ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education

Abstract

Large language models represent a significant advancement in the field of AI. The underlying technology is key to further innovations and, despite critical views and even bans within communities and regions, large language models are here to stay. This commentary presents the potential benefits and challenges of educational applications of large language models, from student and teacher perspectives. We briefly discuss the current state of large language models and their applications. We then highlight how these models can be used to create educational content, improve student engagement and interaction, and personalize learning experiences. With regard to challenges, we argue that large language models in education require teachers and learners to develop sets of competencies and literacies necessary to both understand the technology as well as their limitations and unexpected brittleness of such systems. In addition, a clear strategy within educational systems and a clear pedagogical approach with a strong focus on critical thinking and strategies for fact checking are required to integrate and take full advantage of large language models in learning settings and teaching curricula. Other challenges such as the potential bias in the output, the need for continuous human oversight, and the potential for misuse are not unique to the application of AI in education. But we believe that, if handled sensibly, these challenges can offer insights and opportunities in education scenarios to acquaint students early on with potential societal biases, criticalities, and risks of AI applications. We conclude with recommendations for how to address these challenges and ensure that such models are used in a responsible and ethical manner in education.

Full text

A Position Paper
ChatGPT for Good? On Opportunities and
Challenges of Large Language Models for Education
Enkelejda Kasneci1*, Kathrin Sessler1, Stefan K¨
uchemann2, Maria Bannert1, Daryna
Dementieva1, Frank Fischer2, Urs Gasser1, Georg Groh1, Stephan G¨
unnemann1, Eyke
H¨
ullermeier2, Stephan Krusche1, Gitta Kutyniok2, Tilman Michaeli1, Claudia Nerdel1, J ¨
urgen
Pfeffer1, Oleksandra Poquet1, Michael Sailer2, Albrecht Schmidt2, Tina Seidel1, Matthias
Stadler2, Jochen Weller2, Jochen Kuhn2, Gjergji Kasneci3
Abstract
Large language models represent a significant advancement in the field of AI. The underlying technology is key
to further innovations and, despite critical views and even bans within communities and regions, large language
models are here to stay. This position paper presents the potential benefits and challenges of educational
applications of large language models, from student and teacher perspectives. We briefly discuss the current
state of large language models and their applications. We then highlight how these models can be used to create
educational content, improve student engagement and interaction, and personalize learning experiences. With
regard to challenges, we argue that large language models in education require teachers and learners to develop
sets of competencies and literacies necessary to both understand the technology as well as their limitations and
unexpected brittleness of such systems. In addition, a clear strategy within educational systems and a clear
pedagogical approach with a strong focus on critical thinking and strategies for fact checking are required to
integrate and take full advantage of large language models in learning settings and teaching curricula. Other
challenges such as the potential bias in the output, the need for continuous human oversight, and the potential
for misuse are not unique to the application of AI in education. But we believe that, if handled sensibly, these
challenges can offer insights and opportunities in education scenarios to acquaint students early on with potential
societal biases, criticalities, and risks of AI applications. We conclude with recommendations for how to address
these challenges and ensure that such models are used in a responsible and ethical manner in education.
Keywords
Large language models — Artificial Intelligence — Education — Educational Technologies
1Technical University of Munich, Germany
2Ludwig-Maximilians-Universit¨
at M¨
unchen, Germany
3University of T¨
ubingen, Germany
*Corresponding author: Enkelejda.Kasneci@tum.de
1. Introduction
Large language models, such as GPT-3 [
1
], have made signifi-
cant advancements in natural language processing (NLP) in
recent years. These models are trained on massive amounts
of text data and are able to generate human-like text, answer
questions, and complete other language-related tasks with
high accuracy.
One key development in the area is the use of trans-
former architectures [
2
,
3
] and the underlying attention mech-
anism [
4
], which have greatly improved the ability of auto-
regressive
1
self-supervised
2
language models to handle long-
1
Auto-regressive because the model uses its previous predictions as input
for new predictions.
2
Self-supervised because they learn from the data itself, rather than being
explicitly provided with correct answers as in supervised learning.
range dependencies in natural-language texts. The transformer
architecture, introduced in [
4
], uses the self-attention mecha-
nism to determine the relevance of different parts of the input
when generating predictions. This allows the model to better
understand the relationships between words in a sentence, re-
gardless of their position.
Another important development is the use of pre-training,
where a model is first trained on a large dataset before being
fine-tuned on a specific task. This has proven to be an effec-
tive technique for improving performance on a wide range of
language tasks [
5
]. For example, BERT [
2
] is a pre-trained
transformer-based encoder model that can be fine-tuned on
various NLP tasks, such as sentence classification, question
answering and named entity recognition. In fact, the so-called
few-shot learning capability of large language models to be

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education — 2/13
efficiently adapted to down-stream tasks or even other seem-
ingly unrelated tasks (e.g., as in transfer learning) has been
empirically observed and studied for various natural-language
tasks [
6
], e.g., more recently in the context of generating
synthetic and yet realistic heterogeneous tabular data [7].
Recent advancements also include GPT-3 [
1
] and Chat-
GPT [
8
], which were trained on a much larger datasets, i.e.,
texts from a very large web corpus, and have demonstrated
state-of-the-art performance on a wide range of natural-language
tasks ranging from translation to question answering, writ-
ing coherent essays, and computer programs. Additionally,
extensive research has been conducted on fine-tuning these
models on smaller datasets and applying transfer learning to
new problems. This allows for improved performance on
specific tasks with smaller amount of data.
