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Research Article
Treading water: new data on the impact of AI ethics information
sessions in classics and ancient language pedagogy
Edward A. S. Ross and Jackie Baines
Department of Classics, University of Reading, Reading, UK
Abstract
Over 2023, many universities and policy organisations in the higher education (HE) sector are working to create guiding principles and
guidelines for the use of generative artificial intelligence (AI) in HE Teaching and Learning (T&L). Despite these guidelines, students
remain unsure if and how they should use AI. This article discusses the AI information sessions held over the Autumn 2023 term in the
Department of Classics at the University of Reading, which aimed to provide students with the knowledge and tools to make informed
judgements about using AI in their studies. These sessions discussed the benefits and drawbacks of generative AI, highlighting training
data, content policy, environmental impact, and examples of potential uses. Staff and student participants were surveyed before and after
these information sessions to gather their opinions surrounding AI use. Although at least 60% of participants had previously used
generative AI, 80% of participants were apprehensive of or against using generative AI tools for learning purposes following the AI
information sessions. By providing staff and students with the ethical considerations surrounding generative AI, they can make an
informed judgement about using AI in their work without misplaced faith or excessive fear.
Keywords: artificial intelligence, conversational AI tools, new teaching tools, Ancient Greek, ClassicalLatin
Introduction
One year has passed since the public research preview of ChatGPT
3.5 was released, and a flood of new generative artificial intelligence
(AI) tools have entered the market. This includes a wide variety of
generative text tools (e.g. Bard AI, EssayAILab), generative image
tools (e.g. DALL-E 3, Midjourney, Stable Diffusion), generative
PowerPoint tools (e.g. Tome), generative audio tools (e.g.
CassetteAI), generative video tools (e.g. HeyGen), and AI-powered
search co-pilots (e.g. Bing Chat, Claude-2, Perplexity) (Anthropic,
2023; Cassette, 2020; EssayAIGroup, 2020; Google, 2023a; HeyGen,
2023; Magical Tome, 2023; Microsoft, 2023; Midjourney, 2023;
OpenAI, 2023d; Perplexity, 2023a; Stability.ai, 2023). Conversational
AI tools, in particular, now have beta features which allow them to
recognise images and engage in voice chat with a user, enhancing
user experience and ease of access (Google, 2023b; OpenAI, 2023b).
Furthermore, OpenAI has even released a new suite to create
personalised Generative Pre-trained Transformers (GPTs) which
users can fine tune for their preferred uses (OpenAI, 2023e).
Particularly relevant for Classics, generative text AI tools and
AI-powered search co-pilots have greatly improved their abilities
with ancient languages since March 2023, especially Latin and
Ancient Greek (Ross, 2023). The sheer speed by which these tools
are developing has made it difficult to consider how best to
approach using these tools in the Classics context; the picture keeps
on changing.
This article presents the methods used in the Department of
Classics at the University of Reading to inform and educate staff
and students about the ethical considerations for using generative
AI programs in Classics and ancient language pedagogy. This essay
highlights the key themes discussed in planning sessions with
teaching staff and information sessions for students about AI
training data, content policy, and environmental impact. To
illustrate these discussions, including staff and student opinions, we
will present the results of several diagnostic surveys taken during
these discussions and presentations.
This article is divided into four parts. First, we will outline the
state of the generative AI tool market at the time of writing. Due to
the sheer number of tools and how rapidly they are changing, this
section will only discuss the new developments which have
applications for ancient language study. In the second part, we will
discuss the major issues that were presented to staff and students
in the Department of Classics at the University of Reading:
training data, content policy, and environmental impact. These
issues are contextualised with survey results from the Department’s
teaching staff. Then, we will present the survey results from
students at the information sessions over the course of the Autumn
2023 term. Finally, we will outline the current stage of guidelines
for use of generative AI tools in Classics at the University of
Reading.
