Thesis/Outline

TYPE Opinion

PUBLISHED 17 May 2023

DOI 10.3389/feduc.2023.1166682

OPEN ACCESS

EDITED BY

Kamal Kant Hiran,

Symbiosis University of Applied Sciences, India

REVIEWED BY

Lina Kaminskiene,

Vytautas Magnus University, Lithuania

*CORRESPONDENCE

Kevin Fuchs

[email protected]

RECEIVED 15 February 2023

ACCEPTED 11 April 2023

PUBLISHED 17 May 2023

CITATION

Fuchs K (2023) Exploring the opportunities and

challenges of NLP models in higher education:

is Chat GPT a blessing or a curse?

Front. Educ. 8:1166682.

doi: 10.3389/feduc.2023.1166682

COPYRIGHT

© 2023 Fuchs. This is an open-access article

distributed under the terms of the Creative

Commons Attribution License (CC BY). The use,

distribution or reproduction in other forums is

permitted, provided the original author(s) and

the copyright owner(s) are credited and that

the original publication in this journal is cited, in

accordance with accepted academic practice.

No use, distribution or reproduction is

permitted which does not comply with these

terms.

Exploring the opportunities and challenges of NLP models in higher education: is Chat GPT a blessing or a curse?

Kevin Fuchs*

Faculty of Hospitality and Tourism, Prince of Songkla University, Phuket, Thailand

KEYWORDS

higher education, natural language processing, Chat GPT, technology, pedagogy

1. Introduction

The world has changed a lot in the past few decades, and it continues to change. Chat

GPT has created tremendous speculation among stakeholders in academia, not the least of

whom are researchers and teaching staff (Biswas, 2023). Chat GPT is a Natural Language

Processing (NLP) model developed by OpenAI that uses a large dataset to generate text

responses to student queries, feedback, and prompts (Gilson et al., 2023). It can simulate

conversations with students to provide feedback, answer questions, and provide support

(OpenAI, 2023). It has the potential to aid students in staying engaged with the course

material and feeling more connected to their learning experience. However, the rapid

implementation of these NLP models, like Chat GPT by OpenAI or Bard by Google, also

poses several challenges. In this article, I will discuss a range of challenges and opportunities

for higher education, as well as conclude with implications that (hopefully) expose gaps in

the literature, stimulate research ideas, and, finally, advance the discussion about NLP in

higher education.

2. Discussion

2.1. The emergence of NLP in academia

Natural Language Processing (NLP) models have been in development since the 1950s

(Jones, 1994) but it was not until the past decade that they gained significant attention

and advancement, particularly with the development of deep learning techniques and

large datasets (Kang et al., 2020). NLP models are rapidly becoming relevant to higher

education, as they have the potential to transform teaching and learning by enabling

personalized learning, on-demand support, and other innovative approaches (Odden

et al., 2021). In higher education, NLP models have significant relevance for supporting

student learning in multiple ways. These models can be employed to analyze and process

vast amounts of textual data, such as academic papers, textbooks, and other course

materials, to provide students with personalized recommendations for further study based

on their learning requirements and preferences. In addition, NLP models can be used

to develop chatbots and virtual assistants that offer on-demand support and guidance

to students, enabling them to access help and information as and when they need it.

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Chat GPT by OpenAI and Bard (Google’s response to Chat

GPT) are examples of NLP models that have the potential to

transform higher education. These generative language models, i.e.,

Chat GPT and Google Bard, can generate human-like responses to

open-ended prompts, such as questions, statements, or prompts

related to academic material. The recent release and increasing

popularity (in early 2023) of Chat GPT and Google Bard made

its use particularly relevant for supporting student learning in a

range of contexts, such as language learning, writing, research, and

general academic inquiry. Therefore, the use of NLP models in

higher education expands beyond the aforementioned examples,

with new applications being developed to aid students in their

academic pursuits.

2.2. Opportunities for higher education

Personalized learning is an approach to education that

aims to tailor instruction to the unique needs, interests,

and abilities of individual learners. NLP models can facilitate

personalized learning by analyzing students’ language patterns,

feedback, and performance to create customized learning plans

that include content, activities, and assessments tailored to the

individual student’s needs. Personalized learning can be particularly

effective in improving student outcomes. Research has shown

that personalized learning can improve academic achievement,

engagement, and self-efficacy (Wu, 2017). When students are

provided with content relevant to their interests and abilities, they

are more likely to engage with the material and develop a deeper

understanding of the subject matter. NLP models can provide

students with personalized learning experiences by generating

content tailored specifically to their individual learning needs.

For example, when a student submits a response to a

question, the model can analyze the response and provide feedback

customized to the student’s understanding of the material. This

feedback can help the student identify areas where they might

need additional support or where they have demonstrated mastery

of the material. Furthermore, the processing models can generate

customized learning plans for individual students based on their

performance and feedback. These plans may include additional

practice activities, assessments, or reading materials designed to

support the student’s learning goals. By providing students with

these customized learning plans, these models have the potential

to help students develop self-directed learning skills and take

ownership of their learning process.

