# Perception, performance, and detectability of conversational artificial
intelligence across 32 university courses

Hazem Ibrahim  Division of Science, New York University Abu Dhabi, UAE
Fengyuan Liu  Division of Science, New York University Abu Dhabi, UAE  Rohail
Asim  Division of Science, New York University Abu Dhabi, UAE  Balaraju Battu
Division of Science, New York University Abu Dhabi, UAE  Sidahmed
Benabderrahmane  Division of Science, New York University Abu Dhabi, UAE
Bashar Alhafni  Division of Science, New York University Abu Dhabi, UAE  Wifag
Adnan  Division of Social Science, New York University Abu Dhabi, UAE  Tuka
Alhanai  Division of Engineering, New York University Abu Dhabi, UAE  Bedoor
AlShebli  Division of Social Science, New York University Abu Dhabi, UAE
Riyadh Baghdadi  Division of Science, New York University Abu Dhabi, UAE
Jocelyn J. Bélanger  Division of Science, New York University Abu Dhabi, UAE
Elena Beretta  Division of Science, New York University Abu Dhabi, UAE  Kemal
Celik  Division of Engineering, New York University Abu Dhabi, UAE  Moumena
Chaqfeh  Division of Science, New York University Abu Dhabi, UAE  Mohammed F.
Daqaq  Division of Engineering, New York University Abu Dhabi, UAE  Zaynab El
Bernoussi  Division of Social Science, New York University Abu Dhabi, UAE
Daryl Fougnie  Division of Science, New York University Abu Dhabi, UAE  Borja
Garcia de Soto  Division of Engineering, New York University Abu Dhabi, UAE
Alberto Gandolfi  Division of Science, New York University Abu Dhabi, UAE
Andras Gyorgy  Division of Engineering, New York University Abu Dhabi, UAE
Nizar Habash  Division of Science, New York University Abu Dhabi, UAE  J.
Andrew Harris  Division of Social Science, New York University Abu Dhabi, UAE
Aaron Kaufman  Division of Social Science, New York University Abu Dhabi, UAE
Lefteris Kirousis  Division of Science, New York University Abu Dhabi, UAE
Korhan Kocak  Division of Social Science, New York University Abu Dhabi, UAE
Kangsan Lee  Division of Social Science, New York University Abu Dhabi, UAE
Seungah S. Lee  Division of Social Science, New York University Abu Dhabi, UAE
Samreen Malik  Division of Social Science, New York University Abu Dhabi, UAE
Michail Maniatakos  Division of Engineering, New York University Abu Dhabi,
UAE  David Melcher  Division of Science, New York University Abu Dhabi, UAE
Azzam Mourad  Division of Science, New York University Abu Dhabi, UAE  Minsu
Park  Division of Social Science, New York University Abu Dhabi, UAE  Mahmoud
Rasras  Division of Engineering, New York University Abu Dhabi, UAE  Alicja
Reuben  Division of Social Science, New York University Abu Dhabi, UAE  Dania
Zantout  Division of Science, New York University Abu Dhabi, UAE  Nancy W.
Gleason  Division of Social Science, New York University Abu Dhabi, UAE  Kinga
Makovi  Division of Social Science, New York University Abu Dhabi, UAE  Talal
Rahwan  Yasir Zaki

## Abstract

The emergence of large language models has led to the development of powerful
tools such as ChatGPT that can produce text indistinguishable from human-
generated work. With the increasing accessibility of such technology, students
across the globe may utilize it to help with their school work—a possibility
that has sparked discussions on the integrity of student evaluations in the
age of artificial intelligence (AI). To date, it is unclear how such tools
perform compared to students on university-level courses. Further, students’
perspectives regarding the use of such tools, and educators’ perspectives on
treating their use as plagiarism, remain unknown. Here, we compare the
performance of ChatGPT against students on 32 university-level courses. We
also assess the degree to which its use can be detected by two classifiers
designed specifically for this purpose. Additionally, we conduct a survey
across five countries, as well as a more in-depth survey at the authors’
institution, to discern students’ and educators’ perceptions of ChatGPT’s use.
We find that ChatGPT’s performance is comparable, if not superior, to that of
students in many courses. Moreover, current AI-text classifiers cannot
reliably detect ChatGPT’s use in school work, due to their propensity to
classify human-written answers as AI-generated, as well as the ease with which
AI-generated text can be edited to evade detection. Finally, we find an
emerging consensus among students to use the tool, and among educators to
treat this as plagiarism. Our findings offer insights that could guide policy
discussions addressing the integration of AI into educational frameworks.