Corresponding author: Edward A. S. Ross; Email: edward.ross@reading.ac.uk
Cite this article: Ross EAS and Baines R (2024). Treading water: new data on the
impact of AI ethics information sessions in classics and ancient language pedagogy.
TheJournal of Classics Teaching 25, 181–190. https://doi.org/10.1017/S2058631024000412
The Journal of Classics Teaching (2024), 25, 181–190
doi:10.1017/S2058631024000412
https://doi.org/10.1017/S2058631024000412 Published online by Cambridge University Press
182 Edward A. S. Ross and Jackie Baines
The current state of generative AI text tools for classics
As of November 2023, conversational AI tools, such as
ChatGPT-3.5/4 (OpenAI) and Bard (Google), have greatly improved
their abilities with Latin and Ancient Greek and have added
additional features which streamline and improve user experience
(Google, 2023a, 2023b; OpenAI, 2023a, 2023b). ChatGPT’s abilities
with Ancient Greek have significantly improved since March 2023.
Where it had previously mixed together grammatical forms from
Classical, New Testament, and Modern Greek, ChatGPT-3.5’s
translation and composition abilities are now on par with its Latin
abilities. Compared to its translation of the test phrase ‘The giant
who eats men is not in the field now’ in March 2023, ChatGPT is
now able to produce a reasonably accurate output that follows Attic
Greek grammar and properly labels grammatical forms (Figure 1)
(Ross, 2023, 147–154; Taylor, 2016, 34).1 These abilities are still at an
elementary level and frequently make errors, but ChatGPT and
other generative text tools can effectively discuss grammar, create
vocabulary quizzes, and translate and compose short passages in
Latin and Ancient Greek with reasonable accuracy. These
functionalities with ancient languages have further applications for
student use with generative text AI’s new beta features.
Voice chat with generative text AI tools was made available as a
beta feature through Bard in July 2023 and ChatGPT in September
2023 (Google, 2023b; OpenAI, 2023b). This feature allows a user to
provide a prompt to the AI tool orally through a microphone, and
the tool then provides its output verbally in response. ChatGPT’s
style of voice output appears very human-like and mimics a phone
call. The tool can use five different voice tones that adapt its
conversation style as the conversation progresses, and it provides a
transcript of the inputs and outputs once the conversation is
complete (OpenAI, 2023b). Furthermore, although we do not know
how ancient languages sounded, ChatGPT’s voice outputs are able
to vocalise Latin and Ancient Greek words according to general
academic considerations of how these languages may have sounded
(Clackson, 2011; Morpurgo Davies, 2015) (Supplementary File 1).
The tools’ abilities with Latin and Ancient Greek in voice chat are
consistent with its text abilities, but this does present a more human
manner of supporting ancient language learning outside the
classroom.
Another new feature which could impact Classics and ancient
language T&L is generative text tools’ image recognition.
ChatGPT-4, Bard AI, and Perplexity can now accept images as
prompts, read them, summarise them, and provide outputs based
on their content (Google, 2023b; OpenAI, 2023b; Perplexity,
2023b). This presents an interesting possibility for ancient language
teaching. A student could take a photograph of some homework
questions and their answers, requesting that the AI provide
constructive feedback on these answers (Figure 2). In this example,
the AI is prepared with a screenshot of the homework questions
from Henry Cullen and John Taylor’s Latin to GCSE: Part 2 and
then provided a photo of handwritten answers to the questions
(2016, 22). ChatGPT-4 can read both images accurately, provide
transcriptions, and then provide constructive feedback for the
translations as teacher or tutor would. Unfortunately, the tool is
limited by its capabilities with Latin and Ancient Greek and
sometimes makes errors, but, overall, it tends to highlight actual
points of error and frames them with supportive feedback.
