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Empowering Education with LLMs – the Next-Gen Interface and Content Generation

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# On the application of Large Language Models for language teaching and
assessment technology

Andrew Caines  ALTA Institute & Computer Laboratory, University of Cambridge
Luca Benedetto  Shiva Taslimipoor  Christopher Davis  Yuan Gao  Øistein
Andersen  Zheng Yuan  King’s College London  Mark Elliott  Cambridge
University Press & Assessment  Russell Moore  Christopher Bryant  Writer, Inc.
Marek Rei  Imperial College London  Helen Yannakoudakis  Andrew Mullooly
Diane Nicholls  English Language iTutoring (ELiT)  Paula Buttery

(2023)

###### Abstract

The recent release of very large language models such as PaLM and GPT-4 has
made an unprecedented impact in the popular media and public consciousness,
giving rise to a mixture of excitement and fear as to their capabilities and
potential uses, and shining a light on natural language processing research
which had not previously received so much attention. The developments offer
great promise for education technology, and in this paper we look specifically
at the potential for incorporating large language models in AI-driven language
teaching and assessment systems. We consider several research areas – content
creation and calibration, assessment and feedback – and also discuss the risks
and ethical considerations surrounding generative AI in education technology
for language learners. Overall we find that larger language models offer
improvements over previous models in text generation, opening up routes toward
content generation which had not previously been plausible. For text
generation they must be prompted carefully and their outputs may need to be
reshaped before they are ready for use. For automated grading and grammatical
error correction, tasks whose progress is checked on well-known benchmarks,
early investigations indicate that large language models on their own do not
improve on state-of-the-art results according to standard evaluation metrics.
For grading it appears that linguistic features established in the literature
should still be used for best performance, and for error correction it may be
that the models can offer alternative feedback styles which are not measured
sensitively with existing methods. In all cases, there is work to be done to
experiment with the inclusion of large language models in education technology
for language learners, in order to properly understand and report on their
capacities and limitations, and to ensure that foreseeable risks such as
misinformation and harmful bias are mitigated.

###### keywords:

large language models education technology natural language processing
question difficulty estimation text generation automated assessment
grammatical error correction responsible AI

##  1 Introduction

The training of _large language models_ (LLMs) – also known as _pre-trained
language models_ or _foundation models_ – has had a transformative effect on
the fields of natural language processing (NLP) and artificial intelligence
(AI) more broadly. LLMs are ‘large’ because they are neural networks made up
of billions or trillions of parameters. The networks are Transformers [1]
trained on huge swathes of text from the World Wide Web, using language
modelling objectives such as predicting omitted (or, ‘masked’) words and
sentence pairs [2], or predicting the next token in a sequence [3].
Furthermore, in the few-shot learning paradigm, LLMs can be directed towards
new tasks without large quantities of task-specific data [4, 5], the
collection of which tends to be time-consuming and costly. Overall, LLMs also
offer great potential for educational applications. One previous paper has
already provided an overview of some of the possible applications of LLMs to
educational technology as a whole [6], across subjects. Our distinct
contribution is to focus on the language learning and assessment domain
specifically. In this paper, we describe some of the uses for LLMs in the
context of language learning, discuss the state of the art or work in
progress, and consider practical, societal and ethical implications.