## Significance statement

Given the recent emergence of conversational artificial intelligence tools,
educational institutions worldwide are facing the significant challenge of
addressing the integration of artificial intelligence into educational
frameworks. Yet, the literature lacks a systematic study evaluating the
performance of such tools on university-level courses and their susceptibility
to detection, and also lacks an examination of students’ and educators’
perspectives on the use of such tools in educational contexts. This work fills
these gaps, providing timely and vital insights into the performance of the
latest such tool—ChatGPT—and the threat of “AI-plagiarism” that it entails.
Our findings can inform policy discussions of how to shape student evaluation
frameworks in the age of artificial intelligence.

## Introduction

Generative artificial intelligence (AI) refers to the use of machine learning
algorithms that build on existing material, such as text, audio, or images to
create new content. Recent advancements in this field, coupled with its
unprecedented accessibility, has led many to consider it a “game-changer that
society and industry need to be ready for” [1]. In the realm of art, for
example, Stable Diffusion and DALL-E have received significant attention for
their ability to generate artwork in different styles [2, 3]. Amper Music,
another generative AI tool, is capable of generating music tracks of any given
genre, and has already been used to create entire albums [4, 5]. ChatGPT is
the latest tool in this field, which can generate human-like textual responses
to a wide range of prompts across many languages. More specifically, it does
so in a conversational manner, giving users the ability to naturally build on
previous prompts in the form of an ongoing dialogue. This tool has been
described as an “extraordinary hit” [6], and a “revolution in productivity”
[7], for its seemingly endless utility in numerous out-of-the-box
applications, such as creative writing, marketing, customer service, and
journalism, just to name a few. The capabilities of the tool have sparked
widespread interest, with ChatGPT reaching one million users in only five days
after its release [8], and soaring to over 100 million monthly users in just
two months.

In spite of its impressive ability, generative AI has been marred by ethical
controversies. In particular, as generative AI models are trained on massive
amounts of data available on the internet, there has been an ongoing debate
regarding the ownership of this data [9, 10, 11]. Furthermore, as these tools
continue to evolve, so does the challenge of identifying what is created by
humans and what is created by an algorithm. In the context of education,
ChatGPT’s ability to write essays and generate solutions to assignments has
sparked intense discussion concerning academic integrity violations by school
and university students. For instance, in the United States its use has been
banned by school districts in New York City, Los Angeles, and Baltimore [12].
Similarly, Australian universities have announced their intention to return to
“pen and paper” exams to combat students using the tool for writing essays
[13]. Indeed, many educators have voiced concerns regarding plagiarism, with
professors from George Washington University, Rutgers University, and
Appalachian State University opting to phase out take-home, open-book
assignments entirely [14]. In the realm of academic publishing, a number of
conferences and journals have also banned the use of ChatGPT to produce
academic writing [15, 16], which is unsurprising given that abstracts written
by ChatGPT have been shown to be indistinguishable from human-generated work
[17]. Yet, many have argued for the potential benefits of ChatGPT as a tool
for improving writing output, with some even advocating for a push to reform
the pedagogical underpinnings of the evaluative process in the education
system [18, 19]. Despite these examples, the perspectives of students and
educators around the globe remain unclear. The literature also lacks a
systematic study comparing the performance of ChatGPT against that of students
across various disciplines. Finally, the detectability of ChatGPT-generated
solutions to homework remains unknown.

Here, we examine the potential of ChatGPT as a tool for plagiarism by
comparing its performance to that of students across 32 university-level
courses from eight disciplines. Further, we evaluate existing algorithms
designed specifically to detect ChatGPT-generated text, and assess the
effectiveness of an obfuscation attack that can be used to evade such
algorithms. To better understand the perspectives of students and educators on
both the utility of ChatGPT as well as the ethical and normative concerns that
arise with its use, we survey participants (N=1601), recruited from five
countries, namely Brazil, India, Japan, United Kingdom, and the United States.
Additionally, we survey 151 undergraduate students and 60 professors more
extensively at the authors’ institution to explore differences in perceptions
of ChatGPT across disciplines. We find that ChatGPT’s performance is
comparable, or even superior, to that of students on nine out of the 32
courses. Further, we find that current detection algorithms tend to
misclassify human answers as AI-generated, and misclassify ChatGPT answers as
human-generated. Worse still, an obfuscation attack renders these algorithms
futile, failing to detect 95% of ChatGPT answers. Finally, there seems to be a
consensus among students regarding their intention to use ChatGPT in their
schoolwork, and among educators with regard to treating its use as plagiarism.
The inherent conflict between these two poses pressing challenges for
educational institutions to craft appropriate academic integrity policies
related to generative AI broadly, and ChatGPT specifically. Our findings offer
timely insights that could guide policy discussions surrounding educational
reform in the age of generative AI.