These new functions support a growing potential use for
conversational AI tools as an out-of-hours language tutor. Both
the voice chat and image recognition functions present easy
methods for students to ask grammar questions or request
feedback without needing to type out their questions or
homework answers. These features could be further fine-tuned
with a language model trained specifically to respond to questions
at the expected course level. OpenAI’s GPTs product provides a
streamlined API (application programming interface) for teachers
to train a GPT by uploading training data specific to their courses
and detailing the specific actions which they want the GPT to
perform (OpenAI, 2023e). These custom programs could be
extremely useful to support ancient language learning, but they
are just as restricted as ChatGPT by their Latin and Ancient Greek
abilities and require significant development time from the
creator. With proper fine-tuning, however, an ancient language
GPT could support at-home learning in a way which current
digital study tools are unable to fulfill.
Although conversational AI tools are still inconsistent with their
Latin and Ancient Greek abilities, they could be an increasingly
useful first port-of-call for ancient language learning support when
teachers are unavailable or when students are too nervous to submit
Figure 1. Left: Figure 18 from Ross, 2023, p. 153; Right: OpenAI, ChatGPT 3.5, September 25, 2023 version, personal communication, generated 8 October 2023. Prompt: ‘Provide an
Ancient Greek translation of this English sentence, “The giant who eats men is not in the field now.” and provide all grammatical information about each Ancient Greek word.’
https://doi.org/10.1017/S2058631024000412 Published online by Cambridge University Press
The Journal of Classics Teaching 183
their work for formative feedback. This, however, is entirely
dependent on whether students wish to use these tools once they
have been informed about the ethical considerations for AI.
Training data
In the AI information sessions that were held in the Department of
Classics at the University of Reading, we introduced three major
considerations to our students. The first focused on the training
data involved in the creation and development of generative AI
tools. We focused on the training data used in GPT-3, one of
OpenAI’s legacy models, to demonstrate the general areas where
information was gathered and to highlight its problems. This
training data included filtered Common Crawl data, WebText,
ebooks, and Wikipedia articles from prior to September 2021
(Brown et al., 2020, 9; OpenAI, 2023c). The details of this training
data are generally vague, only listing the major websites where data
was gathered and vague group identifiers, but users have been
researching what information is present in ChatGPT’s training data
(Brown et al., 2020, 9; veekaybee, 2022). Of interest to ancient
language study, Patrick J. Burns has found that GPT-3 has
approximately 339.1 million Latin word tokens in its training data,
and ChatGPT-4 will likely have many more (2023). The WebText
and ebooks in the training data, on the other hand, have drawn
wider attention from global governments and media.
To contextualise the problematic position of AI training data, we
showed students information about the current legal disputes
surrounding training data collection. OpenAI faced legislation and
lawsuits related to disclosing its training data over 2023, and this has
re-ignited debates on the nature of copyright and data protection
(Appel et al., 2023; Bikbaeva, 2023; Lucchi, 2023; Taylor, 2023;
Vincent, 2023; Zahn, 2023). Authors and artists claim that their
works were used to train AI without proper attribution and that this
is severely impacting their markets (Creamer, 2023; Metz, 2023;
Wong , 2023). Furthermore, the risk of data protection breaches in
using social media content in training data has led the European
Union to request the full details of OpenAI’s training datasets
(Vincent, 2023). At the time of writing, many of these disputes have
yet to have a resolution, but artists, authors, and users are working to
navigate through this tumultuous period of innovation.
Since much of the training data for generative AI tools has come
from open repositories, sometimes without permission, developers
have been working on new tools to help creators maintain ownership
of their art and style. However, these developments require us to
consider if AI models contain harmful inputs in their training data
that impact future student use of the tools. The Glaze team at the
University of Chicago has developed a new form of their image
cloaking tool called Nightshade (Katz, 2023). The original tool,
Glaze, affects the pixels of an image in a way which is humanly
imperceptible and misleads the AI from properly analysing the
artistic style of an image, rendering the outputs based on this data
useless (Shan et al., 2023). Nightshade takes this a step further,
manipulating the pixels in an image to appear unchanged to a
human but completely different to an AI. An image of a dog can be
manipulated to appear like a cat to an AI, eventually resulting in the
image of a cat being mislabelled as a dog and affecting future
outputs. As few as 300 of these manipulated images can corrupt the
training data of an AI tool and significantly skew future outputs,
changing requests for a dog to an image of a cat, or a fantasy-style
painting to an image styled as pointillism (Heikkilä, 2023). Although
this intentional obstruction is beneficial for artists, potentially
preventing AI tools from using their artwork without permission, it
does raise the issue of disinformation in AI training data.