We set out a number of uses for LLMs in the language learning domain, relating
to content creation and calibration, automated assessment of written texts,
and personalised feedback. In each case the general principles of the approach
are well established thanks to previous work with pre-existing LLMs such as
BERT [2] – language models with millions of parameters have existed for
several years already. We look at the opportunities presented by the recent
and rapid steps taken by OpenAI in releasing new variants from the ‘generative
pre-training’ (GPT) model series, along with some newly published pre-prints
relating to LLMs and the language learning research field. We refer to some
LLM-driven language learning applications already in use, and outline the
variety of LLMs available besides GPT. It is a fast evolving research field,
one being driven by industry developments. We perceive some possible risks in
this research trajectory, which include but are not limited to the absence of
proper safeguards on education technology, the lack of public understanding as
to how LLMs are trained and how they can confidently assert incorrect
information, and the harm to the advancement of education technology as a
whole if it is considered ‘solved’ by investors and research councils – not to
mention the ethical issues that are already well known, such as data
protection [7], examination malpractice [8, 9]111Note that the text-matching
tool Turnitin [10], which is commonly used to detect plagiarism, has developed
a module to detect the use of AI in essays: https://www.turnitin.com/blog/the-
launch-of-turnitins-ai-writing-detector-and-the-road-ahead, environmental
impact [11, 12], and internet addiction [13, 14, 15], among others.

##  2 Large Language Models & Language Learning EdTech

At the time of writing, one of the most prominent LLMs is OpenAI’s GPT-4 [16]
released in March 2023 after six months of pre-launch work improving safety
and fact-checking, also for product development with selected partners
including the education technology (EdTech) firms
Duolingo222https://blog.duolingo.com/duolingo-max/ and Khan
Academy333https://blog.khanacademy.org/harnessing-ai-so-that-all-students-
benefit-a-nonprofit-approach-for-equal-access/. This built on the prior
success and notoriety of GPT-3, released in June 2020, along with its related
chatbot application, ChatGPT444https://openai.com/blog/chatgpt. Recently, we
have seen more focus on the effects and implications of using chatbots for
creating interactions with language learners [17, 18].

Of most relevance here is the partnership between OpenAI and Duolingo, the
language learning application developer, which resulted in the subscription
service Duolingo Max [19]. Duolingo Max presents two new features: ‘Role Play’
and ‘Explain My Answer’. The former involves some limited conversation towards
a goal such as ordering food, which starts with a pre-scripted prompt but then
proceeds over several open-ended chat turns between user and chatbot. The
latter is an option for additional feedback on grammatical points, involving a
limited dialogue with pre-specified responses for the user to guide the
conversation (e.g. “Yes, I’m all set”, “Can I see an example?”, “No, please
elaborate”). Another limitation is that the service is only currently
available in selected countries and for a few languages.

Nevertheless, this development points towards further opportunities in AI-
driven education technology for language learning, as discussed below. It
should be noted that there are many alternatives to the GPT models, including
the ‘text-to-text Transformer’ (T5) [20], PaLM (Parallel Language Model) [21]
and LaMDA (Language Model for Dialogue Applications) [22] by Google; LLaMA
[23] and Open Pre-trained Transformers (OPT) [24] by Meta AI; and DeepMind’s
Gopher [25]. At least a few of these models are ‘multilingual’, having been
trained on corpora from multiple languages, albeit with a strong bias towards
English555e.g. See the distribution of languages in the training data for
GPT-3:
https://github.com/openai/gpt-3/blob/master/dataset_statistics/languages_by_document_count.csv.
In addition there are models which have been trained on bilingual data,
notably Chinese–English [26] and
Russian–English666https://github.com/yandex/YaLM-100B. Alongside LLM
developments by large technology companies, we also note the various open-
source efforts to train on known datasets (e.g. EleutherAI’s GPT-X [27] and
_The Pile_ [28]), or as massive research collaborations (e.g. BLOOM: the
BigScience Large Open-science Open-access Multilingual Language Model [29]),
or to democratise LLMs for wider use in web applications involving natural
language interfaces (e.g.
langchain777https://python.langchain.com/en/latest/index.html, Cohere
AI888https://cohere.com/, and Transformers [30]). There are also open-source
alternatives to ChatGPT, such as Open Assistant999https://open-assistant.io/
and StableVicuna101010https://stability.ai/blog/stablevicuna-open-source-rlhf-
chatbot. Finally we highlight efforts to transparently evaluate LLMs in
comprehensive and varied ways, for instance in the HELM project (Holistic
Evaluation of Language Models) [31].