While large language models have made great strides in
recent years, there are still many limitations that need to be
addressed. One major limitation is the lack of interpretabil-
ity, as it is difficult to understand the reasoning behind the
model’s predictions. There are ethical considerations, such
as concerns about bias and the impact of these models, e.g.,
on employment, risks of misuse and inadequate or unethical
deployment, loss of integrity, and many more. Overall, large
language models will continue to push the boundaries of what
is possible in natural language processing. However, there
is still much work to be done in terms of addressing their
limitations and the related ethical considerations.
1.1 Opportunities for Learning
The use of large language models in education has been iden-
tified as a potential area of interest due to the diverse range of
applications they offer. Through the utilization of these mod-
els, opportunities for enhancement of learning and teaching
experiences may be possible for individuals at all levels of edu-
cation, including primary, secondary, tertiary and professional
development.
For elementary school students, large language models
can assist in the development of reading and writing skills
(e.g., by suggesting syntactic and grammatical corrections), as
well as in the development of writing style and critical think-
ing skills. These models can be used to generate questions
and prompts that encourage students to think critically about
what they are reading and writing, and to analyze and inter-
pret the information presented to them. Additionally, large
language models can also assist in the development of reading
comprehension skills by providing students with summaries
and explanations of complex texts, which can make reading
and understanding the material easier.
For middle and high school students, large language
models can assist in the learning of a language and of writ-
ing styles for various subjects and topics, e.g., mathematics,
physics, language and literature, and other subjects. These
models can be used to generate practice problems and quizzes,
which can help students to better understand, contextual-
ize and retain the material they are learning. Additionally,
large language models can also assist in the development of
problem-solving skills by providing students with explana-
tions, step-by-step solutions, and interesting related questions
to problems, which can help them to understand the reasoning
behind the solutions and develop analytical and out-of-the-box
thinking.
For university students, large language models can assist
in the research and writing tasks, as well as in the development
of critical thinking and problem-solving skills. These models
can be used to generate summaries and outlines of texts, which
can help students to quickly understand the main points of a
text and to organize their thoughts for writing. Additionally,
large language models can also assist in the development of
research skills by providing students with information and re-
sources on a particular topic and hinting at unexplored aspects
and current research topics, which can help them to better
understand and analyze the material.
For group & remote learning, large language models
can be used to facilitate group discussions and debates by
providing a discussion structure, real-time feedback and per-
sonalized guidance to students during the discussion. This
can help to improve student engagement and participation. In
collaborative writing activities, where multiple students work
together to write a document or a project, language models
can assist by providing style and editing suggestions as well
as other integrative co-writing features. For research purposes,
such models can be used to span the range of open research
questions in relation to already researched topics and to au-
tomatically assign the questions and topics to the involved
team members. For remote tutoring purposes, they can be
used to automatically generate questions and provide practice
problems, explanations, and assessments that are tailored to
the students’ level of knowledge so that they can learn at their
own pace.
To empower learners with disabilities, large language
models can be used in combination with speech-to-text or text-
to-speech solutions to help people with visual impairment. In
combination with the previously mentioned group and remote
tutoring opportunities, language models can be used to de-
velop inclusive learning strategies with adequate support in
tasks such as adaptive writing, translating, and highlighting
of important content in various formats. However, it is im-
portant to note that the use of large language models should
be accompanied by the help of professionals such as speech
therapists, educators, and other specialists that can adapt the
technology to the specific needs of the learner’s disabilities.
For professional training, large language models can
assist in the development of language skills that are specific
to a particular field of work. They can also assist in the
development of skills such as programming, report writing,
project management, decision making and problem-solving.
For example, large language models can be fine-tuned on
a domain-specific corpus (e.g. legal, medical, IT) in order
to generate domain-specific language and assist learners in
writing technical reports, legal documents, medical records etc.

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education — 3/13
They can also generate questions and prompts that encourage
learners to think critically about their work and to analyze and
interpret the information presented to them.
In conclusion, large language models have the potential to
provide a wide range of benefits and opportunities for students
and professionals at all stages of education. They can assist
in the development of reading, writing, math, science, and
language skills, as well as providing students with personal-
ized practice materials, summaries and explanations, which
can help to improve student performance and contribute to en-
hanced learning experiences. Additionally, large language
models can also assist in research, writing, and problem-
solving tasks, and provide domain-specific language skills
and other skills for professional training. However, as pre-
viously mentioned, the use of these models should be done
with caution, as they also have limitations such as lack of
interpretability and potential for bias, unexpected brittleness
in relatively simple tasks [9] which need to be addressed.
1.2 Opportunities for Teaching
Large language models, such as ChatGPT, have the potential
to revolutionize teaching and assist in teaching processes.
Below we provide only a few examples of how these models
can benefit teachers:
For personalized learning, teachers can use large lan-
guage models to create personalized learning experiences for
their students. These models can analyze student’s writing
and responses, and provide tailored feedback and suggest ma-
terials that align with the student’s specific learning needs.
Such support can save teachers’ time and effort in creating
personalized materials and feedback, and also allow them to
focus on other aspects of teaching, such as creating engaging
and interactive lessons.