Figure 2. OpenAI, ChatGPT 3.5, October 17, 2023 version, personal communication,
generated 20 October 2023. Prompt: ‘These are the homework questions for Classical
Latin. In this conversation, I want you to read my work and tell me if I have translated the
sentences accurately.’
https://doi.org/10.1017/S2058631024000412 Published online by Cambridge University Press
184 Edward A. S. Ross and Jackie Baines
After showing students the impact of Nightshade on AI outputs,
we discussed how other generative AI programs could be similarly
affected. If authors and creators produce a significant number of
articles and posts with intentional disinformation, these texts could
be analysed by generative text AI tools and cause a Nightshade-like
effect in their outputs. This corruption of training data can easily
lead the generative tools to produce text outputs which present
frequent disinformation as fact. Although this disinformation
could be easily overlooked by a person if they knew the nuances of
a topic, this hurdle can be difficult to overcome for an uninformed
student. This same issue also appears with the limitations set by
generative AI content policy.
Content policy
The next issue discussed in the AI information sessions was AI
content restrictions. These guardrails are themes and topics which
generative AI tools are programmed to avoid or not discuss. In
OpenAI’s case, these restrictions include hate, harassment, violence,
self-harm, sexual activity, shocking content, illegal activity,
deception, politics, public and personal health, and spam (OpenAI,
2023f). When prompted to generate a response including these
themes, ChatGPT and other OpenAI models will generally respond
in one of two ways: writing a disclaimer that as an AI it is unable to
discuss the requested topic and will not provide an answer or
respond to the prompt in a manner that addresses the question in a
way which does not breach its content policy (Figure 3).
The second type of response to a content-restricted prompt can
be problematic for an uninformed user because the output may lack
crucial information or contain misleading information to fulfill the
prompt without breaching the AI’s restrictions. If a user is
uninformed on the topic, they may take the problematic response
at face value and develop an erroneous understanding. This is a
particularly pressing issue for students at the beginning of their
studies.
In Classics, this is a pervasive issue. We surveyed ancient
language teachers in the Department of Classics at the University of
Reading in August 2023 to determine which content restriction
themes were discussed in their classes and texts, and every theme
except spam was selected (Figure 4) (Ross and Baines, 2023c).2 This
presents a significant hurdle for Classics students if they decide to
Figure 3. Left: OpenAI, ChatGPT 3.5, November 21, 2023 version, personal communication, generated 22 December 2023. Prompt: ‘Can you write a Latin poem about a porne?’; Right:
OpenAI, ChatGPT 3.5, November 21, 2023 version, personal communication, generated 22 December 2023. Prompt: ‘Who was the character of Megilla/Megillos in Lucian’s Dialogue
of the Courtesans?’
Figure 4. Survey data gathered from ancient language teachers in the Department of Classics at the University of Reading over summer 2023 (Ross and Baines, 2023c).
https://doi.org/10.1017/S2058631024000412 Published online by Cambridge University Press
The Journal of Classics Teaching 185
use generative AI to support their studies because many topics and
themes related to the ancient world may be avoided or
misrepresented by the AI.
