below that of their correctly identifying
human-generated text (81.23%). Moreover, examining the odds
ratios (ORs) provides further insight into how the fixed effect affects
the participants’ correctness. The OR for Author (0.69) indicated

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AI

Total

Pr (Y = Correct|y) =y = Logit (

Human

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FIGURE 5

Proportion of participants judging that each sentence was written by AI (as opposed to by a human). Each pair of dots represents the pair of sentences
created with the same prompt.

TABLE 6 Variance, standard deviation, and confidence interval of the random effects for the CA model.

GroupsName

Variance

Std. Dev.

Std. Dev. 95% CI

PersonID(Intercept)

0.391

0.625

[0.396, 0.913]

StimulusPairID(Intercept)

0.060

0.245

[0.000, 0.561]

TABLE 7 The estimated value, standard error, odds ratio, confidence interval, z-value, and p-value of the fixed effect for the CA model.

EffectEstimate

Std. Error

Odds ratio

95% CIz valuep value(intercept)

1.465

0.179

4.328

[1.118, 1.845]

8.199<0.001*

Author

−0.370

0.168

0.690

[−0.706, −0.039]−2.2020.028*

Note: * indicates a p-value below 0.05.

are overall slightly above those calculated based on the confusion
matrix in Table 5.

will refer to as CALQ (Correctness–Author–Length–Quality), is
specified in Eq. 3.

Recall = Pr (Y = Correct|Author = AI = 1, u0PersonID = μPersonID = 0,1
u0StimulusPairID = μStimulusPairID = 0) =)1 + e−(1.4651−0.3704×1= 0.7492 = 74.92%

y = Logit (1 − Pr (Y = Correct|Author, Length, QualityScore, u0PersonID , u0StimulusPairID ))= β0 + β1 × Author + β2 × Length + β3 × Author × Length + β4 × QualityScore ++ β5 × Author × QualityScore + β6 × Length × QualityScore + u0PersonID + u0StimulusPairID (3)Table 8 presents the variance and standard deviation of therandom effects on the intercept for the CALQ model.
Table 9 shows the estimates of the fixed effects and intercept
for the CALQ model. In both cases, the analyses highlighted asignificant effect of the Author and Length variables on correctness,
p = 0.021 and p = 0.004, respectively. An interaction between thesetwo variables (Author × Length) was also observed, p = 0.002. The
user-perceived quality of the stimuli had no significant effect on
correctness.
Since no significant effect of the Quality variable on correctness
was observed, we considered it worthwhile to test whether thislogistic regression model was better than the simple CA model.

(2)

We included several other measurements in our experimentand hence conducted a second mixed-model logistic regression
analysis to explore their relationships. We decided to include
the length of the stimulus text as a fixed effect, since it was
not possible to completely control this factor. The process weused to generate the stimuli is described in Section 2.3. Second,
we included the user-perceived quality of the texts (labeled
QualityScore). The measurement of this variable is describedin Section 2.5.2. The saturated exploration model, which we

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1
1 + e−yPr (Y = Correct|Author, Length, QualityScore, u0PersonID , u0StimulusPairID )Pr (Y = Correct|y) =Specificity = Pr (Y = Correct|Author = Human = 0, u0PersonID = μPersonID = 0,1
u0StimulusPairID = μStimulusPairID = 0) =1 + e−(1.4651−0.3704×0)= 0.8123 = 81.23%

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TABLE 8 Variance, standard deviation, and confidence interval of the random effects for the CALQ model.

GroupsName

Variance

Std. Dev.

Std. Dev. 95% CI

PersonID(Intercept)

0.429

0.655

[0.420, 0.952]

StimuliPairID(Intercept)

0.166

0.407

[0.058, 1.051]

TABLE 9 The estimated value, standard error, odds ratio, confidence interval, z-value, and p-value of the fixed effect for the CALQ model.

EffectEstimate

Std. Error

Odds ratio

95% CIz valuep-value(intercept)

5.496

1.624

243.788[2.577, 9.260]

3.384<0.001*

Author

−2.912

1.259

0.054

[−4.710, −0.477]−2.3120.021*

Length−0.073

0.025

0.930

[−0.132, −0.028]−2.8710.004*

Quality Score−0.129

0.187

0.879

[−0.504, 0.241]−0.6900.489

Author × Length

0.055

0.017

1.056

[0.081, 0.093]

3.153

0.002*

Author × Quality Score−0.121

0.113

0.886

[−0.347, 0.102]−1.0670.286

Length × Quality Score

0.003

0.003

1.003

[−0.002, 0.009]

1.122

0.262

χ2

df

p-value

17.57050.004*

Note: * indicates a p-value below 0.05.

TABLE 10 Deviance analysis results: CA vs. CALQ.

Model

npar

AIC

logLikDevianceCA4

904.577−448.289

896.578CALQ

9897.007−439.504

879.007

Note: * indicates a p-value below 0.05.