## Results

We start off by exploring the current capabilities of generative AI to solve
university-level exams and homework. To this end, we reached out to faculty
members at New York University Abu Dhabi (NYUAD), asking them to provide 10
questions from a course that they have taught at the university, along with
three randomly-chosen student answers to each question. Additionally, for each
course, ChatGPT was used to generate three distinct answers to each of the 10
questions. Both students’ and ChatGPT’s answers were then compiled into a
single document in random order, labelled as “Submission 1” to “Submission 6”.
Each of these submissions were then graded by three different graders,
recruited by the faculty member who had taught that course; see Methods for
more details, and Supplementary Table 1 for inter-rater reliability. The
results of this evaluation can be found in Figure 1a, and see Supplementary
Table 2 for the numeric values. Apart from Mathematics and Economics, each
discipline has at least one course on which ChatGPT’s performance is
comparable to, or even surpasses, that of students. These courses are: (i)
Data Structures; (ii) Introduction to Public Policy; (iii) Quantitative
Synthetic Biology; (iv) Cyberwarfare; (v) Object Oriented Programming; (vi)
Structure and Properties of Civil Engineering Materials; (vii) Biopsychology;
(viii) Climate/Change; and (ix) Management and Organizations. As a robustness
check, we standardized the grades given by each grader of each course to
account for grader-specific effects, and again found ChatGPT’s performance is
comparable, or superior, to student in the above nine courses (Supplementary
Table 3).

Having analyzed ChatGPT’s performance on different courses, we now perform an
exploratory analysis of how its performance varies along different levels of
cognition and knowledge. To this end, we asked participating faculty to
specify where each of their questions fell along the “knowledge” and
“cognitive process” dimensions of Anderson and Krathwohl’s taxonomy [20]; see
Table 1 for a description of the levels that constitute each dimension. The
results of this analysis are summarized in Figure 1b. Interestingly, the gap
in performance between ChatGPT and students is substantially smaller on
questions requiring high levels of knowledge and cognitive process, compared
to those requiring intermediate levels. Also interesting is ChatGPT’s
performance on questions that require creativity—the highest level along the
cognitive process dimension—receiving an average grade of 7.5 compared to the
students’ average grade of 7.9. Perhaps unsurprisingly, the only questions on
which ChatGPT outperforms students are those requiring factual knowledge,
attesting to the massive amounts of data on which it was trained. Finally, we
compare ChatGPT’s performance against different types of questions. To this
end, for each question, we asked the participating faculty to specify whether
the question: (i) involves mathematics; (ii) involves code snippets; (iii)
requires knowledge of a specific author, paper/book, or a particular
technique/method; and (iv) is a trick question. The results are summarized in
Figure 1c. Again, we find that the largest performance gap between ChatGPT and
students was for math-related questions, followed by trick questions. For the
time being, humans seem to outperform ChatGPT in these areas.

Table 1: A description of each level along the “knowledge” and “cognitive process” dimensions of Anderson and Krathwohl’s taxonomy, taken from [20]. Knowledge dimension | Description
---|---
Factual | The basic elements that students must know to be acquainted with a discipline
| or solve problems in it
Conceptual | The interrelationships among the basic elements within a larger structure that
| enable them to function together
Procedural | How to do something; methods of inquiry, and criteria for using skills,
| algorithms, techniques, and methods
Metacognitive | Knowledge of cognition in general as well as awareness and knowledge of one’s
| own cognition
Cognitive process dimension | Description
Remember | Retrieving relevant knowledge from long-term memory
Understand | Determining the meaning of instructional messages, including oral, written,
| and graphic communication
Apply | Carrying out or using a procedure in a given situation
Analyze | Breaking material into its constituent parts and detecting how the parts relate
| to one another and to an overall structure or purpose
Evaluate | Making judgments based on criteria and standards
Create | Putting elements together to form a novel, coherent whole or make
| an original product
Figure 1: Comparing ChatGPT to university-level students. Comparing ChatGPT’s
average grade (green) to the students’ average grade (blue), with error bars
representing 95% confidence intervals. (a) Comparison across university
courses. (b) Comparison across the “cognitive process” and “knowledge”
dimensions of Anderson and Krathwohl taxonomy’s of learning. (c) Comparison
across question types. $p$-values are calculated using bootstrapped two-sided
Welch’s T-test, and only shown for courses where GPT does not receive a
significantly lower grade compared to students (** = $p<0.01$; *** =
$p<0.001$; $ns=$ not significant, i.e., $p>0.05$).