These misleading responses can be corrected through
developing the training data, as seen with how ChatGPT’s general
responses related to translating Catullus 16 have changed from
providing a translation of a different, non-sexual text to consistently
providing a disclaimer about Catullus 16’s sexual content (Figure 5)
(Ross, 2023, 149–150). Although this is an improvement from
providing misleading information without context, many
unidentified cases would not be flagged unless they have been
recognised and corrected. To combat this misleading information
without context, it is crucial for knowledgeable users to check the
information and outputs related to avoided topics to ensure the
outputs are fully addressing the topic accurately with all essential
content. Any instance where it is not the case should be highlighted
and reported so that the generative AI training data can be
improved.
This leaves a particular problem for uninformed students.
Unless they are fully aware of the details related to a topic, a user
cannot be sure that the information provided by generative AI is
complete. As such, students need to be conscious of this potential
pitfall prior to using the tools so that they can make informed
judgements about the veracity of its outputs.
Environmental impact
The third aspect which we introduce to students is the
environmental impact of AI training and maintenance. When
GPT-2 was initially trained in 2019, researchers at the University of
Massachusetts, Amherst, found that training a standard AI model
produced approximately 625,000 pounds of CO2 emissions
(Strubell et al., 2019). This was equivalent to the emissions of five
US cars over the course of their entire lifetime, including
manufacturing (Hao, 2019). GPT-2 and other models during that
time are much smaller than the current models on the public
market and in training. A 2023 study by Alex de Vries at VU
Amsterdam found that accelerated development of current models
like Bing Chat and Bard present unprecedented energy
consumption rates. If Google Bard is trained and maintained with
current technological advancements, it would consume an average
of 29.3 TWh per year, which is equivalent to the entire country of
Ireland (de Vries, 2023). And this consumption is only for one
model. Considering the large number of AI models that have
greatly increased usage and development, these energy numbers
may become exponentially large.
These numbers bring to question whether we should consider
supporting the use of AI in the classroom, especially in a time of
environmental crisis. Since Reading has actioned its sustainability
plans for 2020–2026 to include a behaviour and awareness program,
it is crucial that students are aware of the environmental impact of
generative AI (University of Reading Sustainability Services, 2020).
In conversation with students following the information sessions,
several mentioned that they were astounded by the carbon
emissions of generative AI. Both these environmental
considerations alongside the problematic training data and outputs
appear to have affected students’ views on AI use.
Speaking to students
During the AI information sessions that we held in Autumn 2023
term, we gathered survey data from student participants (Ross and
Baines, 2023a, 2023b). Most of our presentations were for ancient
language students in the Department of Classics at the University
of Reading, but we also held general AI information sessions for the
wider student body in both the Classics and modern languages
departments. To ensure effective data collection, we followed a
methodology approved by the University of Reading University
Research Ethics Committee.
Prior to each presentation, we asked participants to voluntarily
complete the first portion of the survey form to gather their
opinions about generative AI. This portion of the survey was
intended to collect student’s opinion before they learned more
about potential ethical considerations. From the language student
results, we found that 92.1% of students had heard of generative AI
tools for study, and 62.9% of students had also had AI tools for
study advertised to them (Figure 6). Most of these students had
heard about conversational AI tools, generative image AI, and
AI-powered search co-pilots; ChatGPT, DALL-E, and Perplexity
were the most popular answers. However, many students
mentioned more obscure tools, including Tome, Loom AI,
SnapchatAI, WordTune, and PicsArt. The majority were hearing
Figure 5. Left: Figure 11 from Ross, 2023, p. 150; Right: OpenAI, ChatGPT 3.5, November 21, 2023 version, personal communication, generated 22 December 2023. Prompt: ‘Provide
an accurate translation of Catullus 16 in English.’
https://doi.org/10.1017/S2058631024000412 Published online by Cambridge University Press
186 Edward A. S. Ross and Jackie Baines
about these tools on social media or in conversation with friends
and classmates (Figure 7). It is clear from these results that students
not only know about generative AI, but they are constantly being
bombarded with advertisements for all sorts of tools as they are
published.