##  3 Content Creation: Creating Assessment Items and Teaching Materials

Pre-trained transformer models are being explored to generate exam texts and
items for educational purposes like in the Duolingo English Test [32], or in
Google’s quantitative reasoning application, Minerva [33]. Variations of GPT
models are best known to the wider public as being able to create common forms
of language assessment tests. However, there are other successful pre-trained
LLMs such as variations of BERT [2], BART [34], or T5 [20] which are popular
among NLP scientists. Since these models are trained with different target
task objectives, they should be prompted differently for various kinds of text
generation. None of them has been trained with a storytelling objective to
generate fluent long texts as GPT* models have. Nevertheless, since the target
tasks are better defined, evaluation of the performance of these models is
more thorough and explainable. BERT-based models have shown impressive results
in filling the gaps in sequences of text. BART achieves state-of-the-art
results in generating parallel sentences as in machine translation or
grammatical error correction [34, 35]. T5 modelled $24$ tasks as text-to-text
generation and proved very successful in question answering and text
summarisation [20].

These models are widely used for narrower tasks like question generation [36],
for reading comprehension exercises [37], or prompt generation for writing and
speaking. For example, Felice _et al._ [38] use BERT and ELECTRA [39] to
predict the position of the gaps for designing high-quality cloze tests for
language learners, while various pre-trained language models are used for
generating distractors [40, 41, 42]. In addition LLMs have been put to use for
the purpose of text simplification, which is relevant for language learners in
the context of reading comprehension exercises and adapting texts
automatically to an appropriate level. Notably, a GPT-3 based solution by the
UniHD team was the winning entry for the English track of the TSAR-2022 Shared
Task on Multilingual Lexical Simplification [43, 44].

### Datasets and Evaluation

With the emergence of LLMs, large-scale datasets are required for evaluation.
Text generation methods are evaluated using text-similarity based metrics like
BLEU [45], ROUGE [46], METEOR [47], and more recently BERTScore [48], or
learned evaluation metrics [49, 50] which assess the correlation between
generated texts (e.g. the question) and the ones originally written by human
experts. Automatic evaluations require top-quality and expert-designed
datasets. Available datasets for language learning exams for NLP research
include RACE [51], SCDE [52] and CLOTH [53]. However, there are smaller-scale
datasets such as CEPOC [54] for Cloze test creation, and the Teacher-Student
Chatroom Corpus [55], which can be used as test sets to evaluate zero-shot or
few-shot learning models. Evaluation approaches for open-ended text generation
are still far from being ideal. Human-centric evaluations involve ranking the
generated texts based on different factors, such as fluency and coherence of
the generated texts, or its relevance to the context document and the answer
(where available) [56]. In this new era of increasingly large language models,
human evaluation is more difficult and time-consuming, leading researchers to
design comparison datasets that contain human-labelled comparisons between
outputs of different systems [57].

### Human-in-the-loop content generation

As an exploratory study, we have worked with publicly available GPT-3 models
to generate open-ended texts and evaluate their suitability as a basis for
low-stakes, self-study language learning exercises. Having a human-in-the-loop
policy in mind, the prompts are engineered by a human expert, with post-
generation text refinement and question authoring also carried out by experts.
Such an approach can be seen as helping mitigate against various known risks
associated with the output of LLMs (e.g., hallucinations, offensive content,
stereotyping, etc).

All definable model parameters (Temperature, Frequency Penalty, Presence
Penalty and Max Length) are kept to fixed levels throughout to limit the
number of variables across the dataset. Input prompts containing target genre
and key content points are designed by the human expert in order to provide a
basis for possible testing foci at the target level. The key content points
also help generate similar enough output texts from a single (or slightly
modified) input prompt, to allow for collation of the best elements from
multiple output versions.