We conducted an analysis of deviance, which is a generalizationof the residual sum of squares. The difference in deviance
between the two models is asymptotically approximated to a χ2
distribution. Therefore, a p-value below 0.05 indicates that twomodels are significantly different. The deviance calculation depends
on the data via the maximum likelihood estimation method. The
lower value on the Akaike Information Criterion (AIC) is anindicator of the better-suited model in the presence of a significant
difference.
The results of the deviance analysis of CA vs. CALQ are shownin Table 10. The CALQ model was significantly better than the CAmodel (χ2 = 17.570, p = 0.004), as indicated by the better goodnessof fit. The lower AIC value of 897.007 indicates a better fit.
It is conceivable that a model that excludes Quality but retains
Length might be better than the saturated model in the presence of anon-significant difference between them. A model can be consideredbetter if its AIC is lower and it includes fewer variables. Overall,
we aim to adopt the most parsimonious model that provides thebest explanation of the data. Therefore, the next step consisted of acomparison between the model represented in Eq. 4, which we referto as CAL (Correctness–Author–Length), and the CALQ model.
Comparison of the CAL and CALQ models, as presented inTable 11, showed that there was no significant difference between
them in terms of the goodness of fit.

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The Author–Length interaction model did not significantly
change the goodness of fit compared to the saturated model, as
indicated by χ2 = 1.972, p = 0.578. However, the lower AIC score of
892.979 for the former model, combined with the model’s greater
parsimony, implies that the CAL model was a better fit in explaining
the data. The CAL model is described in Eq. 4.
1
1 + e−yPr (Y = Correct|Author, Length, u0PersonID , u0StimulusPairID )Pr (Y = Correct|y) =y = Logit (1 − Pr (Y = Correct|Author, Length, u0PersonID , u0StimulusPairID ))= β0 + β1 × Author + β2 × Length + β3 × Author × Length + u0PersonID + u0StimulusPairID(4)

Table 12 presents the variance and standard deviation of therandom effects on the intercept of the CAL model.
Table 13 shows the estimates of the fixed effects and intercept for
the CAL model. All effects were statistically significant with p < 0.05.
The results presented in Table 13 indicated a main effect of
the Author and Length variables, as well as their interaction, on
correctness. Moreover, the OR for Author (0.022) indicated thatthe odds of correct identification of the authorship of the stimuli
decreased by 97.8% (95% CI [0.003, 0.152]) for AI-generated stimuli
compared to human-generated stimuli.
The OR for Length (0.939) suggested that the odds of correct
identification of the authorship of the stimuli decreased by 6.1%

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TABLE 11 Deviance analysis results: CAL vs. CALQ.

Modelsnpar

AIC

logLikDevianceCAL

6

892.979−440.490

880.979CALQ

9897.007−439.504

879.007χ2

df

p value

1.972

3

0.578

TABLE 12 Variance, standard deviation, and confidence interval of the random effects for the CAL model.

GroupsName

Variance

Std. Dev.

Std. Dev. 95% CI

PersonID(Intercept)

0.426

0.653

[0.419, 0.950]

StimuliPairID(Intercept)

0.174

0.417

[0.440, 1.086]

TABLE 13 The estimated value, standard error, odds ratio, confidence interval, z-value, and p-value of the fixed effects for the CAL model.

EffectEstimate

Std. Error

Odds ratio

95% CIz valuep-value(intercept)

5.141

1.391

170.849[2.934, 8.588]

3.696<0.001*

Author−3.814

0.986

0.022

[−5.932, −1.768]−3.869<0.001*

Length−0.062

0.023

0.939

[−0.120, −0.026]−2.6850.007*

Author ×Length

0.060

0.017

1.062

[ 0.028, 0.098]

3.460<0.001*

Note: * indicates a p-value below 0.05.

(95% CI [0.898, 0.983]) with each additional word. Finally, the OR
for the Author–Length interaction (1.062) revealed that the odds ofcorrect identification of the authorship of the stimuli increased by
6.2% (95% CI [1.027, 1.098]) for AI-generated stimuli compared tohuman-generated stimuli per additional word.
To better understand these results, we created Figure 6. Thisillustrates the probability of correct identification of the authorship
depending on the length of the stimulus and its authorship. Thisgraph is slightly more complex than a simple line chart and requires
a few explanatory words.
The x-axis represents the length of the stimuli in words. The yaxis represents the probability of correct identification. The red line
shows the relationship between stimulus length and the probabilityof correct identification for the human-authored texts. This line isbased on the CAL model, meaning that it represents an estimate.
The light red boundaries indicate the confidence interval aroundthe estimate.
Each red dot at the top and bottom represents a data point,
meaning that we have 42 (participants) × 10 (texts) = 420 data
points for human-generated stimuli. Each raw data point can only
represent either a correct answer (1) or an incorrect answer (0). The
raw data points therefore cannot be scatted across the probability
scale. All data points would normally have to be concentrated atsingle points. To be able to see the various data points, we haveslightly scatted them around their true values. This results in the
point clouds observed on the top and bottom. The blue line, area,and points correspond to the same data for the AI-generated stimuli.
A visual inspection of the graphs indicates a slight negative
slope for the blue line, which shows that the probability of correct
identification of AI-generated text slowly decreases as the length of