For lesson planning, large language models can also as-
sist teachers in the creation of (inclusive) lesson plans and
activities. Teachers can input to the models the corpus of
document based on which they want to build a course. The
output can be a course syllabus with short description of each
topic. Language models can also generate questions and
prompts that encourage the participation of people at different
knowledge and ability levels, and elicit critical thinking and
problem-solving. Moreover, they can be used to generate tar-
geted and personalized practice problems and quizzes, which
can help to ensure that students are mastering the material.
For language learning, teachers of language classes can
use large language models in an assistive way, e.g., to high-
light important phrases, generate summaries and translations,
provide explanations of grammar and vocabulary, suggest
grammatical or style improvements and assist in conversation
practice. Language models can also provide teachers with
adaptive and personalized means to assist students in their
language learning journey, which can make language learning
more engaging and effective for students.
For research and writing, large language models can
assist teachers of university and high school classes to com-
plete research and writing tasks (e.g., in seminar works, paper
writing, and feedback to students) more efficiently and effec-
tively. The most basic help can happen at a syntactic level,
i.e., identifying and correcting typos. At a semantic level,
large language models can be used to highlight (potential)
grammatical inconsistencies and suggest adequate and person-
alized improvement strategies. Going further, these models
can be used to identify possibilities for topic-specific style
improvement. They can also be used to generate summaries
and outlines of challenging texts, which can help teachers and
researchers to highlight the main points of a text in a way
that is helpful for further deep dive and understanding of the
content in question.
For professional development, large language models
can also assist teachers by providing them with resources,
summaries, and explanations of new teaching methodologies,
technologies, and materials. This can help teachers stay up-to-
date with the latest developments and techniques in education,
and contribute to the effectiveness of their teaching. They can
be used to improve the clarity of the teaching materials, locate
information or resources that professionals may be in need for
as they learn on the job, as well as used for on-the-job training
modules that require presentation and communication skills.
For assessment and evaluation, teachers can use large
language models to semi-automate the grading of student work
by highlighting potential strengths and weakness of the work
in question, e.g., essays, research papers, and other writing
assignments. This can save teachers a significant amount of
time for tasks related to individualized feedback to students.
Furthermore, large language models can also be used to check
for plagiarism, which can help to prevent cheating. Hence,
large language models can help teachers to identify areas
where students are struggling, which adds to a more accurate
assessments of student learning development and challenges.
Targeted instruction provided by the models can be used to
help students excel and to provide opportunities for further
development.
The acquaintance of students with AI challenges related
to the potential bias in the output, the need for continuous hu-
man oversight, and the potential for misuse of large language
models are not unique to education. In fact, these challenges
are inherent to transformative digital technologies. Thus, we
believe that, if handled sensibly by the teacher, these chal-
lenges can be insightful in learning and education scenarios to
acquaint students early on with potential societal biases, and
risks of AI application.
In conclusion, large language models have the potential
to revolutionize teaching from a teacher’s perspective by pro-
viding teachers with a wide range of tools and resources that
can assist with lesson planning, personalized content creation,
differentiation and personalized instruction, assessment, and
professional development. Overall, large language models
have the potential to be a powerful tool in education, and
there are a number of ongoing research efforts exploring its
potential applications in this area.

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education — 4/13
2. Current Research and Applications of
Language Models in Education
2.1 Overview of Current Large Language Models
The GPT (Generative Pre-trained Transformer) [
10
] model
developed by OpenAI was the first large language model that
was publicly released in 2018. GPT was able to generate
human-like text, answer questions, and assist in tasks, such as
translation and summarization, through human-like comple-
tion. Based on this initial model, OpenAI later released the
GPT-2 and GPT-3 models with more advanced capabilities.
It can be argued that the release of GPT marked a significant
milestone in the field of NLP and has opened up many ways
for dissemination, both in research and industrial applications.
Another model that was released by Google Research in
2018 is BERT (Bidirectional Encoder Representations from
Transformers [
2
]), which is also based on a transformer ar-
chitecture and is pre-trained on a massive dataset of text data
on two unsupervised tasks, namely masked language mod-
eling (to predict missing parts in a sentence and learn their
context) and next sentence prediction (to learn plausible sub-
sequent sentences of a given sentence) with the aim to learn
the broader context of words in various topics.
One year later in 2019, Google AI released XLNet [
11
],
which is trained using a process called permutation language
modeling and enables XLNet to cope with tasks that involve
understanding the dependencies between words in a sentence,
e.g., natural language inference and question answering. An-
other model developed by Google Research was T5 (Text-to-
Text Transfer Transformer) [
12
], which was released in 2020.
Like the predecessor models, T5 is also transformer-based
model trained on a massive text dataset with the key feature
being its ability to perform many NLP tasks with a single
pre-training and fine-tuning pipeline.
In parallel with Open AI and Google, Facebook AI devel-
oped a large language model called RoBERTa (Robustly Opti-
mized BERT Pre-training) [
13
], which was released in 2019.
RoBERTa is a variant of the BERT model which uses dynamic
masking instead of static masking during pre-training. Addi-
tionally, RoBERTa is trained on a much larger dataset, and
hence clearly outperformed BERT and other models such as
GPT-2 and XLNet by the time of its release.
Currently, the most widely used and the largest available
language model is GPT-3, which was also pre-trained on a
massive text dataset (including books, articles, and websites,
among other sources) and has 175 billion parameters. As all
other previously described language models, GPT-3 uses a
transformer architecture, which allows it to efficiently process
sequential data and generate more coherent and contextually
adjusted text. Indeed, text generated by GPT-3 is almost
indistinguishable from human-written text [
14
]. With the
ability to perform zero-shot learning, GPT-3 can cope with
tasks it has not been specifically trained on, providing hence
enormous opportunities for applications, from automation
(summarizing, completing texts from bullet points), to dialog
systems, chatbots, and creative writing.