For this research, the generated texts are intended to support single B2 CEFR
level111111The Common European Framework of Reference for Languages (CEFR)
organises language proficiency in six levels, A1 to C2, which represent Basic
User, Independent User and Proficient User. multiple choice reading
comprehension questions with 3 answer options. The generated texts are
reviewed by the human expert and given an ‘accept’ or ‘reject’ status based on
their appropriateness for the target proficiency level and relevance to the
content points. Accepted texts are added to a content pool, also containing
fully human-authored texts. Another group of human experts (question writers)
approach the accepted texts as they would any other content. For openness and
transparency, question writers are informed in advance that the pool contains
AI-generated content, but not which texts are AI-generated, and which have
been written by human authors. Question writers select and edit the texts,
writing one 3-option multiple choice question per text. The annotations of
this dataset, including the accept/reject status and the measures of quality
of the generated texts assessed by question writers (based on the amounts of
edits made on the texts) can be used to train models which can automatically
assess the generated texts in future. The dataset can also be used to train
reward functions for further fine-tuning of the generative models.

ChatGPT has been trained using a combination of supervised fine-tuning and
reinforcement learning from human feedback (RLHF) [58, 59]. It uses
InstructGPT [57] and includes the steps: pre-training, fine-tuning, and reward
learning. The reward function used to fine-tune InstructGPT is trained using a
dataset of pairs of generated texts with a human-labelled judgement on which
text is better, with the objective to maximise the score difference between
the ‘winning’ and ‘losing’ texts. The purpose of collecting coarse human
labelling is to mitigate any mismatch between the true objective and the
preferences of human annotators, thus increasing inter-annotator agreement
[59]. Nevertheless, relying solely on general annotations, as in the case of
InstructGPT, results in a reward function that fails to shed light on the
quality of texts across various aspects, making it too broad to apply in
narrower tasks and fields. To address this limitation, we can take advantage
of the existing high-quality annotations available to us from skilled and
experienced professional human annotators. By exploiting their expertise, we
can train a more nuanced reward function that offers fine-grained evaluation
and provides interpretable scores, aligning more effectively with our specific
research goals. Finally, we can evaluate different methods of content
generation on our reading practice platform,
Read&Improve121212https://readandimprove.englishlanguageitutoring.com/ [60].
By collecting both implicit user feedback – which texts they engage with more
by spending longer reading them, clicking on definitions, completing the
tasks, _etc_ – and explicit user feedback (e.g. by asking them to rate texts
and express opinions) we can assess which LLM-driven systems are most
successful.

##  4 Calibrating Assessment Items and Teaching Materials

In addition to content creation, LLMs can potentially be leveraged for the
evaluation and calibration of existing learning content: test items and
teaching content. An example of this is question difficulty estimation (QDE)
from text, which has received increasing research interest in recent years
[61, 62]. QDE from text offers a way to overcome the limitations of
traditional approaches such as manual calibration and statistical analysis
from pre-testing. These traditional approaches are either subjective or
introduce a long delay between item creation and deployment due to the
complexities of pre-testing on sizeable and representative populations.

QDE from text is a regression task, where the model is asked to provide, given
the text of the question, a numerical estimation of its difficulty on a given
scale. It can be either a supervised or unsupervised task, depending on
whether a dataset of already calibrated exam questions is available, and LLMs
have been used in both scenarios. Regarding supervised estimation, LLMs that
leverage transfer learning – specifically, BERT [2] and DistilBERT [63] – are
the current state of the art [64, 65] and have been shown to outperform other
approaches using traditional NLP-derived features [66]. There has been less
work on unsupervised estimation but, even in this scenario, LLMs have been
shown to be helpful for estimating question difficulty from text [67].