To understand how the use of ChatGPT is perceived by educators and students,
we fielded a global survey in five countries, namely Brazil, India, Japan, the
UK, and the US, targeting a minimum of 100 educators and 200 students per
country; see Methods for more details. A summary of our findings can be seen
in Figure 2. Before delving into this analysis, it should be noted that
students and educators in our survey come from various levels of the
education. As such, we performed a similar analysis focusing only on
undergraduate and postgraduate students, as well as university-level
educators, and found broadly similar results; see Supplementary Figure 1. We
start off by comparing the responses of students vs. educators in different
countries; see panels a-c in Figure 2. Here, each plot corresponds to a
different question in the survey, asking about the degree to which respondents
agree or disagree with a particular statement about ChatGPT ($-2=$ strongly
disagree; $-1=$ disagree; $0=$ neutral; $1=$ agree; $2=$ strongly agree). We
present statements in three broad categories: (i) the ethics of using ChatGPT
in the context of education in panel a; (ii) the impact of ChatGPT on future
jobs in panel b; and (iii) the impact of ChatGPT on inequality in education in
panel c; see Supplementary Figure 4 for the remaining statements on the
survey. Starting with panel a (ethics), there seems to be a consensus that
using ChatGPT in school work should be acknowledged. In contrast, opinions
vary when it comes to determining whether the use of ChatGPT in homework is
unethical and whether it should be prohibited in school work, e.g., students
in India and the US believe it is unethical and should be prohibited, while
those in Brazil believe otherwise. Moving on to panel b (jobs), students in
all five countries believe they can outsource mundane tasks to ChatGPT, and
the educators in Brazil and India seem to agree with this statement. India is
the only country where educators believe ChatGPT is needed to increase their
competitiveness in their job, and the students therein agree the most with
this statement. Moreover, educators and students in India are the only ones
who worry that ChatGPT will take their job in the future. In terms of panel c
(inequality), there seems to be a consensus that ChatGPT will increase the
competitiveness of non-native English students. When it comes to whether
ChatGPT will reduce inequality in education, educators in Brazil and Japan
(the two non-English speaking countries in our sample) agree with this
statement, while those in the remaining three countries disagree.

Next, we compare the distribution of the educators’ and students’ responses to
the following question: “What percentage of your students/peers do you think
will use ChatGPT in their studies?”. The results are depicted in Figure 2d,
where the distributions of educators and students responses are illustrated in
orange and blue, respectively, with vertical lines of the same colors
representing the means. The black vertical line represents the percentage of
students who answered “Yes” to the question: “Considering your next term of
studies, would you use ChatGPT to assist with your studies?”. As can be seen
in the fourth row, representing the average response across the five
countries, 74% of students indicate that they would use ChatGPT (black line),
while both educators and students underestimated this share. For the students
who said they would use it (74%), their main reasons are to improve their
skills and save time (Figure 2f). As for those who said they would not use
ChatGPT (26%), their main reasons are not knowing how to do so or not having a
need for it, rather than a fear of being penalized or acting unethically
(Figure 2g).

Finally, we perform an OLS regression analysis to explore which factors that
might be associated with the students’ decision to use ChatGPT during their
next term of studies. Figure 2e summarizes the results for a few independent
variables of interest; the remaining variables can be found in Supplementary
Figure 2-3. As can be seen, students from Brazil and India are significantly
more likely, and students from Japan are significantly less likely, to use
ChatGPT than those from the USA. As for prior experience with ChatGPT, those
who have used it are significantly more likely to use it again. On the
contrary, simply hearing about ChatGPT is not significantly associated with
the students’ decision to use it to assist with their studies. Finally,
students from poor and working class backgrounds are significantly more likely
to indicate they would use ChatGPT for their studies compared to upper class
students.

Figure 2: Global survey responses. (a-c) Educators’ average responses (x-axis)
and students’ average responses (y-axis) to eight questions regarding ChatGPT;
$1=$ agree; $-1=$ disagree; $\star=$ average over the five countries. (d)
Distributions of students’/educators’ estimation of the percentage of their
peers/students who they believe will use ChatGPT in their studies. The dashed
black line represents the percentage of students who indicated they would use
ChatGPT in their studies. (e) OLS-estimated coefficients and 95% confidence
intervals of selected independent variables predicting a student’s decision to
use ChatGPT in their next term of studies; see Supplementary Materials for all
independent variables considered. (f-g) A breakdown of why students indicated
they would or would not use ChatGPT.

Having analyzed the global survey, we now shift our attention to the second
survey, which was conducted at the authors’ institution—NYUAD. While this
survey is narrower in scope than the previous one, it focuses on university
students and professors, allowing us to examine variances in responses with
respect to GPA in the case of students, and type of appointment in the case of
professors. Figure 3a depicts the responses of 151 students (y-axis) and 60
professors (x-axis) with regards to the same eight statements discussed
earlier, grouped into three broad categories: (i) the ethics of using ChatGPT
in the context of education in red; (ii) the impact of ChatGPT on future jobs
in green; and (iii) the impact of ChatGPT on inequality in education in blue;
see Supplementary Figure 5 for the remaining statements. As can be seen,
professors agreed more, or disagreed less, with all statements in category (i)
compared to students (notice how all red data points fall below the diagonal)
while students agreed more, or disagreed less, with all statements in
categories (ii) and (iii). Despite these differences, professors and students
seem to agree that ChatGPT’s use should be acknowledged, and neither believes
that it will take their future job.