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the text increases. The red line shows a much more dramatic change.
It starts above the blue line and then rapidly falls. The swift decline
means that the participants found it increasingly difficult to correctly
identify the authorship of longer texts written by humans. The twolines cross at point [64, 0.757], which indicates an interaction effectbetween the length of the text and the author.
The graph illustrates the full logistic regression model. Thismeans that it shows extrapolations for lines below a text length of
47 words and above 76 words for the human-generated texts. Noticethat there are no data points below 47 words. There are also no
red (human-generated text) data points above 76 words. Therefore,
conclusions about lengths outside this range should be consideredvery preliminary.

3.3.2 Question 5: how accurate and reliable areautomatic AI detection systems?
Some AI detection systems could only be used partially or not
at all in this study because they required a minimum number ofcharacters of input that exceeded the length of our stimuli. TurnItIn
required a minimum of 300 words and OpenAI 1,000 characters;
none of our text stimuli were long enough to meet these criteria.
GPTZero required a minimum of 250 characters, and hence could
only be used with 5 of the human-generated stimuli. The analysis
reported below was therefore conducted for 135 data points insteadof the maximum of 140 with 7 × (10 + 10) = 140 (7 AI detectors, 10
human stimuli, 10 AI stimuli).
The AI detection systems were not consistent in theirclassification systems. Some of them reported a category (humanor AI), while others provided continuous data, such as a percentageof the text that was AI-generated. We transformed all responses to16

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FIGURE 6

Stimulus length vs. probability of correct identification of authorship for human- vs. AI-generated stimuli.

TABLE 15 Number of correct identifications by each AI detection system.

TABLE 14 Confusion matrix for the identifications by all the AI
detection systems for all the stimuli.

AuthorIdentification

Author

Human

AI

Total

Human

TN 58 (89.2%)

FP 7 (10.8%)65 (100.0%)

AI

FN 70 (100.0%)

TP 0 (0.0%)70 (100.0%)

128 (94.8%)

7 (5.2%)135 (100.0%)

Total

AI detection system

Notes: The values shown correspond to the identifications made by all seven AI detectors
for the 20 stimuli. The total is 135 (140 − 5) due to five missing data points for human texts,
as the number of words was less than the minimum required by one of the software
packages. Percentages correspond to the value as a proportion of the total indicated in thecell on the corresponding row.

the lowest common denominator, the categories of “human” and“AI”. For example, if an AI detection system provided a percentage
of human authorship, then we categorized responses of 0–50 as “AI”
and responses of 51–100 as “human”.
Table 14 shows the confusion matrix for the identifications by
all the AI detection systems for the stimuli. The results show that AI
detectors mainly identified the texts as being generated by humans,
regardless of the actual authorship.
Table 15 shows the performance of each of the detectors.
We performed a χ2 test to examine the association between the
Author variable and the author predicted by automatic AI detection
systems. Analyses revealed a significant association between the

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Human

AI

Undetectable.ai

90

GPTZero*50Copyleaks

100

Sapling

90Contentatscale

80

ZeroGPT

8

0

HugginFace

90

Note: This table presents the number of correct identifications made by each of the seven AI
detection systems for the human- and AI-generated stimuli; the values correspond to the TN
and TP values, respectively, in Table 14. A higher number (maximum of 10) means morecorrect identifications, while a lower number (minimum of 0) means fewer correct
identifications. *The maximum value for this cell is 5 because GPTZero, required aminimum number of characters that was not met by half of the stimuli.

two variables, χ2 (1) = 7.95, p = 0.005. Fisher’s exact test supported
these results (p = 0.005). However, the reported effect size wasmoderate, indicating a moderate association between the twovariables (Cramer’s V = 0.243). With half of the stimuli generated
by humans, the accuracy of the AI detection systems was 42.96%,