All the models proposed in previous research require some kind of transfer
learning starting from the publicly available pre-trained models, which might
be expensive and not feasible in all scenarios. Bigger LLMs, such as the
aforementioned GPT models, could be used for zero-shot or few-shot difficulty
estimation from text, which is yet to be explored. As an example, ChatGPT can
be asked to rank given questions by difficulty, and it can also provide an
indication of the specific difficulty level (e.g. Easy, Medium, Hard).
Crucially, the difficulty of a pool of questions depends on the specific
student population that is assessed with them, and it is difficult to provide
the LLM with all the information required to describe the specific pool of
learners that will be assessed with the items. The model seems to be – at
least partially – capable of distinguishing between different CEFR levels,
since the same question can be assigned different levels depending on whether
the model is asked to consider learners of level A1 or C1. However, extensive
experiments should be carried out to better evaluate this, as the model
sometimes performs counterintuitive estimations: in our preliminary
experiments, for instance, ChatGPT sometimes estimated a question to be more
difficult for C1-level learners (i.e., “Proficient”) than A1-level learners
(i.e., “Basic”).

##  5 Automated Assessment of Language Learners

Automated assessment has long been a prominent task in educational
applications research: for instance assessing learner English speech [68, 69,
70, 71] and writing [72, 73, 74, 75, 76]. Here we focus on writing and the
task of ‘automated essay scoring’ (AES). Whereas previous systems have
involved feature engineering – typically centred around informative sequences
of characters, words, part-of-speech tags, as well as phrase structures from a
parser and automatically detected errors [73, 77] – more recent research
systems have involved neural models for assessment [74, 75, 76]. These models
tend to be carefully crafted and evaluated, since language assessment can be a
task with major consequences for the learner, including education and career
prospects. Therefore any involvement of LLMs in assessment systems must be
approached cautiously and its impact measured on existing benchmarks.
Deployment of LLM-based assessment models should be restricted to human-in-
the-loop low-stakes contexts first, including practice applications [77, 60]
or placement tests such as
Linguaskill131313https://www.cambridgeenglish.org/exams-and-
tests/linguaskill/.

The idea of using ChatGPT for assessing students’ answers was put forward by
Jeon & Lee [78]. Further practical steps were taken by Mizumoto & Eguchi [79]
who experimented with GPT-3.5 for AES on 12,000 essays from the ETS Corpus of
Non-Native Written English (TOEFL11) [80], compared the scores to benchmark
levels on a 0-9 scale, and concluded that a GPT-only model only achieves weak
agreement with the reference scores ($.388$ quadratic weighted kappa). The
authors furthermore compare the GPT scorer with several models involving
various combinations of 45 linguistic features – related to lexical diversity,
lexical sophistication, syntactic complexity and dependency, and cohesion –
and observe that although the GPT baseline is outperformed by the linguistic
features on their own, the best results are obtained by combining the two
approaches ($.605$ QWK). This is a finding similar to previous research, as
the previous state-of-the-art performance was obtained by combining BERT-style
neural models and feature-based models [76, 81].

One potential use for LLMs regarding assessment that, to the best of our
knowledge, has not been thoroughly explored is for explaining assessment
predictions. _Explainable AI_ is an emerging research topic, a regulatory
prospect [82], and a challenge for NLP models dependent on ‘black box’ neural
networks. One possibility is to adopt the ‘chain-of-thought’ prompting style
[83] – as opposed to zero-shot or few-shot prompting [5] – to elicit
explanations about assessment decisions from LLMs. Exactly how to engineer a
series of prompts for the LLM in the chain-of-thought style is a matter for
investigation, but for instance they could be similar to the following (albeit
longer to elicit better explanations):

        A class of students has been given the essay prompt: <essay_prompt>.
        This student essay -- <example_text>
        -- was given a score of <example_score>.
        Explanation: the use of language and grammatical resource are advanced
        but there is a spelling error in the first sentence and a grammatical
        error in the final sentence.
        This student essay -- <target_text>
        -- has been given a score of <predicted_score>.
        Please give an explanation why the text was given this score.