Figure 3b shows students’ empirical expectations (first and third rows) and
normative expectations (second and fourth rows) of whether they plan to use
ChatGPT to assist with their assignments (first two rows) and whether they
think one should use ChatGPT to assist with one’s assignment (last two rows).
The majority of students plan to use ChatGPT to assist with their assignments
(57%), and expect their peers to use it for this purpose (64%). Moreover, the
majority believe ChatGPT should be used (61%), and expect that their peers
believe it should be used (55%), to assist with assignments. Similarly, Figure
3c depicts professors’ empirical expectations (first and third rows) and
normative expectations (second and fourth row) of whether they plan to treat
ChatGPT’s use as plagiarism (first two rows) and whether they think one should
treat ChatGPT’s use as plagiarism (last two rows). Most professors plan to
treat the use of ChatGPT as plagiarism (69%), and expect others to do so
(71%). Furthermore, the majority believe the use of ChatGPT should be treated
as plagiarism (72%), and expect that their peers believe it should be treated
as such (73%).

Figure 3d compares students’ intention to use ChatGPT for their studies across
disciplines, GPA, and socioeconomic status. Starting with discipline, the
majority of students from all four disciplines indicated that they plan on
using ChatGPT. As for GPA, disregarding those who preferred not to disclose
their GPA, the majority of students from all GPA brackets into which students
fell indicated that they would use the tool. Similarly, with regards to
socioeconomic status, the majority of students from all socioeconomic
mentioned they would use ChatGPT. As for professors, Figure 3e compares
responses on whether they plan to treat the usage of ChatGPT as plagiarism
across disciplines, length of teaching experience, and type of appointment at
the institution. As shown in this figure, in each discipline apart from
Engineering, the majority of faculty intend to treat it as plagiarism. In
terms of teaching experience, again, the majority of faculty intend to do so,
regardless of their experience. Similarly, for each type of appointment at the
institution, the majority of faculty plan on treating the use of ChatGPT as
plagiarism.

Figure 3: NYUAD survey responses. (a) Professors’ responses (x-axis) and
Students’ responses (y-axis) to eight statements regarding ChatGPT; the
statements about ethics, employment, and inequality are colored in red, green,
and blue, respectively. (b \- c) Descriptive norms and Injunctive norms of
students and professors at NYUAD with regards to using ChatGPT, and treating
it as plagiarism, respectively. (d) Comparing student responses across
disciplines, GPA, and socioeconomic status. (e) Comparing professor responses
across disciplines, length of teaching experience, and type of appointments.

We conclude our analysis by assessing the detectability of using ChatGPT. To
this end, we use two classifiers, namely GPTZero [21] and OpenAI’s own AI text
classifier [22], both of which are designed specifically to determine whether
a body of text has been generated using AI. We use these classifiers to
quantify the percentage of human submissions that are misclassified as
ChatGPT, as well as the proportion of ChatGPT submissions that are
misclassified as human. More formally, our goal is to construct a normalized
confusion matrix for each classifier, and to quantify its false-positive and
false-negative rates; see Methods for more details. Out of all 320 questions
in our dataset, there were 40 questions whose answers consisted entirely of
mathematical equations that are incompatible with the classifiers; those were
excluded from our evaluation, leaving us with 280 questions. The results are
depicted in Figure 4a. As can be seen, OpenAI’s Text Classifier misclassifies
5% of student submissions as AI-generated, and 49% of ChatGPT’s submissions as
human-generated. GPTZero has a higher false positive rate (18%), but a lower
false-negative rate (32%).

Next, we analyze the degree to which these classifiers are susceptible to
obfuscation attacks. We devised a simple attack that involves running the
ChatGPT-generated text through Quillbot [23]—a popular paraphrasing/re-writing
tools utilized by students worldwide. Our “Quillbot attack” was chosen since
it can be readily implemented by students, without requiring any technical
know-how; see Methods for a detailed description of the attack, and see Figure
4b for a stylized example of how it works. Out of the 280 questions, 47
required answers that involved snippets of computer code. For those questions,
about half of the ChatGPT submissions were already misclassified as human-
generated, without any obfuscation attacks. Based on this, as well as the fact
that Quillbot does not work on code snippets, we excluded those questions from
the sample on which we run the Quillbot attack. The results of this evaluation
are summarized in Figure 4c. As shown in this figure, the attack is remarkably
effective. In the case of OpenAI’s text classifier, the false-negative rate
increased from 49% to 98%, and in the case of GTPZero, the false-negative rate
increased from 32% to 95%. Figure 4d evaluates the two classifiers on various
courses. In the left column, triangles represent the percentage of student
submissions that are misclassified as AI-generated. In the right column, the
solid and empty circles represent the percentage of ChatGPT submissions that
are misclassified as human-generated before, and after, the Quillbot attack,
respectively, with arrows highlighting the difference between the two circles.
Note that there are a handful of courses for which there are no empty circles
since they include code snippets. As the figure illustrates, in each
discipline, there are courses for which students’ submissions are
misclassified, as well as courses for which ChatGPT submissions are
misclassified. Importantly, the Quillbot attack is successful on all courses,
often increasing the false-negative rate to 100%. Supplementary Figure 6
analyzes the performance of both classifiers on different question types, and
analyzes their performance along both dimensions of the Anderson and Krathwohl
taxonomy [24].