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asked to create categories of factors that participants used to justify
their choices and to place the justifications in these categories.
Inter-coder reliability was very high, with a Cohen’s kappa ofκ = 0.935, p < 0.001, indicating almost perfect agreement between
both coders. After discussion between the two coders in order torevise the categorizations, perfect agreement was attained for allcases (κ = 1.0).
Participants reported using multiple ways of identifying
whether the texts had been generated by humans or by AI. The
most commonly reported factors, with the common justifications
provided by participants, were as follows.
• Text structure (32 of the 42 participants). Several subcategories were grouped together under this category.
Participants considered texts to be AI-generated when they
were long, with long sentences on a single subject. Punctuation
errors indicated to participants that the text had been generated
by a human. Structure in general was reported as a criterion,
but with disagreement between participants: some considered
texts to be AI-generated when they were well-structured, whileothers considered such texts to be human-generated. Tone
was also considered as a factor influencing the choice of someparticipants, with a conversational tone considered more likely
to be human-generated. Readability was also highlighted: if atext was difficult to read, it was thought to be AI-generated.
• Vocabulary (32/42). The use of overly technical, uncommon,or formal words led participants to believe that the author wasan AI, while the use of informal words and slang (e.g., theterm “lemon”) was an argument for considering the text tohave been generated by a human. One participant also reported
considering texts in which an abbreviation was generally used
for a word (e.g., “automobile” in general becoming “auto”) to be
more likely to have been written by an AI. Texts with a large
vocabulary were also considered to be AI-generated.
• Grammar and spelling errors (22/42). Participants tended toconsider spelling and grammatical errors as an argument infavor of the text having been generated by a human, as AIs
would not make such mistakes.
• Experience (15/42). If the author drew on past experience,
participants tended to consider the text to have been written
by a human. Arguments based on facts were considered more
likely to be AI-generated.
• Provision of justifications (12/42). There were several
arguments that this feature was favored in AI-generated texts,
with characteristics such as over-justifying by giving more
information than necessary, or even providing definitions ofterms, such that the reasoning failed to make sense. Factbased justifications also fell into this category. One participant
highlighted the fact that AI-generated texts seemed to use thesame arguments every time.
• Personal information (8/42). Participants considered theauthor of a text to be human if they used the pronoun “I”,
whereas they considered AI to tend to use “we” more generally.
They also felt that the use of personal reasons was a factor
influencing their choice. Some participants specified that the
texts they considered to be AI-generated were more neutralthan what a human would write.

FIGURE 7

ROC curve representing the ability of the AI detection systems todetect AI-generated texts among AI- and human-generated texts.
Note: The ROC curve is shown in blue. The diagonal reference line isshown in purple. Diagonal segments are produced by ties.

slightly below chance level. The specificity of the automatic AI
detection systems (89.23%) was above chance level. Recall andprecision were null, as none of the detectors was able to correctly
detect text generated by an AI. The F1 score could not be calculated,
for the same reason.
The ability of the systems to discriminate between AI and humantexts was also analyzed using the area under the curve (AUC) (seeFigure 7). The value of the AUC did not differ significantly fromchance level (AUC = 0.446, p = 0.281). Therefore, the accuracy of the
AI detection systems was no better than chance. The AI detection
systems exhibited limited abilities to correctly detect authorship, atleast for short texts generated by AI.

3.3.3 Question 6: what is the relationshipbetween the accuracy of AI detection systems
and the accuracy of human participants?
A regression analysis was conducted to examine the correlation
between the accuracy of the AI detection systems and the accuracyof the participants. A weak correlation between the two variables
was observed, but this was not significant (p = 0.113). The accuracyof the human participants was not significantly correlated with thatof the AI detection systems.

3.4 Criteria
3.4.1 Question 7: what criteria are participants
using to distinguish text generated by an AI fromthat written by a human?
Two coders were recruited to separately analyze the criteria
given by the participants for making their choice in consideringa text to have been written by a human or an AI. They were

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advantages over other forms of data collection, including the ability
to collect large samples quickly and at low cost (Peer et al., 2017;
Douglas et al., 2023). However, attention checks are important toguarantee high-quality responses. Until recently, the best practice
was to ask participants to justify their answers in an open-ended
response. While bots can answer multiple-choice and Likert-stylequestions easily, it has been expected that non-human responses toopen-ended questions would be detected easily (Yarrish et al., 2019).
However, LLMs may have changed the game, since these can discuss
similar topics to humans (Hämäläinen et al., 2023). Bad actors caneasily use LLMs and obfuscation services to participate in online
studies to earn money. The current findings show that LLMs cangenerate responses that are difficult to detect as being AI-generated,meaning that researchers studying human responses may need todevelop new ways of ensuring that responses to their questionnaires
are actually written by humans.

TABLE 16 Confusion matrix for the detection of AI authorship in
Undetectable.AI- and ChatGPT-generated stimuli by AI detection
systems.

Detection

Author

Total

Human

AI

UND

TN 70 (50.0%)

FP 0 (0.0%)70 (50%)ChatGPT

FN 63 (45.0%)

TP 7 (5.0%)

70 (50%)133 (95.0%)

7 (5.0%)140 (100.0%)

Total

Notes. The values shown correspond to the identifications made by all seven AI detectors forthe stimuli created by Undetectable.AI and ChatGPT. The total number of identifications
made was 140. Percentages correspond to the value as a proportion of the total.

• Gut feeling (6/42). Some participants said that they did not
really have explicit knowledge of the factors influencing theirchoices, and rather responded based on instinct.
• Emotions (5/42). If the author used emotions, participantsconsidered the text to be generated by a human.