The aim of such an approach would be to obtain explanations specific to the
essay, pinpointing relevant sections from the text if possible, and grounded
in marking criteria for the learner’s target level so that the explanation is
relevant and useful. In common with other tasks described in this paper,
further research with LLMs and proper evaluation on existing benchmarks and by
human experts is needed before we can definitively conclude that this is a
research avenue worth exploring.

Finally we note that there are concerns around LLMs being used in fraudulent
ways by learners, but that plagiarism concerns are long-standing in computer-
based exam settings. If proctoring software is set up to prevent text import
from elsewhere (e.g. disabling copy-and-paste keyboard shortcuts) or to detect
bursty text insertion through keystroke logging, then this is one defence
against exam malpractice from LLM text generation or any other online source.
In this way, LLMs are an extension of a threat we are already familiar with.
Furthermore, automatic detection of LLM-generated text is the subject of the
AuTexTification (Automated Text Identification), part of IberLEF 2023 (the 5th
Workshop on Iberian Languages Evaluation Forum) co-located with the SEPLN
Conference this year141414https://sites.google.com/view/autextification. It
appears that the level of performance from submitted systems has been high,
outdoing a logistic regression baseline in many cases, with system
descriptions to be presented in September. It may be that such systems can be
employed as an additional line of defence against exam malpractice involving
LLM-generated text.

##  6 Providing Feedback to Language Learners

As a precursor to providing automatic lexico-syntactic feedback to language
learners, one requirement is to first carry out the NLP task of _grammatical
error detection_ (GED) or _grammatical error correction_ (GEC). These tasks
have a rich pedigree involving various benchmark corpora [84, 85, 86, 87],
research papers [88, 89, 90, 91, 92] and shared tasks [93, 94, 95] – all of
which enable us to establish that the current state-of-the-art approach to GED
and GEC tends to involve supervised fine-tuning of neural network language
models using carefully annotated training data [96]. The recent emergence of
LLMs, however, offers the prospect of developing GED or GEC models which are
largely unsupervised other than some examples for few-shot learning151515Note
that LLM training is often described as ‘self-supervised’ due to the human-
authored training data, but for the purpose of GED/GEC, we say ‘unsupervised’
because in this context no task-specific training data is required..

The challenge ahead, in common with the application of LLMs to other tasks
described in this paper, is to properly benchmark LLM-based models for GED and
GEC on existing corpora, so that their performance can be compared to previous
models. Some preliminary work has been done towards this aim, as described in
a recent survey of GEC [96]. For instance, Wu _et al._ [97] and Coyne &
Sakaguchi [98] present preliminary results applying LLMs to GEC. The former
compares ChatGPT to Grammarly and GECToR [99], a previous state-of-the-art GEC
system, and the latter compares GPT-3.5161616Specifically, they use text-
davinci-003. to two other GEC systems [100, 101]. Both approaches find the
GPT* models perform worse than existing systems when measured using automatic
evaluation techniques on existing benchmark corpora (namely CoNLL-2014 [94],
JFLEG [102], BEA-2019 [95]). The authors ascribe this to the model’s tendency
to over-correct learner text; by inserting additional text or re-structuring
phrases, the corrected text moves further from the original text and is
penalised by the automatic scorers. However, both works carry out human
evaluation to rate the output from each system and find a preference for the
GPT* output because the corrected sentences tend to be more fluent. At the
same time, they found instances of _under_ -correction in the human-generated
reference sentence: in other words GPT* models catching and correcting errors
which were not corrected by the expert annotators.

While both approaches are preliminary and human evaluation tentative – based
on only small samples of 100 sentences at a time from each test set – overly
fluent corrections present a challenge for automatic evaluation methods as
they are much more open-ended than minimal edits targeting grammatical errors
rather than stylistic choices. Furthermore, while fluent corrections may at
times be preferred by human evaluators, they may not aid language learners if
they drift too far from the original text. Existing annotation guides for
error correction state that edits should be as minimal as possible so that the
learner can be helped to express what they are trying to say, rather than told
how to express it differently: that is, how to amend an error rather than
avoid it [103]. The issue is not a new one [104] but remains a matter for
further investigation under the new conditions presented by more capable LLMs.