Interestingly, the usage statistics do not lean as far towards the
positive. Only 47.2% of the surveyed ancient language students had
previously used generative AI; 18% of all those surveyed used them
to support learning or assignments (Figure 8).
It is unclear if the ancient language students in these information
sessions felt pressured to answer in the negative despite several
assurances that all survey responses would be properly safeguarded
and would not return to them, especially since the usage statistics
were surprisingly low compared to public discussion. General
student metrics found around 50% of students currently enrolled in
university were using generative AI in some capacity (Coffey, 2023;
Nam, 2023). So, we compared our ancient language results to the
anonymous survey results gathered through Mentimeter at our
general AI information sessions (Table 1).
In all three sets of surveys, student knowledge about AI was
consistently over 90%, while AI usage varied greatly. The ancient
language sessions were done in small groups, while the Classics and
modern languages sessions were in larger rooms. Despite this, there
appears to be a greater apprehension for using AI among Classics
and ancient language students than modern language students.
Although group sizes may have affected our pre-presentation
results, the post-session results are consistent.
Following the presentation, we asked participants to complete
the second half of the survey once they had a fuller picture of how
generative AI works and is made. Interestingly, only 19.1% of
ancient language students said they would consider using
generative AI, while 80.9% were either against or apprehensive of
using them (Figure 9). Many students noted that they did not like
how inaccurate generative AI could be, especially due to its
elementary abilities with Latin and Ancient Greek. Furthermore,
several students indicated their support for artists and authors,
considering the ongoing Writers Guild of America (WGA) and
Screen Actors Guild – American Federation of Television and
Radio Artists (SAG-AFTRA) strike actions at the time of survey
(Anguiano and Beckett, 2023; Watercutter and Bedingfield, 2023).
Those students who did wish to use generative AI, or were
considering it, thought it could be useful for revision, language
study support, and generating additional homework questions
(Figure 10). Many of these suggested uses, however, were paired
with comments saying that they would only use the AI tool for
these purposes if its Latin and Ancient Greek abilities improved.
Figure 6. Survey data gathered from ancient language students in the Department of Classics at the University of Reading over Autumn 2023 term (Ross and Baines, 2023a).
Figure 7. Survey data gathered from ancient language students in the Department of Classics at the University of Reading over Autumn 2023 term (Ross and Baines, 2023a).
https://doi.org/10.1017/S2058631024000412 Published online by Cambridge University Press
The Journal of Classics Teaching 187
Interestingly, there were also a few students who aimed to use AI
for non-learning related purposes, such as writing professional
emails or Instagram captions.
Both Classics and modern languages students also leaned
towards rejection or apprehension of AI tools after learning about
the ethical considerations (Table 2). As seen with the pre-
presentation results, there is an increase in ‘yes’ responses in the
wider groups of participants. However, the lean towards rejection
remains consistent across all three datasets. At this stage, it appears
that informing students about the benefits and drawbacks of
generative AI provides them with the tools needed to critically
consider whether or not to use it. Students had the option to
indicate the reasons why they rejected using generative AI, and
these reasons included lack of trust in AI outputs (28.42% of all
respondents), fear of plagiarism accusations (8.74%), fear AI will
replace human work (7.10%), unethical training data (3.83%), and
support of affected artists (2.19%) (Ross and Baines, 2023a, 2023b).
It is unclear if these opinions will remain the same over the
academic year, but we can continue to scaffold student knowledge
about AI developments as time progresses.
Building guidelines
As discussed earlier, our research involved discussing AI usage with
both staff and students. It was necessary to draw up guidelines for
AI usage which clearly addressed the issues surrounding AI and
how the Department of Classics would engage with generative AI
in classes and assignments. This required a delicate balance because
there were many strong opinions about AI use in HE. In their
responses to our initial staff survey, there was a broad spread of
views (Ross and Baines, 2023c). Some were completely against AI
use and found it should be ignored, others wanted to embrace it
fully but were unsure of the best methods, and others were utterly
confused by it.