Figure 4: Evaluating AI-text detectors. Quantifying the degree to which
GPTZero and AI text classifier can identify whether a body of text is AI-
generated. (a) Normalized confusion matrices of GPTZero and AI text
classifier. (b) An example of the Quillbot attack taken from our dataset, with
changes highlighted in different colors. (c) Normalized confusion matrices
after deploying the Quillbot attack. (d) Evaluating the two classifiers on
different courses.

## Discussion

This study aimed to provide both quantitative and qualitative insights in the
perceptions and performance of current text-based generative generate AI in
the context of education. We surveyed educators and students at the authors’
institution and in five countries, namely Brazil, India, Japan, UK, and USA,
regarding their perspectives on ChatGPT. There is a general consensus between
educators and students that the use of ChatGPT in school work should be
acknowledged, and that it will increase the competitiveness of students who
are non-native English speakers. Additionally, there is a consensus among
students that, in their future job, they will be able to outsource mundane
tasks to ChatGPT, allowing them to focus on substantive and creative work.
Interestingly, the majority of surveyed students expressed their intention to
use ChatGPT despite ethical considerations. For example, students in India
think the use of ChatGPT in homework is unethical and should not be allowed,
while those in Brazil think it is ethical and should be allowed, yet in both
countries, the vast majority of surveyed students (94%) indicated that they
would use it to assist with their homework and assignments in the coming
semester. The negative implications of this finding go beyond the university,
as studies have demonstrated that academic misconduct in school is highly
correlated to dishonesty and with similar decision-making patterns in the
workplace [25, 26].

The NYUAD survey allowed us to examine whether there exists a descriptive norm
among students backed by injunctive norms regarding the use of ChatGPT, and
among professors with regards to treating ChatGPT’s use as plagiarism. More
specifically, we investigate whether individuals prefer to conform to these
behaviours on the condition that they expect the majority of their peers would
also conform to them [27, 28, 29]. Our results indicate that the majority of
students plan to use ChatGPT to assist with their assignments, and believe
their peers would approve of its use, implying that the use of ChatGPT is
likely to emerge as a norm among students. Similarly, we find that the
majority of professors plan to treat the use of ChatGPT as plagiarism tool,
and believe their peers would approve of treating it as such, implying that
the treatment of ChatGPT as plagiarism is likely to emerge as a norm among
professors. The inherent conflict between these two could create an
environment where students’ incentives suggest they should hide the use of
ChatGPT while professors are eager to detect its use (and may expect their
colleagues to do the same). This poses a major challenge for educational
institutions to craft appropriate academic integrity policies related to
generative AI broadly, and ChatGPT specifically.

When comparing ChatGPT’s performance to that of students across university-
level courses, the results indicate a clear need to take “AI-plagiarism”
seriously. Specifically, ChatGPT’s performance was comparable, if not
superior, to students’ performance on 12 (38%) out of the 32 courses
considered in our analysis. These findings, coupled with the clear ease with
which students can avoid detection by current AI-text classifiers as
demonstrated in our study, suggests that educational institutions are facing a
serious threat with regards to current student evaluation frameworks. To date,
it is unclear whether to integrate ChatGPT into academic and educational
policies, and if so, how to enforce its usage. While several school districts,
and even an entire country in the case of China, have already banned access to
the tool [30], the degree to which these measures can be effectively enforced
remains unknown. Treating the use of ChatGPT as plagiarism is likely to be the
most common approach to counter its usage. However, as we have shown, current
measures to detect its usage are futile. Other universities have opted to
change their evaluative processes to combat the tool’s usage, by returning to
in-person hand-written and oral assessments [13]—a step that may undo decades
of advancements aimed at deploying alternatives to high stakes examinations
that disadvantage some categories of students and minorities [31]. Our
findings suggest that ChatGPT could disrupt higher education’s ability to
verify the knowledge and understanding of students, and could potentially
decrease the effectiveness of teacher feedback to students. In education, it
is crucial to confirm that what is being assessed has been completed by the
person who will receive the associated grades. These grades go on to influence
access to scholarships, employment opportunities, and even salary ranges in
some countries and industries [32]. Teachers use assessment to give feedback
to learners, and higher education institutions use the assessment process to
credential learners. The value of the degree granted is in part linked to the
validity of the assessment process. Countermeasures to academic misconduct
such as plagiarism include applying detection software, such as Turnitin, and
emphasising honor codes in policy and cultural norms. Higher education, in
particular, is susceptible to disruption by ChatGPT because it makes it very
difficult to verify if students actually have the skills and competencies
their grades reflect. It disrupts how learning is assessed because
demonstrated learning outcomes may be inaccurate and thus, prolific use could
diminish the academic reputation of a given higher education institution. Our
findings shed light on the perceptions, performance, and detectibility of
ChatGPT, which could guide the ongoing discussion on whether and how
educational policies could be reformed in the presence of not only ChatGPT,
but also its inevitably more sophisticated successors.