Another potential use of LLMs in this area is providing automatically-
generated feedback comments to learners to explain linguistic concepts,
grammatical points or semantic nuance. Indeed there was a recent shared task
on _feedback comment generation_ [105] where, when presented with an erroneous
sentence such as, “He agrees the opinion”, the task was to produce a comment
such as: The verb agree is an intransitive verb and cannot take direct
objects: add the appropriate preposition171717https://fcg.sharedtask.org/.
Participants in the shared task were able to outperform the baseline system
(‘an encoder-decoder with a copy mechanism based on a pointer generator
network’181818https://github.com/k-hanawa/fcg_genchal2022_baseline) through
careful feature extraction from parsers and GEC models, combined with
prominent LLMs at the time such as T5 or GPT-Neo [106] (e.g. Babakov _et al._
[107] achieved second place in the shared task; developers of the first-placed
entry have not published a system description to the best of our knowledge).
It remains to be seen whether current LLMs can be tuned towards even better
performance on this task: it may be that the pre-processing of texts to obtain
additional linguistic information and the incorporation of pre-defined
templates will continue to be vital for accurate and sensible feedback comment
generation, even with ever-larger LLMs involved [108]. These are methods we
can trial through A/B testing of different feedback models on our essay-
writing practice platform, Write&Improve191919https://writeandimprove.com/
[73, 77].

Other applications of LLMs for language learning feedback include chatbot
interaction to explain linguistic concepts – akin to the ‘Explain My Answer’
feature in Duolingo Max, but also going beyond this with dialogue which is
adaptive to the learner level [18] – word suggestion, paraphrasing and
translation to aid learners with essay writing, and document-level feedback
on, for instance, inter-sentence coherence markers, co-reference and anaphoric
reference, maintaining tense and aspect consistently, argumentation structure,
task completion and more. Key desiderata are that the feedback should be
accurate, based on evidence, personalised, inoffensive and preferably linked
to teaching materials so that the learner may continue to benefit from EdTech
applications for language.

##  7 Risks & Ethical Considerations

We advocate for a cautious approach to the incorporation of LLMs in EdTech for
language learning, in which the training process, performance and limitations,
and pathway to delivery are well documented and the risks of misapplication of
such technology are understood. There are general concerns about AI for NLP
and education which are recorded in the literature and continue to be
relevant, perhaps more so, as LLMs come to the fore. Firstly there is a bias
towards English, and specific genres of English, due to a combination of
commercial pressures, training data availability, and data sourcing from the
World Wide Web: even though several models have been trained in multilingual
ways, the general trend with LLMs has exacerbated this pre-existing bias [109,
110, 111]. As LLMs grow, so does their climate impact: an issue which
interacts with societal and infrastructure complexities but which we should
nevertheless bear in mind and attempt to mitigate [11, 12]. In addition, LLMs
are known to exhibit certain biases [112] – both _representational_ (language
use around demographic groups) [113, 114] and _allocational_ (how a system
distributes resources or opportunities) [115, 113] – which need to be debiased
or otherwise controlled [116, 117].

Suresh & Guttag identified various sources of harm in the ‘machine learning
life cycle’ [115]: historical bias, representation bias, measurement bias,
learning bias, aggregation bias, evaluation bias, deployment bias. They note
that effects cascade downstream and cycle around ML systems. They provide some
mitigation strategies and reference previous work in this area [118, 119].
Kasneci _et al._ [6] also point to copyright issues with output from LLMs
which are largely unresolved, as well as concerns about pedagogical and
learning effects: namely that both teachers and students, “may rely too
heavily on the model”, and that it may be difficult to, “distinguish model-
generated from student-generated answers” [120, 121, 122]. In addition they
raise data privacy and security issues which require firmer regulation and
auditing of EdTech firms, the problem of false information issued by LLMs, and
of designing appropriate application interfaces which are both engaging and
beneficial to end-users. It is worth noting that NLP researchers have made
some attempts at using LLMs to assess the trustworthiness of generated texts,
which could go some way towards mitigating the false information problem [123,
124, 125].