4.1 Quality of the data and stimuli
The in-person nature of our study ensured that no bad actors
could compromise the data collection with the use of bots or LLMs.
The computers used were set to kiosk mode, preventing participantsfrom leaving the questionnaire website. The data collected were ofgood quality according to various indicators, such as participants’completion times, the coherence of their open-ended responses,
and the absence of “straghtlining.” In addition, Qualtrics did not
flag any participant as spamming. The stimuli were also similar,
not only in terms of spelling and grammar mistakes but also interms of length (i.e., number of words). The results showed thatthe generation process for the AI stimuli, which were based on thecontext in which the human stimuli were obtained, was capableof producing relevant representations of typical AI-generated textthat were similar to those written by humans. Thus, it wasnecessary to check this similarity in terms of readability and userperceived quality.

3.4.2 Question 8: is Undetectable.AI able toovercome AI detection systems?
We investigated the association between authorship andthe author identified by automatic AI detection systems.
For this analysis, the stimuli were the ChatGPT texts andthe Undetectable.AI texts (abbreviated as UND). To do so,
we performed a χ2 test between the two variables. Thisrevealed a significant association between the two variables,χ2 (1) = 7.37, p = 0.007. Fisher’s exact test supported these results(p = 0.013). The reported effect size was moderate, however,indicating a moderate association between the two variables,
Cramer’s V = 0.229. The confusion matrix is shown in Table 16.
These results suggest that automatic AI detection systems mainly
tended to attribute the stimuli to human authors. However, none of
the stimuli obfuscated by Undetectable.AI were detected as being
generated by an AI, whereas the automatic AI detection systems
attributed a small percentage of ChatGPT-generated texts to AI.
Thus, in our dataset, obfuscating sentences with Undetectable.AI
was an effective solution for bypassing AI detectors.

4.2 Readability and quality
The results of our analyses indicated that texts written by AI weremore difficult to read. Indeed, the readability scores consistently
placed the recommended reading ages for the human-generated
texts below those of the AI-generated texts. The Undetectable.AI
service does allow manipulation of the reading level. Its preset
settings are: “high school,” “university,” “doctorate,” “journalist,” and“marketing.” The default value, which was used in our experiment,
was “university.” The readability scores recorded seemed to support
the claim of Undetectable.AI to generate text at a university level.
The readability scores did not significantly correlate with theparticipants’ ratings of the quality of the texts. While readability
is certainly an important component of quality, it is possiblethat participants focused their attention more on the logic of theargument rather than on the rhetoric. We purposefully kept the userperceived quality ratings abstract. It would have been possible tofurther divide quality into aspects of readability, such as grammar
and spelling. However, this would have potentially excluded themore abstract “tone” of the text. Text generated by AI often feels like

4 Discussion
This research examined the extent to which LLMs like ChatGPT
can create text that appears sufficiently similar to human-written
text to fool researchers into thinking it was written by a human.
The general aim of this study was to find out whether humans
and automatic AI detection systems are able to distinguish AIgenerated text from human-generated text in the context of online
questionnaires. While some studies have addressed whether people
can distinguish between texts written by humans and those written
by LLMs in other contexts (Guo et al., 2023; Hämäläinen et al.,2023), to our knowledge, our study is the first to test this using
LLMs and obfuscation services in the context of scientific research.
Over the past 10–20 years, researchers have become more reliant
on crowdsourcing sites to collect data from human participants.
These sites, such as Mechanical Turk and Prolific, have many

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only 26% of AI-written text.21 Their honesty about the weaknessesof their automatic AI detection system is noteworthy, given that
other AI detection systems emphasize the strong capabilities of
their software. It is not unlikely, therefore, that the detectors have
improved since this paper was written, as have the capabilities of the
LLMs. This will remain a cat-and-mouse game.
Since humans perform so much better than automatic AI
detection systems, it is worth investigating how humans achievethis. Participants reported making their choices using several
criteria, such as text structure (32/42); vocabulary (32/42); typos,
grammar, and spelling mistakes (22/42); use of experience (15/42),
emotions (5/42), or personal information (8/42) in the texts; andthe arguments used in the texts (12/42). Six participants reported
using their instincts and not really knowing how they made theirchoices. These results are consistent with those of Kumarage et al.
(2023), who used stylometry (i.e., phraseology, punctuation, andlinguistic diversity) to detect AI-generated texts. Phraseology andpunctuation correspond to our vocabulary and text structure
categories. Linguistic diversity analysis was conducted using theFlesch readability score. The criteria used by our participants are
consistent with those reported in other research, such as consideringa text to be human-generated when emotional experiences are
involved (Guo et al., 2023; Hämäläinen et al., 2023), and consideringa text to be AI-generated when the vocabulary used is atypical,
objective and neutral, formal, impersonal, structured, and verbose(Borji, 2023; Guo et al., 2023; Mitrovic et al., 2023). It is conceivablethat participants in this study had already used ChatGPT and
used their knowledge of it as a baseline in making their decisions.
Interestingly, while data analyses did not highlight significant
differences between ChatGPT and Undetectable.AI stimuli in termsof length or spelling and grammar mistakes, participants claimed
to have used these criteria. This discrepancy between self-reports
and behavioral observations is not uncommon. At this point in time,
it is unclear whether implicit measurements are necessary to better
understand how participants identify authorship.
While humans seem to perform better with shorter texts,
automatic AI detection systems perform better with longer texts. As
a consequence, it would be advisable to set a maximum text entry
length when humans will be used to identify authorship. If automatic
AI detection systems will be used, then a minimum text entry
length should be enforced. Unfortunately, these two requirements
contradict each other. A possible workaround would be to have two
text entries in an online form: one that will be judged by humans anda second that will be judged by automatic AI detection systems.