## Methods

### ChatGPT performance analysis

All faculty members at New York University Abu Dhabi (NYUAD) were invited to
participate in this study and were asked to provide student assessments from
their courses. In particular, they were asked to follow these steps: (i)
select a course that they have taught at NYUAD; (ii) randomly select 10 text-
based questions from labs, homework, assignments, quizzes, or exams in the
course; (iii) randomly select three student submissions for each of the 10
questions from previous iterations of the course; (iv) convert any hand-
written student submissions into digital form; (v) obtain students’ consent
for the usage of their anonymized submissions in the study; and (vi) obtain
students’ confirmation that they were 18 years of age or older.

When choosing their questions, participating faculty were given the following
guidelines: (i) Questions should not exceed 2 paragraphs (but the answer could
be of any length); (ii) No multiple-choice questions were allowed; (iii) No
images, sketches, or diagrams were allowed in the question nor the answer;
(iv) No attachments are allowed in the question nor the answer (i.e., a paper
or a report to be critiqued, or summarized etc.); (v) The question could
include a table, and the answer could have a table to be filled; (vi) The
question and the answer could include programming code; (vii) The question and
the answer could include mathematics (e.g., equations); and (viii) Fill-the-
gap questions were allowed.

For each course, ChatGPT was used to generate three distinct answers to each
of the 10 questions. This step was carried out between the 13th and 29th of
January, 2023. Both students’ and ChatGPT’s answers were then compiled into a
single document in random order, labelled as “Submission 1” to “Submission 6”.
The faculty responsible for each course was asked to recruit three graders
(either teaching assistants, postdoctoral fellows, research assistants, PhD
students, or senior students who have taken the course before and obtained a
grade of A or higher), and to provide the graders with rubrics and model
solutions to each question. The participating faculty was instructed not to
inform the graders that some of the submissions are AI-generated. Each grader
was compensated for their time at a rate of $15 per hour. The participating
faculty vetted the graders’ output and ensured that the grading is in line
with the rubrics.

For each of their 10 questions, the faculty were asked to answer the
following: (i) Where does the question fall along the “knowledge” dimension
and the “cognitive process” dimension of the Anderson and Krathwohl taxonomy
[20]; (ii) Does the question or the answer involve mathematics? (Yes/No);
(iii) Does the question or the answer involve snippets of computer code?
(Yes/No); (iv) Does the question or the answer require knowledge of a specific
author, scientific paper/book, or a particular technique/method? (Yes/No); and
(v) Is the question a trick question? (Yes/No). To explain what a trick
question is, the following description was provided to participating faculty:
“A trick question is a question that is designed to be difficult to answer or
understand, often with the intention of confusing or misleading the person
being asked. Trick questions are often used in games and puzzles, and can be
used to test a person’s knowledge or problem-solving skills.”

### Survey

Participants were recruited from Brazil, India, Japan, United Kingdom, and
United States, with a minimum of 200 students and 100 educators per country;
see Supplementary Table 4-5 for descriptive statistics, and Supplementary Note
1.1-1.2 for the complete survey instrument. The survey was piloted in the
United States on Prolific with open-ended questions to investigate that
respondents did not find anything odd or surprising about survey questions,
and also to ensure no new topics emerged about ChatGPT that the opinion
statements covered. Data collection was conducted using the SurveyMonkey
platform. Studies have shown the validity of using this platform to conduct
surveys used in academic research, in addition to the numerous studies which
use such platforms for conducting surveys globally [33, 34, 35, 36, 37]. The
platform utilizes email and location verification for fraud detection and ID
exclusions, thereby guarding against duplicate and bot-submitted responses.
SurveyMonkey enables the targeting of samples with pre-specified demographics.
Specifically, in the US, we identified educators as those whose job is
“educator (e.g., teacher, lecturer, professor),” and identified students as
those who had a “student status.” As for the remaining four countries, the
above filters were not available on SurveyMonkey. Therefore, we recruited 100
respondents whose primary role is “Faculty/Teaching Staff” and recruited 200
respondents whose occupation was “studies.” In addition, we ensured that the
analytical sample only contains current students and educators who are either
teachers or professors based on respondents’ self-reports. Respondents filled
out the survey in the official language of their country. The global surveys
were conducted on the 19th and 20th of January, 2023.