Just recently, the BigScience-community developed and
released the large language model BLOOM (BigScience Large
Open-science Open-access Multilingual Language Model) [
15
]
as on open-source joint project by HuggingFace, GENCI and
IDRIS
3
. The aim of this project was to provide a transparently
trained multi-lingual language model for the academic and
non-profit community. BLOOM is based on the same trans-
former architecture as the models from the GPT-family with
only minor structural changes, but the training data was explic-
itly chosen to cover 46 natural languages and 13 programming
languages, resulting in a data volume of 1.6TB.
2.2
Review of Research Applying Large Language
Models in Education
In the following, we provide an overview of research works
employing large language models in education that were pub-
lished since the release of the first large language model in
2018. These studies have been discussed in the following
according to their target groups, i.e., learners or teachers.
Learners’ perspective. From a student’s perspective, large
language models can be used in multiple ways to assist the
learning process. One example is in the creation and design of
educational content. For example, researchers have used large
language models to generate interactive educational materials
such as quizzes and flashcards, which can be used to improve
student learning and engagement [
16
,
17
]. More specifically,
in a recent work by Dijkstra et al. [
17
], researchers have used
GPT-3 to generate multiple-choice questions and answers
for a reading comprehension task and argue that automated
generation of quizzes not only reduces the burden of manual
quiz design for teachers but, above all, provides a helpful tool
for students to train and test their knowledge while learning
from textbooks and during exam preparation [17].
In another recent work, GPT-3 was employed as a ped-
agogical agent to stimulate the curiosity of children and en-
hance question-asking skills [
18
]. More specifically, the au-
thors automated the generation of curiosity-prompting cues
as an incentive for asking more and deeper questions. Ac-
cording to their results, large language models not only bear
the potential to significantly facilitate the implementation of
curiosity-stimulating learning but can also serve as an efficient
tool towards an increased curiosity expression [18].
In computing education, a recent work by MacNeil et
al. [
19
] has employed GPT-3 to generate code explanations.
Despite several open research and pedagogical questions that
need to be further explored, this work has successfully demon-
strated the potential of GPT-3 to support learning by explain-
ing aspects of a given code snippet.
For a data science course, Bhat et al. [
20
] proposed a
pipeline for generating assessment questions based on a fine-
tuned GPT3 model on text-based learning materials. The gen-
erated questions were further evaluated with regard to their
usefulness to the learning outcome based on automated label-
3https://huggingface.co/bigscience/bloom

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education — 5/13
ing by a trained GPT-3 model and manual reviews by human
experts. The authors reported that the generated questions
were rated favorably by human experts, promoting thus the
usage of large language models in data science education [
20
].
Students can learn from each other by peer-reviewing and
assessing each other’s solutions. This, of course, has the best
effect when the given feedback is comprehensive and of high
quality. For example, Jia et al. [
21
] showed how BERT can
be used to evaluate the peer assessments so that students can
learn to improve their feedback.
In a recent review on conversational AI in language edu-
cation, the authors found that there are five main applications
of conversational AI during teaching [22], the most common
one being the use of large language models as a conversa-
tional partner in a written or oral form, e.g., in the context
of a task-oriented dialogue that provides language practice
opportunities such as pronunciation [
23
]. Another application
is to support students when they experience foreign language
learning anxiety [
24
] or have a lower willingness to commu-
nicate [
25
]. In [
26
], the application of providing feedback, as
a needs analyst, and evaluator when primary school students
practice their vocabulary was explored. The authors of [
27
]
found that a chatbot that is guided by a mind map is more suc-
cessful in supporting students by providing scaffolds during
language learning than a conventional AI chatbot.
A recent work in the area of medical education by Kung et
al. [
28
] explored the performance of ChatGPT on the United
States Medical Licensing Exam. According to the evaluation
results, the performance of ChatGPT on this test was at or
near the passing threshold without any domain fine-tuning.
Based on these results, the authors argue that large language
models might be a powerful tool to assist medical education
and eventually clinical decision-making processes [28].
Teachers’ perspective. As the rate of adoption of AI in
education is still slow compared to other fields, such as indus-
trial applications (e.g., finance, e-commerce, automotive) or
medicine, there are less studies considering the use of large
language models in education [
29
]. A recent review of oppor-
tunities and challenges of chatbots in education pointed out
that the studies related to chatbots in education are still in an
early stage, with few empirical studies investigating the use of
effective learning designs or learning strategies [
30
]. There-
fore, we discuss first the teachers’ perspectives concerning AI
and Learning Analytics in education and transfer these on the
much newer field of large language models.
In this view, a pilot study with European teachers indi-
cates a positive attitude towards AI for education and a high
motivation to introduce AI-related content at school. Overall,
the teachers from the study seemed to have a basic level of
digital skills but low AI-related skills [
31
]. Another study
with Nigerian teachers emphasized that the willingness and
readiness of teachers to promote AI are key prerequisites for
the integration of AI-based technologies in education [
32
].