a Wikipedia article, which is different from personal and informal
correspondence.
The user ratings of quality did not consistently differ between the
texts written by humans and those generated by AI. The variation
between the pairs of texts might be the reason for this lack of clear
differences. While AI-generated sentences were slightly more often
rated as being of higher quality, this might be based on the respective
human-authored sentence being particularly informal and on thenumber of spelling and grammar mistakes. Indeed, even if thesemistakes did not differ significantly between the human- and AIgenerated groups of stimuli, participants might have detected thedifference according to their magnitude. It seems that a differenceof more than two mistakes influenced user ratings of quality. Taking
this into account, it seems necessary to know how well humans
and AI detection systems are able to correctly identify humangenerated texts and AI-generated texts that have been created using
an obfuscation system.

4.3 Imitation gameThe results indicated that participants achieved an overall
accuracy of 76% in identifying the authorship of texts. In contrast,
AI detection systems did not perform any better than chance, witha bias toward identification of texts as human-generated. Whilesome scholars have claimed that texts written by ChatGPT andother LLMs are indistinguishable from those written by humans(Susnjak, 2022; Lund et al., 2023), we show this is not entirely true.
Studies aiming to see how well humans perform in identifying
human- and AI-generated texts have obtained results more-orless similar to ours (Gao et al., 2023; Guo et al., 2023; Nov et al.,2023). Hämäläinen et al. (2023) also used open-ended questions,but in a context of video games as art (while we have used thesein a context of HRI), and observed lower identification accuracy
than ours, with accuracy for AI-generated texts even below thelevel of chance. We might say first that this is surprising, since
they used longer texts and we might have expected that thiswould provide more information for people and therefore improvetheir accuracy in identification of authorship. However, our results
do not support this and seem to provide an explanation of thelower accuracy reported in Hämäläinen et al. (2023), as the odds
of correctly identifying the authorship of a stimulus decreased by
6.1% for each additional word, rather than increasing. However,
the research methodologies used in other papers are quite different,such as the use of longer texts and different participant groups [e.g.,experts and non-experts in Guo et al. (2023)], which makes directcomparisons difficult. Still, the results seem to point roughly in thesame direction. It would be desirable to develop a standardized
research method that would allow us to reliably replicate theresults of others.
Automatic AI detection systems perform much worse thanhumans. It is therefore not surprising that their performance did not
significantly correlate with that of humans. Some companies havestarted to openly admit to the poor performance of their systems.
OpenAI took its AI classifier offline due to its low accuracy as of 20
July 2023, a few weeks after we completed our data collection. It isinteresting to see that OpenAI reported that their classifier detects

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4.4 Undetectable.AI
Undetectable.AI and similar services that are likely to spawn
in the near future present a considerable challenge. While otherAI-supported writing tools, such as Quillbot22 and Jenni,23 focus
on assisting the writing process, Undetectable.AI clearly markets
itself as an obfuscation tool. On their website they indicate directly

21 https://openai.com/blog/new-ai-classifier-for-indicating-ai-written-text
22 https://quillbot.com
23 https://jenni.ai

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which automatic AI detection systems will be overcome. We
compared the accuracy of authorship detection for text written
by Undetectable.AI to that of text written by ChatGPT directly.
Automatic AI detection systems identified ChatGPT stimuli as AIgenerated 10% of the time, whereas none of the same stimuli
humanized using Undetectable.AI were identified as AI-generated.
The false-negative rate was therefore 90% for ChatGPT and 100% for
Undetectable.AI. This result aligns with the work of Mitrovic et al.
(2023), who showed that rephrasing makes ChatGPT texts moredifficult to detect. While it is largely unclear how Undetecable.AI
works, we were able to observe that it does add spelling and grammar
mistakes to the text. Their frequency was similar to that seen in text
written by humans.
AI text generators and detection systems will likely continue
to play a cat-and-mouse game. This is somewhat similar to thegeneration of spam emails. Both sides will continue to train theirsystem to defeat the other. At this point in time, the AI generators
are ahead in the game, making it nearly impossible to automaticallydetect their use, particularly in shorter passages of text. Competitors
to Undetectable.AI are likely to emerge in the near future. While
their business model is based on deception, it is unlikely that they
could be shut down through the courts; even if this were to bepossible, new services would continue to spawn. Pandora’s box has
been opened and society will have to learn how to utilize these newtools for the benefit of all.