Following several discussions and information sessions, the
department determined that allowed AI use should be at the
discretion of module convenors, but all students should be
informed about the benefits and drawbacks of generative AI and
any new developments as they arise. To do this, we developed a
general guidance and citation guide which would be publicly
available to staff and students and consistently updated. The
guidance document was developed bearing in mind the many
guiding principles laid out by HE bodies in the UK and globally
(Atlas, 2023; Jisc, 2023; Quality Assurance Agency for Higher
Education, 2023; Russell Group, 2023; UNESCO International
Institute for Higher Education in Latin America and the Caribbean,
2023). The Autumn 2023 version of the guidance is attached to this
article in Supplemental Materials (Supplementary File 2).
Assignments/citations
If a Reading Classics student’s module convenor approves the use of
AI in an assignment, they are first and foremost required to
document all uses of AI they make while preparing their
assignment, whether any of it ends up in the body text or not. All
cases of AI use need to be indicated in the first footnote of an
assignment following this general method:
1The writing of this assignment is my own, and I take
responsibility for all errors. During the preparation of this
assignment, Perplexity (Perplexity AI, 9 August 2023 version)
was used to gather articles for preliminary research into this
research question.
This allows students to be accountable for their academic work and
protects them from potential plagiarism misunderstandings. The
disclaimer footnote, however, also needs to be accompanied by
proper citations and documentation.
The Department of Classics at the University of Reading
traditionally uses the Harvard citation style to cite modern sources.
Many universities have released their own versions of the Harvard
system to address the issue of citing generative AI outputs
(St.George’s University of London Library, 2023; University of
Queensland, Australia Library, 2023), and many are based on the
Bloomsbury CiteThemRight guide. These citation guides, across all
relevant citation systems, generally consider AI outputs as personal
communications (Bloomsbury Publishing, n.-d.-a, n.-d.-b, n.-d.-c,
n.-d.-d, n.-d.-e, n.-d.-f, n.-d.-g, n.-d.-h). Unfortunately, we found
that these citation styles did not require crucial information about
the AI tools used, so we created an adapted citation guide for our
students (Supplementary File 3).
Figure 8. Survey data gathered from ancient language students in the Department
of Classics at the University of Reading over Autumn 2023 term (Ross and Baines,
2023a).
Table 1. Current use of AI. Survey data gathered from ancient language, Classics, and modern languages students at the University of Reading over Autumn 2023 term
(Ross and Baines, 2023a, 2023b).
Have you heard about AI tools for study? Have you ever used an AI tool?
Yes (%) No (%) Yes (%) No, but I’ve thought about it (%) No (%)
Ancient languages (N = 89) 92.1 7.9 47.2 0.0 52.8
Classics (N = 37) 94.1 5.9 56.8 8.1 35.1
Modern languages (N = 57) 98.2 1.8 83.7 6.1 10.2
https://doi.org/10.1017/S2058631024000412 Published online by Cambridge University Press
188 Edward A. S. Ross and Jackie Baines
In order to ensure that readers know which versions of AI
models are used, we require that students provide the following
information:
• Developer name
• AI tool name
• AI tool model number
• Update version at time of use
• personal communication
• Date output was generated
e.g. (OpenAI, ChatGPT 3.5, 3 August 2023 version, personal
communication, generated 18 September 2023).
It is particularly important that citations include both the model
and version numbers in their citation because multiple different
models of generative AI tools exist at the same time and different
versions can respond to the same questions quite differently. If a
student cites an AI program itself, they should cite it following the
method for a computer program but must also provide the
developer’s name, AI tool name, AI model number, and update
version in the citation.