Along the same lines, the results of a study with teachers from
South Korea indicate that teachers with constructivist beliefs
are more likely to integrate educational AI-based tools than
teachers with transmissive orientations [
33
]. Furthermore, per-
ceived usefulness, perceived ease of use, and perceived trust in
these AI-based tools are determinants to be considered when
predicting their acceptance by the teachers. Similar results
concerning teachers attitudes towards chatbots in education
were reported in [
34
]: perceiving the AI chatbot as easy-to-
use and useful leads to greater acceptance of the chatbot. As
for the chatbots’ features, formal language by a chatbot leads
to a higher intention of using it.
As it seems that teachers’ perspectives on the general use
of AI in education have a lot in common with the mentioned at-
titude towards chatbots in particular, a responsible integration
of AI into education by involving the expertise of different
communities is crucial [35].
Recent works addressing the use of large language models
from the teacher’s perspective have focused on the automated
assessment of student answers, adaptive feedback, and the
generation of teaching content.
For example, a recent work by Moore et al. [
36
] employed
a fine-tuned GPT-3 model to evaluate student-generated an-
swers in a learning environment for chemistry education [
36
].
The authors argue that large language models might (espe-
cially when fine-tuned to the specific domain) be a powerful
tool to assist teachers in the quality and pedagogical evalua-
tion of student answers [
36
]. In addition, the following studies
examined NLP-based models for generating automatic adap-
tive feedback: Zhu et al. [
37
] examined an AI-based feedback
system incorporating automated scoring technologies in the
context of a high school climate activity task. The results show
that the feedback helped students revise their scientific argu-
ments. Sailer et al. [
38
] used NLP-based adaptive feedback
in the context of diagnosing students’ learning difficulties in
teacher education. In their experimental study, they found that
pre-service teachers who received adaptive feedback were
better able to justify their diagnoses than prospective teachers
who received static feedback. Bernius et al. [
39
] used NLP-
based models to generate feedback for textual student answers
in large courses, where grading effort could be reduced by
up to 85% with a high precision and an improved quality
perceived by the students.
Large language models can not only support the assess-
ment of student’s solutions but also assist in the automatic
generation of exercises. Using few-shot learning, [
40
] showed
that the OpenAI Codex model is able to provide a variety of
programming tasks together with the correct solution, auto-
mated tests to verify the student’s solutions, and additional
code explanations. With regard to testing factual knowledge
in general, [
41
] proposed a framework to automatically gen-
erate question-answer pairs. This can be used in the creation
of teaching materials, e.g., for reading comprehension tasks.
Beyond the generation of the correct answer, transformer
models are also able to create distractor answers, as needed
for the generation of multiple choice questionnaires [
42
,
43
].
Bringing language models to mathematics education, sev-

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education — 6/13
eral works discuss the automatic generation of math word
problems [
44
,
45
,
46
], which combines the challenge of un-
derstanding equations and putting them into the appropriate
context.
Finally, another recent work [
47
] investigated the capabil-
ity of state-of-the-art conversational agents to adequately reply
to a student in an educational dialogue. Both models used in
this work (Blender and GPT-3) were capable of replying to
a student adequately and generated conversational dialogues
that conveyed the impression that these models understand the
learner (in particular Blender). They are however well behind
human performance when it comes to helping the student [
47
],
thus emphasizing the need for further research.
3. Opportunities for Innovative
Educational Technologies
Looking forward, large language models bear the potential to
considerably improve digital ecosystems for education, such
as environments based on Augmented Reality (AR), Virtual
Reality (VR) [
48
,
49
], and other related digital experiences.
Specifically, they can be used to amplify several key fac-
tors, which are crucial for the immersive interaction of users
with digital content. For example, large language models
can considerably improve the natural language processing
and understanding capabilities of an AR/VR system to enable
an effective natural communication and interaction between
users and the system (e.g., virtual teacher or virtual peers).
The latter has been identified early on as a key usability aspect
for immersive educational technologies [
50
] and is in general
seen as a key factor for improving the interaction between
humans and AI systems [51].
Large language models can also be used to develop more
natural and sophisticated user interfaces by exploiting their
ability to generate contextualized, personalized, and diverse
responses to natural language questions asked by users. Fur-
thermore, their ability to answer natural language questions
across various domains can facilitate the integration of diverse
digital applications into a unified framework or application,
which is also critical for expanding the bounds of educational
possibilities and experiences [52,49].
In general, the ability of these models to generate contextu-
alized natural language texts, code for various implementation
tasks [
53
] as well as various types of multimedia content
(e.g., in combination with other AI systems, such as DALL-
E [
54
]) can enable and scale the creation of compelling and
immersive digital (e.g., AR/VR) experiences. From gamifica-
tion to detailed simulations for immersive learning in digital
environments, large language models are a key enabling tech-
nology. To fully realize this potential, however, it is important
to consider not only technical aspects but also ethical, legal,
ecological and social implications.
In the following section, we take a brief look at the risks re-
lated to the application on large language models in education
and provide corresponding mitigation strategies.
4. Key Challenges and Risks Related to
the Application of Large Language
Models in Education
Copyright Issues. When we train large language models on
a task to produce education-related content – course syllabus,
quizzes, scientific paper – the mode should be trained on
examples of such texts. During the generation for a new
prompt, the answer may contain a full sentence or even a
paragraph seen in the training set, leading to copyright and
plagiarism issues.