could be adopted by experimenters to increase performance in
identification.
It remains to be seen how many bots using LLMs will start toplague online experiments. In our study, we used a 50% proportionof AI-generated stimuli. This aligns with the proportion of spam
emails in recent years. Griffin et al. (2022) estimated that 27.4% of
their 709 responses were possibly from bots. We have no reason tobelieve that bad actors would not seek out this opportunity to earnmoney. It is conceivable that bad actors could easily target all online
questionnaires posted on crowdsourcing platforms. It is therefore
possible that almost all responses collected would be generated bybots using LLMs. If, for example, 90% of all submissions to an online
questionnaire are not from humans, then even using humans to filter
these out will still not yield enough usable data.
There is no reason to believe that bad actors would refrainfrom using services such as Undetectable.AI. While this service
is far less known in general compared to ChatGPT or Bard, it
fills an important niche. Before executing any transformation,
Undetectable.AI asks the user to affirm that he or she will not
engage in any academic misconduct (see Figure 8). By asking users
not to engage in academic misconduct, they do of course pointout that it is possible to use the service to do so. Again, because
this is a systematic risk and individual scholars cannot reliably
eliminate all AI responses from their own data, the focus is on
providers of these technologies to minimize their use for scientificmisconduct. Asking users not to engage in this is one step, butother consequences may become necessary if too many users violate
this rule.
Unless the crowdsourcing platforms acknowledge this problem
and prevent payment for bot submissions, the economic incentive
to abuse the system will remain. Some crowdsourcing platforms
allow researchers to deny payment for unacceptable submissions.
However, this verification process is labor-intensive and can be(automatically) challenged by the participant. It would be desirable
if the crowdsourcing platforms would take a more active role in
the prevention of fraud by fundamentally challenging the businessmodel of bots.
Services like Prolific have some quality controls and achieve
relatively low rates of poor-quality data. If the percentage ofbots is lower, and humans are able to accurately detect themapproximately 76% of the time, this would not be a major problem.
The key, then, is to ensure high rates of quality participants on thecrowdsourcing platforms. AI will threaten researchers’ abilities to
identify individual bad actors, so the solution has to be systematic. A
bad actor might be able to use AI to generate bad responses and getaway with it sometimes, but if crowdsourcing platforms are vigilant
in identifying accounts using bots and eliminating their responses
approximately three-quarters of the time, bad actors will struggle
to prosper.
At this point, almost all methods of automatically distinguishinghumans from bots and LLMs have failed in one way or another.
Bots have become faster and more accurate at solving CAPTCHAs(Searles et al., 2023). Increasing the difficulty of these is not an
option, since this would make it too difficult for humans tosolve them. Neither CAPTCHAs nor free text responses offer
sufficient protection. Researchers and platforms have the option
to increase their efforts by combining various methods, which
is our recommendation. However, this will eventually lead to

5 Conclusion and future work
The results of our experiment show that the arrival of LLM
services, such as Undetectable.AI, renders automatic AI detection
systems ineffective. These systems produce far too many false
negatives and thereby allow AI-generated responses into data
analysis. This has the potential to fundamentally corrupt datacollected in online questionnaires. Moreover, the responses to open
questions collected in online questionnaires do not often meetthe minimum length required to enable any chance at effective
automatic AI detection.
One could argue that asking participants in online studies towrite more text might be a solution, since this might enable better
performance by automatic AI detection systems. However, thisidea fails to recognise the power of obfuscation services, such as
Undetectable.AI. Moreover, since participants are paid according to
the amount of time they spend on a questionnaire and additional
length comes at no practical cost for LLMs, experimenters will
only set up more attractive targets for abuse. Having to write longtext answers might also discourage participants from completing anexperiment and might introduce a bias towards participants withhigh levels of literacy.
The participants in our experiment were students who werefamiliar with computer science. Their overall accuracy in identifying
the authorship of texts was approximately 76%. While this issignificantly above chance level, it is not as accurate as one
might hope, given the normally accepted level of false positives
in psychological experiments of 5%. Guo et al. (2023) showed thatwhen humans have to distinguish between texts of AI origin andhuman origin, if the texts are presented one by one, performanceis lower than when the texts are presented in pairs. This strategy

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FIGURE 8

The Undetectable.AI interface requires the acceptance of their terms of service. The red box has been added by the authors to highlight therequirement to agree to the terms of service.

by an LLM.25 While some scholars have attempted to examine
the performance of these AI detectors (see Uzun (2023) for anon-systematic list of some current AI detectors), their accuracy
remains insufficiently clear. Our data suggest that, at least in
the case of short answers to prompts, human readers may bebetter able to detect AI responses than automated detectors.
However, their accuracy level may still not be sufficient to proveacademic misconduct to a sufficient standard for disciplinary action
to be taken.

diminishing returns. At some point, conducting questionnaires
online may no longer be quicker and cheaper than conducting
them in person.
This concern applies not only to the field of HRI, but toany segment of the wider research community that uses online
questionnaires. Problems with data quality will only ever increase
the difficulties in conducting studies that can be reliably replicated.
The concerns raised in this study thereby fundamentally apply to thereplication crisis and its possible remedies.