Regarding AIED and language learning, LLMs present specific risks relating to
generated outputs which may be inaccurate, confusing, offensive, and so on –
risks which are present in human teachers too, but made no less harmful as a
result. For this reason the most successful systems may be human-machine
hybrids, with humans in-the-loop or similar, where LLMs are viewed as
assistive technology for human experts rather than replacements for them –
performing the more mundane and mechanical tasks while experts provide the
inputs characteristic of human interaction [126]. Another way that humans can
monitor LLM outputs is through evaluation, and feedback mechanisms for systems
in production, so that problematic outputs may be flagged.

We can also look at standards for ‘responsible AI’ published by technology
firms and research institutes [127, 128,
129]202020https://huggingface.co/blog/ethical-charter-multimodal. For example,
Duolingo [130] sets out its approach to responsible AI under Validity &
Reliability, Fairness, Privacy & Security, Accountability & Transparency – all
of which have been touched on in this paper. Regarding the last attribute in
particular – Transparency – it is apparent from recent media stories that more
can be done in this area in terms of educating the general public about how
LLMs are trained, how trustworthy they may or may not be, and how best to
interact with them. This is a general problem but one which nonetheless
presents a challenge for EdTech applications.

##  8 Conclusion

In this paper, we have explored the opportunities for language-learning EdTech
offered by ‘generative AI’ through LLMs. We conclude that preliminary
indications are promising, but that the best systems may still require human
intervention and/or the inclusion of well-established linguistic features. It
may well be that LLMs can enhance language-learning EdTech, if we can
establish the following through further empirical work:

  1. 1.

that models enhanced by LLMs perform better than existing models on
established benchmarks, or on alternative evaluation metrics which need to be
defined in order to properly probe LLM capabilities for language teaching and
assessment [131] – moreover that performance is _sufficiently_ better to
justify the additional costs in computing and environmental terms;

  2. 2.

that LLM-enhanced technology is of benefit to language learners, whether that
is measured through engagement, enjoyment, learning outcomes or some
combination of the three;

  3. 3.

that LLM-enhanced technology does not disadvantage relevant groups (learners,
teachers, writers and editors of materials, examiners) whether through bias,
misinformation, or adversely affecting student progress – instead, the
technology should be assistive to all groups in some regard.

Finally, we note that LLMs should not be over-hyped as an AI revolution, but
rather as an evolutionary step in neural network models – the inevitable
result of the inexorable growth in network size since the Transformer was
first applied to language tasks in 2017 [1]. LLMs represent a milestone on an
evolutionary path which has been unfolding for many years and thus is well
documented in open access publications and open source code repositories. If
we maintain this tradition – by close inspection of proprietary models, or
opting to use models trained in open ways – it will be of benefit both to
future researchers and scientific development, but also users of AI
applications who require some transparency regarding the technology. Harmful
bias and other risks remain an ongoing challenge for developers of AI systems,
and LLMs deployed in language learning EdTech may only exacerbate these.
Therefore, proper mitigations should be put in place to address the issues
which have been identified in this paper and elsewhere.

Nevertheless, LLMs present a great opportunity to continue improving EdTech
for language learning, including novel ways to generate content, provide
feedback, and deal with other linguistic features which hitherto have not been
commonly attempted: for instance, chatting in open-ended ways at the level of
the learner [18], providing document-level assessment and feedback [132],
handling code-switching or ‘plurilingual’ learning [133].

###### Acknowledgements.

This work was supported by Cambridge University Press & Assessment. We thank
Dr Nick Saville and Professor Michael McCarthy for their support. We are
grateful to the anonymous reviewers for their helpful comments.

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