Important steps to responsibly mitigate such an issue can
be the following:
•
Asking the authors of the original documents trans-
parently (i.e., purpose and policy of data usage) for
permission to use their content for training the model
•
Compliance with copyright terms for open-source con-
tent
•
Inheritance and detailed terms of use for the content
generated by the model
•
Informing and raising awareness of the users about
these policies
Bias and fairness. Large language models can perpetuate
and amplify existing biases and unfairness in society, which
can negatively impact teaching and learning processes and
outcomes. For example, if a model is trained on data that
is biased towards certain groups of people, it may produce
results that are unfair or discriminatory towards those groups
(e.g., local knowledge about minorities such as small ethnic
groups or cultures can fade into the background). Thus, it is
important to ensure that the training data or the data used for
fine-tuning on down-stream tasks for the model is diverse and
representative of different groups of people. Regular monitor-
ing and testing of the model’s performance on different groups
of people can help identify and address any biases early on.
Hence, human oversight in the process is indispensable and
critical for the mitigation of bias and beneficial application of
large language models in education.
More specifically, a responsible mitigation strategy would
focus on the following key aspects:
•
A diverse set of data to train or fine-tune the model, to
ensure that it is not biased towards any particular group
•
Regular monitoring and evaluation of the model’s per-
formance (on diverse groups of people) to identify and
address any biases that may arise
•
Fairness measures and bias-correction techniques, such
as pre-processing or post-processing methods
•
Transparency mechanisms that enable users to compre-
hend the model’s output, and the data and assumptions
that were used to generate it

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education — 7/13
•
Professional training and resources to educators on how
to recognize and address potential biases and other fail-
ures in the model’s output
•
Continuous updates of the model with diverse, unbiased
data, and supervision of human experts to review the
results
Learners may rely too heavily on the model. The effort-
lessly generated information could negatively impact their
critical thinking and problem-solving skills. This is because
the model simplifies the acquisition of answers or informa-
tion, which can amplify laziness and counteract the learners’
interest to conduct their own investigations and come to their
own conclusions or solutions.
To encounter this risk, it is important to be aware of the
limitations of large language models and use them only as
a tool to support and enhance learning [
55
], rather than as
a replacement for human authorities and other authoritative
sources. Thus a responsible mitigation strategy would focus
on the following key aspects:
•
Raising awareness of the limitations and unexpected
brittleness of large language models and AI systems in
general (i.e., experimenting with the model to build an
own understanding of the workings and limitations)
•
Using language models to generate hypotheses and ex-
plore different perspectives, rather than just to generate
answers
•
Strategies to use other educational resources (e.g., books,
articles) and other authoritative sources to evaluate and
corroborate the factual correctness of the information
provided by the model (i.e., encouraging learners to
question the generated content)
•
Incorporating critical thinking and problem-solving ac-
tivities into the curriculum, to help students develop
these skills
•
Incorporating human expertise and teachers to review,
validate and explain the information provided by the
model
It is important to note that the use of large language models
should be integrated into the curriculum in a way that com-
plements and enhances the learning experience, rather than
replacing it.
Teachers may become too reliant on the models. Using
large language models can provide accurate and relevant infor-
mation, but they cannot replace the creativity, critical thinking,
and problem-solving skills that are developed through human
instruction. It is therefore important for teachers to use these
models as a supplement to their instruction, rather than a
replacement. Thus, crucial aspects to mitigate the risk of
becoming too reliant on large language models are:
•
The use of language models only as as a complementary
supplement to the generation of instructions
•
Ongoing training and professional development for
teachers, enabling them to stay up-to-date on the best-
practise use of language models in the classroom to
elicit and promote creativity and critical thinking
•
Critical thinking and problem-solving activities through
the assistance of digital technologies as an integral part
of the curriculum to ensure that students are developing
these skills
•
Engagement of students in creative and independent
projects that allow them to develop their own ideas and
solutions
•
Monitoring and evaluating the use of language models
in the classroom to ensure that they are being used ef-
fectively and not negatively impacting student learning
•
Incentives for teachers and schools to develop (inclu-
sive, collaborative, and personalized) teaching strate-
gies based on large language models and engage stu-
dents in problem-solving processes such as retrieving
and evaluating course/assignment-relevant information
using the models and other sources
Lack of understanding and expertise. Many educators and
educational institutions may not have the knowledge or exper-
tise to effectively integrate new technologies in their teaching
[
56
]. This particularly applies to the use and integration of
large language models into teaching practice. Educational
theory has long since suggested ways of integrating novel
tools into educational practice (e.g., [
57
]). As with any other
technological innovation, integrating large language models
into effective teaching practice requires understanding their
capabilities and limitations, as well as how to effectively use
them to supplement or enhance specific learning processes.
There are several ways to address these challenges and
encounter this risk:
•
Research on the challenges of large language models in
education by investigating existing educational models
of technology integration, students’ learning processes
and transfer them to the context of large language mod-
els, as well as developing a new educational theory
specifically for the context of large language models
•
Assessing the needs of the educators and students and
provide case-based guidance (e.g., for the secure ethical
use of large language models in education scenarios)
•
Demand-oriented Training and professional develop-
ment opportunities for educators and institutions to
learn about the capabilities and potential uses of large
language models in education, as well as providing
best practices for integrating them into their teaching
methods

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education — 8/13
•
Open educational resources (e.g., tutorials, studies, use
cases, etc.) and Guidelines for educators and institu-
tions to access and learn about the use of language
models in education
•
Incentives for collaboration and community building
(e.g., professional learning communities) among edu-
cators and institutions that are already using language
models in their teaching practice, so they can share their
knowledge and experience with others
•
Regular analysis and feedback on the use of language
models to ensure their effective use and make adjust-
ments as necessary
Difficulty to distinguish model-generated from student–
generated answers. It is becoming increasingly difficult to
distinguish whether a text is machine- or human-generated,
presenting an additional major challenge to teachers and ed-
ucators [
58
,
59
,
60
,
61
]. As a result, the New York City’s
Department of Education recently banned ChatGPT from
schools’ devices and networks [62].