5.2 Limitations and future work

5.1 Implications for education

It would have been possible to conduct this experimentcompletely online using one of the popular platforms to recruit
participants and conduct the questionnaire. While this would haveallowed us to conveniently increase the number of participants, itwould have had the potential to be manipulated by bad actors. To
ensure that no AI was participating in our study, we decided to
conduct it in person. We are aware that the convenience sampling
employed has its own limitations.
Some details relating to the participants need to be discussed.
While a sample size of 42 participants does not seem to bemuch greater according to what was discussed in the introduction(Baxter et al., 2016), G*Power was used a priori and indicated arequired minimum sample size of 31 participants for two-tailed
logistic regression analyses, based on the odds ratio collected fromthe results of Gao et al. (2023), an expected power of 0.8, and alphaof 0.05. Using one-tailed logistic regression analyses did not leadNot only do the challenges of LLMs apply to experiments in HRI,
but they also have the potential to impact many aspects of education.
Students can use LLMs to generate responses to quizzes and even to
write entire essays or cheat on their exams (Susnjak, 2022; Rahman
and Watanobe, 2023). Some LLMs have been able to pass academic
examinations in various areas, such as computer science (Bordt
and von Luxburg, 2023) or medicine (Gilson et al., 2022; Kung et al.,2023). This has led some academics to develop concerns aboutclassic assessment methods and the impact on critical thinking,while others believe that we will still be able to identify AI-generated
essays because of the possible poor capabilities of the AI services,which potentially provide incorrect or irrelevant information.24
However, people might blindly rely on ChatGPT (Rahman and
Watanobe, 2023).
Plagiarism software providers, such as TurnItIn, have recently
announced that their software can now detect content generated

25 https://www.turnitin.com/blog/the-launch-of-turnitins-ai-writing-detector24 https://www.nature.com/articles/d41586-022-04397-7

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to an underpowered study, as a minimum of 40 participants was
required using the same G*Power settings. It is also important tonote that although Gao et al. (2023) did not use a within-participants
design (their participants were each exposed to half of the stimuli),
our study did, with 20 stimuli. It would therefore be expected toachieve sufficient power, if not more, with fewer participants. Mostof the participants in our study had a technical background. Students
from different disciplines might be less capable of identifying an
AI system. Guo et al. (2023) showed that people who frequently
use ChatGPT achieve better accuracy in identifying its output thanpeople who have never heard about ChatGPT. It would therefore
be desirable to replicate this study with wider sampling. It is also
not a given that computer science students will always be betterat detecting AI-generated text. It is conceivable that students inother fields, such as English literature, might be more fine-tuned
to the style and voice of text and therefore better at detecting
AI-generated text.
We do not have any evidence on how many responses toonline questionnaires are currently being created by AI; nor can we
accurately predict how prevalent this problem will become. What iscertain is that it typically only takes a small number of bad actors toerode trust in the common good. Honesty boxes normally disappear
quickly if small numbers of criminals can take advantage of them.

editing. ST: Data curation, Formal Analysis, Methodology,
Visualization, Writing–original draft, Writing–review andediting. AV: Conceptualization, Methodology, Supervision,
Writing–original draft, Writing–review and editing. CB:
Conceptualization,
Methodology,
Project
administration,
Supervision, Writing–original draft, Writing–review and editing.

Funding
The authors declare that no financial support was received forthe research, authorship, and/or publication of this article.

Conflict of interest
The authors declare that the research was conducted in theabsence of any commercial or financial relationships that could beconstrued as a potential conflict of interest.

Publisher’s noteAll claims expressed in this article are solely those of theauthors and do not necessarily represent those of their affiliatedorganizations, or those of the publisher, the editors and thereviewers. Any product that may be evaluated in this article, or claimthat may be made by its manufacturer, is not guaranteed or endorsedby the publisher.

Data availability statement
All data (raw and data analysis) supporting the conclusion of thisarticle can be found at http://doi.org/10.17605/OSF.IO/TFA48.

Ethics statement

Author disclaimer

The studies involving humans were approved by the Universityof Canterbury Human Research Ethics Committee (HREC
2023/51/LR-PS). The studies were conducted in accordance withthe local legislation and institutional requirements. The participants
provided their written informed consent to participate in this study.

This study was pre-registered at AsPredicted with reference
number 135091. This study was approved by the University ofCanterbury Human Ethics Committee (HREC 2023/51/LR-PS).

Supplementary material

Author contributions

The Supplementary Material for this article can be foundonline at: https://www.frontiersin.org/articles/10.3389/frobt.2023.
1277635/full#supplementary-material

BL: Data curation, Formal Analysis, Investigation, Methodology,
Visualization, Writing–original draft, Writing–review andReferences
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