As for the NYUAD survey, it includes responses from 151 students (out of a
total of 2103, 7.2%) and 60 professors (out of a total of 393, 15.2%)
collected between January 23rd and February 3rd, 2023. This survey used the
same questions as those used in the global survey, but with a few additions.
Specifically, for students, we asked them to specify their current cumulative
GPA, year of study, as well as empirical and normative expectations [29, 38]
related to the use of ChatGPT among students. As for faculty, we asked them
about the number of years they have been teaching, their academic division,
their appointment type (e.g., tenure, tenure track, contract, or visiting), as
well as their empirical and normative expectations surrounding the treatment
of ChatGPT as plagiarism. We excluded student participants who indicated that
they aware of our study, as well as faculty members who coauthored the paper.
Descriptive statistics can be found in Supplementary Tables 6-7, while the
survey questions can be found in Supplementary Notes 1.3-1.4.

### Obfuscation attacks

Once all student and ChatGPT submissions were collected for all courses, they
were used to test two classifiers designed specifically to determine whether a
body of text has been generated using AI, namely, GPTZero [21], and OpenAI’s
own AI text classifier [22]. The former classifies text into five categories:
(i) “likely to be written entirely by a human”, (ii) “most likely human
written but there are some sentences with low perplexities”, (iii) “more
information is needed”, (iv)“includes parts written by AI”, (v) “likely to be
written entirely by AI”. As for the latter, it classifies text into five
categories: (i) very unlikely, (ii) unlikely, (iii) unclear if it is, (iv)
possibly, or (v) likely AI-generated. We start our analysis by examining the
proportion of human submissions that are misclassified as ChatGPT, as well as
the proportion of ChatGPT submissions that are misclassified as human. More
formally, our goal is to construct a normalized confusion matrix for each
classifier, to quantify its false-positive and false-negative rates. For both
classifiers, ChatGPT submissions that are classified as categories (i) or (ii)
are treated as false negatives, while human submissions that are classified as
(iv) or (v) are treated as false positives. As for submissions classified as
(iii), we consider them to be classified as human. This decision is motivated
by our context, where the classifier is used to detect AI-plagiarism. Given
that a student is arguably “innocent until proven otherwise”, having an
inconclusive classification means that, as far as the teacher is concerned,
there is no conclusive evidence incriminating the student of AI-plagiarism,
implying that such a classification has practically the same consequences as
being classified as human. Based on this, ChatGPT submissions that are
classified as (iii) are treated as false negatives.

To evaluate the classifiers’ susceptibility to obfuscation attacks, we devised
a simple attack that involves running the ChatGPT-generated text through
Quillbot. Our attack starts by setting the “Synonym” slider in Quillbot to the
highest level, which maximizes the number of modifications introduced to the
text. Then, we run every ChatGPT-generated submission through each of the
“Modes” available on Quillbot, each resulting in a different output. Each
output is then re-analyzed on both classifiers, and if any of them manages to
flip the text classification from category (iv) or (v) to any other category,
the attack on this particular submission is considered successful.

## Data availability

All of the data used in our analysis can be found at the following repository:
https://github.com/comnetsAD/ChatGPT.

## Author Contributions

T.R. and Y.Z. conceived the study and designed the research; H.I., B.B, N.G.,
T.R., and Y.Z. wrote the manuscript; H.I., BA.A., and B.B performed the
literature review; H.I., T.R., and Y.Z. collected the data; K.M. designed the
surveys and ran the NYUAD survey; H.I. and Y.Z. ran the global survey; H.I.,
F.L., R.A., S.B., T.R., and Y.Z. analyzed the data and ran the experiments;
H.I., F.L., R.A., S.B., T.R., and Y.Z. designed the obfuscation attack; H.I.,
F.L., R.A., S.B., and Y.Z. ran the obfuscation attack; H.I., F.L., T.R., and
Y.Z. produced the visualizations; W.A., T.A., BE.A., R.B., J.B., E.B., K.C.,
M.C., M.D., B.G.S., Z.E., D.F., AL.G., AN.G., N.H., A.H., A.K., L.K., K.K.,
K.L., S.S.L., S.M., M.M., D.M., A.M., M.P., A.R., D.Z., N.G., K.M., T.R., and
Y.Z. provided course questions and answers, and recruited graders.

## Ethics statement

The research was approved by the Institutional Review Board of New York
University Abu Dhabi (HRPP-2023-5). All research was performed in accordance
with relevant guidelines and regulations. Informed consent was obtained from
all participants in every segment of this study.

## Acknowledgement

K.M. acknowledges funding from the NYUAD Center for Interacting Urban Networks
(CITIES), funded by Tamkeen under the NYUAD Research Institute Award CG001.

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