Just recently, Cotton et al. [
60
] proposed several strate-
gies to detect work that has been generated by large language
models, and specifically ChatGPT. In addition, tools, such as
the recently released GPTZero [
63
], which uses perplexity,
as a measure that hints at generalization capabilities (of the
agent by which the text was written), to detect AI involvement
in text writing, are expected to provide additional support.
More advanced techniques aim at watermarking the content
generated by language models [
64
,
65
], e.g., by biasing the
content generation towards terms, which are rather unlikely
to be jointly used by humans in a text passage. In the long
run, however, we believe that developing curricula and instruc-
tions that encourage the creative and evidence-based use of
large language models will be the key to solving this problem.
Hence, a reasonable mitigation strategy for this risk should
focus on:
•
Research on transparency, explanation and analysis
techniques and measures to distinguish machine- from
human-generated text
•
Incentives and support to develop curricula and instruc-
tions that require the creative and complementary use
of large language models
Cost of training and maintenance. The maintenance of
large language models could be a financial burden for schools
and educational institutions, especially those with limited
budgets. To address this challenge, the use of pre-trained mod-
els and cloud technology in combination with cooperative
schemes for usage in partnership with institutions and com-
panies can serve as a starting point. Specifically, a mitigation
strategy for this risk should focus on the following aspects:
•
Use of pre-trained open-source models, which can be
fine-tuned for specific tasks
•
Development and exploration of partnerships with pri-
vate companies, research institutions as well as govern-
mental and non-profit organizations that can provide
financial support, resources and expertise to support the
use of large language models in education
•
Shared costs and cooperative use of scalable (e.g., cloud)
computing services that provide access to powerful
computational resources at a low cost
•
Use of the model primarily for high-value educational
tasks, such as providing personalized and targeted learn-
ing experiences for students (i.e., assignment of lower
priority to low-value tasks)
•
Research and development of compression, distillation,
and pruning techniques to reduce the size of the model,
the data, and the computational resources required
Data privacy and security. The use of large language mod-
els in education raises concerns about data privacy and secu-
rity, as student data is often sensitive and personal. This can
include concerns about data breaches, unauthorized access to
student data, and the use of student data for purposes other
than education.
Some specific focus areas to mitigate privacy and security
concerns when using large language models in education are:
•
Development and implementation of robust data privacy
and security policies that clearly outline the collection,
storage, and use of student data in compliance with
regulation (e.g., GDPR, HIPAA, FERPA) and ethical
standards
•
Transparency towards students and their families about
the data collection, storage, and use practices, with
obligatory consent before data collection and use
•
Modern technologies and measures to protect the col-
lected data from unauthorized access, breaches, or un-
ethical use (e.g., anonymized data and secure infras-
tructures with modern means for encryption, federation,
privacy-preserving analytics, etc.)
•
Regular audits of the data privacy and security measures
in place to identify and address any potential vulnera-
bilities or areas for improvement
•
Incident response plan to quickly respond and mitigate
any data breaches or unauthorized access to data
•
Education and awareness of the staff, i.e., educators
and students about the data privacy and security poli-
cies, regulations, ethical concerns and best practices to
handle and report related risks

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education — 9/13
Sustainable usage. Large language models have high com-
putational demands, which can result in high energy consump-
tion. Hence, energy-efficient hardware and shared (e.g., cloud)
infrastructure based on renewable energy are crucial for their
environmentally sustainable operation and scaling needed in
the context of education.
For model training and updates, only data that has been
collected and annotated in a regulatory compliant and ethical
way should be considered. Therefore, governance frameworks
that include policies, procedures, and controls to ensure such
appropriate use of such models are key to their successful
adoption.
Likewise, for the long-term trustworthy and responsible
use of the models, transparency, bias mitigation, and ongoing
monitoring are indispensable.
In summary, the mitigation strategy for this risk would
include:
•
Energy-efficient hardware and shared infrastructure
based on renewable energy as well as research on reduc-
ing the cost of training and maintenance (i.e., efficient
algorithms, representation, and storage)
• Collection, annotation, storage, and processing of data
in a regulatory compliant and ethical way
•
Transparency and explanation techniques to identify
and mitigate biases and prevent unfairness
•
Governance frameworks that include policies, proce-
dures, and controls to ensure the above points and the
appropriate use in education
Cost to verify information and maintain integrity. It is
important to verify the information provided by the model by
consulting external authoritative sources to ensure accuracy
and integrity. Additionally, there may be financial costs asso-
ciated with maintaining and updating the model to ensure it is
providing accurate up-to-date information.
A responsible mitigation strategy for this risk would con-
sider the following key aspects:
• Regularly updates of the model with new and accurate
information to ensure it is providing up-to-date and
accurate information
•
Use of multiple authoritative sources to verify the in-
formation provided by the model to ensure correctness
and integrity
•
Use of the model in conjunction with human expertise,
e.g., teachers or subject matter experts, who review and
validate the information provided by the model
•
Development of protocol and standards for fact-checking
and corroborating information provided by the model
•
Provide clear and transparent information on the model’s
performance, what it is or is not capable of, and the con-
ditions under which it operates.