Since generative AI outputs are non-reproducible, the
Department of Classics at the University of Reading requires
students to maintain a screenshot folder of all AI outputs used for
producing their assignments. All AI outputs used for a particular
assignment need to be paired with a full citation, the prompt used,
and an image and attached as an appendix at the end of their
assignment. Although many generative AI tools now create stable
links to share outputs, this is not a universal feature, so we still
require a screenshot alongside any stable links.
Conclusions
It is clear from our research into conversational AI tools and their
potential ethical use in ancient language learning that the starting
point of any discussions on its use needs to begin with informing
and educating staff and students alike to provide them with a greater
understanding of such tools, their content policies, how their
training data is gathered and the environmental impact. Excessive
feelings of fear or misplaced faith in AI abound if there is a lack of
understanding of how the conversational tools work, their current
state of accuracy and how they gather their data. From our pre-
presentation surveys, it is evident that most students know of and
have used AI tools. However, once they are presented with more
in-depth information, they can make an informed judgement about
the ethical use of AI and, as a result, show a greater reluctance about
using it. The students of Latin and Ancient Greek were sceptical
about using AI tools after the presentation once they realised the
inadequacies of such tools. Modern languages students are aware of
the skill of AI tools for their studies but fewer than 20% indicated
that they would use them after attending the information session.
From a pedagogical point of view AI conversational tools may
have a place in Latin and Ancient Greek beginner and intermediate
classes as an additional tool for students to use while studying on
their own. It is possible for students outside the classroom, with
appropriate direction, to create quizzes, to revise grammar points,
to check homework answers, to parse and to translate and compose
short passages (Ross, 2023).
The development of conversational AI tools continues to
accelerate and become more sophisticated in all its aspects. This
Figure 9. Survey data gathered from ancient language students in the Department of
Classics at the University of Reading over Autumn 2023 term (Ross and Baines, 2023a).
Figure 10. Survey data gathered from ancient language students in the Department of Classics at the University of Reading over Autumn 2023 term (Ross and Baines, 2023a).
https://doi.org/10.1017/S2058631024000412 Published online by Cambridge University Press
The Journal of Classics Teaching 189
highlights the need to continue to be vigilant and to check how well
the new developments perform in relation to Latin and Ancient
Greek along with other areas studied within the field of Classics. If
we continue to do this and keep our staff and students informed,
the use of conversational AI tools provides an exciting and valuable
addition to our teaching resources.
As a continuation of our research, with the aid of focus groups
of students, we will test a variety of applications for their output
effectiveness and reliability when using a specific set of guiding
phrases to be used to assist Latin and Ancient Greek language
learning. This includes ChatGPT 3.5/4, Bard AI, Claude-2, Bing
Chat, and custom GPTs through OpenAI’s new GPT system. We
also will be updating our guidance documents as new tools and
guiding principles are released.
Supplementary material
The supplementary material for this article can be found at https://
doi.org/10.1017/S2058631024000412.
Acknowledgments
This article is part of the wider ‘ChatGPT: A Conversational
Language Study Tool’ project at the University of Reading. This
project has been reviewed by the University of Reading University
Research Ethics Committee and has been given a favourable ethical
opinion for conduct. The project is supported by a Teaching and
Learning Enhancement Project (TLEP) grant from the University
of Reading and an Education Grant from the Council of University
Classics Departments. Special thanks to Jacinta Hunter, Fleur
McRitchie Pratt, Nisha Patel, Luke Edwards, and Rikard Roitto for
bringing changes in AI outputs to our attention.
Notes
1 Taylor (2016) expects the answer to be: ‘ὁ γίγας ὃς ἀνθρώπους ἐσθίει οὔκ
ἐστιν νῦν ἐν τῷ ἀγρῷ.’
2 Many artists consider the Classics field to be safe spaces to discuss the content
policy restricted issues listed by OpenAI (2023f). The temporal distances of
ancient texts from our current context allows us to discuss difficult issues in a
distanced but critical manner. For more on this concept of Classics as a safe
space, see Ewans and Johnson (2019).
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