•
Training and resources for educators and learners on
how to use the model, interpret its results and evaluate
the information provided
•
Regular review and evaluation of the model with trans-
parent reporting on the model’s performance, i.e., what
it is or is not capable of and the identification of con-
ditions under which inaccuracies or other issues may
arise
Difficulty to distinguish between real knowledge and con-
vincingly written but unverified model output. The abil-
ity of large language models to generate human-like text can
make it difficult for students to distinguish between real knowl-
edge and unverified information. This can lead to students
accepting false or misleading information as true, without
questioning its validity.
To mitigate this risk, in addition to the above verification-
and integrity-related mitigation strategy, it is important to pro-
vide education on how information can be evaluated critically
and teach students exploration, investigation, verification, and
corroboration strategies.
Lack of adaptability. Large language models are not able to
adapt to the diverse needs of students and teachers, and may
not be able to provide the level of personalization required for
effective learning. This is a limitation of the current technol-
ogy, but it is conceivable that with more advanced models, the
adaptability will increase.
More specifically, a sensible mitigation strategy would be
comprised of:
•
Use of adaptive learning technologies to personalize the
output of the model to the needs of individual students
by using student data (e.g., about learning style, prior
knowledge, and performance, etc.)
• Customization of the language model’s output to align
with the teaching style and curriculum (by using data
provided by the teacher)
•
Use of multi-modal learning and teaching approaches,
which combine text, audio, video, and experimentation
to provide a more engaging and personalized experience
for students and teachers
•
Use of hybrid approaches, which combine the strengths
of both human teachers and language models to gener-
ate targeted and personalized learning materials (based
on feedback, guidance, and support provided by the
teachers)
•
Regular review of the model and continual improve-
ment for curriculum-related uses cases to ensure ade-
quate and accurate functioning for education purposes
•
Research and development to create more advanced
models that can better adapt to the diverse needs of
students and teachers

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education — 10/13
5. Further Issues Related to User
Interfaces and Fair Access
Appropriate user interfaces. For the integration of large
language models into educational workflows, further research
on Human-Computer Interaction and User Interface Design is
necessary.
In this work, we have discussed several potential use cases
for learners of different age – from children to adults. While
creating such AI-based assistants, we should take into account
the degree of psychological maturity, fine motor skills, and
technical abilities of the potential users. Thus, the user in-
terface should be appropriate for the task, but may also have
varying degrees of human imitation – for instance, for chil-
dren it might be better to hide machinery artifacts in generated
text and use gamified interaction and learning approaches
as much as possible so as to enable a smooth and engaging
interaction with such technologies, whereas for older learn-
ers the machine-based content could be exploited to promote
problem-solving, critical thinking and fact-checking abilities.
In general, the design of user interfaces for AI-based as-
sistance and learning tools should promote the development
of 21st century learning and problem-solving skills [
66
], espe-
cially, critical thinking, creativity, communication, and collab-
oration, for which further evidence-based research is needed.
In this context, a crucial aspect is the appropriate age- and
background-related integration of AI-based assistance to max-
imize its benefits and minimize any potential drawbacks.
Multilingualism and fair access. While the majority of
the research in large language models is done for the En-
glish language, there is still a gap of research in this field
for other languages. This can potentially make education for
English-speaking users easier and more efficient than for other
users, causing unfair access to such education technologies
for non-English speaking users. Despite the efforts of various
research communities to address multilingualism fairness for
AI technologies, there is still much room for improvement.
Lastly, the unfairness related to financial means for access-
ing, training and maintaining large language models may need
to be regulated by governmental organisations with the aim
to provide equity-oriented means to all educational entities
interested in using these modern technologies. Without fair
access, this AI technology may seriously widen the education
gap like no other technology before it.
We therefore conclude with UNESCO’s call to ensure that
AI does not widen the technological and educational divides
within and between countries, and recommended important
strategies for the use of AI in a responsible and fair way to
reduce this existing gap instead. According to the UNESCO
education 2030 Agenda [
67
]: “UNESCO’s mandate calls in-
herently for a human-centred approach to AI. It aims to shift
the conversation to include AI’s role in addressing current
inequalities regarding access to knowledge, research and the
diversity of cultural expressions and to ensure AI does not
widen the technological divides within and between countries.
The promise of “AI for all” must be that everyone can take ad-
vantage of the technological revolution under way and access
its fruits, notably in terms of innovation and knowledge.”
6. Concluding Remarks
The use of large language models in education is a promising
area of research that offers many opportunities to enhance the
learning experience for students and support the work of teach-
ers. However, to unleash their full potential for education, it is
crucial to approach the use of these models with caution and
to critically evaluate their limitations and potential biases. In-
tegrating large language models into education must therefore
meet stringent privacy, security, and - for sustainable scal-
ing - environmental, regulatory and ethical requirements, and
must be done in conjunction with ongoing human monitoring,
guidance, and critical thinking.
While this position paper reflects the optimism of the au-
thors about the opportunities of large language models as a
transformative technology in education, it also underscores
the need for further research to explore best practices for inte-
grating large language models into education and to mitigate
the risks identified.
We believe that despite many difficulties and challenges,
the discussed risks are manageable and should be addressed to
provide trustworthy and fair access to large language models
for education. Towards this goal, the mitigation strategies
proposed in this position paper could serve as a starting point.
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