Moreover, on-demand support is a crucial aspect of effective

learning, particularly for students who are working independently

or in online learning environments. The NLP models can provide

on-demand support by offering real-time assistance to students

struggling with a particular concept or problem. The benefits

of on-demand support are numerous. It can help students

overcome learning obstacles and enhance their understanding of

the material. In addition, on-demand support can help build

students’ confidence and sense of self-efficacy by providing them

with the resources and assistance they need to succeed. These

models can offer on-demand support by generating responses to

student queries and feedback in real time.When a student submits a

question or response, the model can analyze the input and generate

a response tailored to the student’s needs.

This can be particularly helpful for students working

independently or in online learning environments where

they might not have immediate access to a teacher or tutor.

Furthermore, chatbots can offer support to students at any time

and from any location. Students can access the system from their

mobile devices, laptops, or desktop computers, enabling them

to receive assistance whenever they need it. This flexibility can

help accommodate students’ busy schedules and provide them

with the support they need to succeed. Additionally, NLP models

can provide students with on-demand support in a variety of

formats, including text-based chat, audio, or video. This can cater

to students’ individual learning preferences and provide them with

the type of support that is most effective for them.

2.3. Challenges for higher education

Although there is a wide range of opportunities for NLP

models, like Chat GPT and Google Bard, there are also several

challenges (or ethical concerns) that should be addressed. The first

challenge is the issue of accuracy. The accuracy of the system

depends heavily on the quality, diversity, and complexity of the

training data, as well as the quality of the input data provided

by students. In previous research, Fuchs (2022) alluded to the

importance of competence development in higher education and

discussed the need for students to acquire higher-order thinking

skills (e.g., critical thinking or problem-solving). The system might

struggle to understand the nuances and complexities of human

language, leading to misunderstandings and incorrect responses.

Moreover, a potential source of inaccuracies is related to the quality

and diversity of the training data used to develop the NLP model.

If the training data is not adequately diverse or is of low quality,

the system might learn incorrect or incomplete patterns, leading

to inaccurate responses. The accuracy of NP models might be

impacted by the complexity of the input data, particularly when

it comes to idiomatic expressions or other forms of linguistic

subtlety. Additionally, the model’s accuracy might be impacted by

the quality of the input data provided by students. If students do not

provide clear, concise, and relevant input, the systemmight struggle

to generate an accurate response. This is particularly challenging

in cases in which students are not sure what information they

need or cannot articulate their queries in a way that the system

easily understands.

Another significant challenge that students might face when

using NLP models in higher education is the potential risk of over-

reliance on technology, which could undermine the development of

important critical thinking skills (while critical thinking has been

singled out as an exemplary skill, the list of skills is countless and

multiple higher-order thinking skills could be further discussed in

the context of chatbots). While these models can offer valuable

support and personalized learning experiences, students must be

careful to not rely too heavily on the system at the expense of

developing their own analytical and critical thinking skills. Over-

reliance on systems such as Chat GPT and Google Bard could

lead to students becoming passive learners who simply accept the

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responses generated by the system without questioning or critically

evaluating the accuracy or relevance of the information provided.

This could lead to a failure to develop important critical thinking

skills, such as the ability to evaluate the quality and reliability

of sources, make informed judgments, and generate creative and

original ideas.

Moreover, over-reliance could reinforce existing biases and

perpetuate inequalities in education. For example, if the system

is trained on biased or incomplete data, it might generate

responses reflecting these biases, thereby leading to a reinforcement

of existing inequalities and a failure to challenge and disrupt

discriminatory practices in higher education. To address these

challenges, institutions must provide clear guidance to students

on how to use NLP models as a tool to support their

learning rather than as a replacement for critical thinking and

independent learning. Institutions must also ensure that students

are provided with opportunities to engage in active learning

experiences that encourage critical thinking, problem-solving, and

independent inquiry.

Another important challenge that should be mentioned is the

linguistic aspect of NLP, like Chat GPT and Google Bard. Emerging

evidence in the body of knowledge indicates that chatbots have

linguistic limitations (Wilkenfeld et al., 2022). For example, a

study by Coniam (2014) suggested that chatbots are generally

able to provide grammatically acceptable answers. However, at

the moment, Chat GPT lacks linguistic diversity and pragmatic

versatility (Chaves and Gerosa, 2022). Still, Wilkenfeld et al. (2022)

suggested that in some instances, chatbots can gradually converge

with people’s linguistic styles. While the development of artificial

intelligence and natural language processing models like Chat GPT

is just the beginning (Molnár and Szüts, 2018), it is not far-fetched

to hypothesize that over time the linguistic accuracy of NLPmodels

will improve and more closely mimic the writing style of humans

(including expressive writing styles as similarly alluded to by Park

et al., 2021).

3. Conclusion

In this article, I discussed the challenges and opportunities

regarding natural language processing (NLP) models like Chat

GPT and Google Bard and how they will transform teaching and

learning in higher education. The article highlights the potential

benefits of using NLP models for personalized learning and on-

demand support, such as providing customized learning plans,

generating feedback and support, and offering resources to students

whenever and wherever they need them. However, the article

also acknowledges the challenges that NLP models may bring,

including the potential loss of human interaction, bias, and ethical

implications. To address the highlighted challenges, universities

should ensure that NLP models are used as a supplement to, and

not as a replacement for, human interaction. Institutions should

also develop guidelines and ethical frameworks for the use of NLP

models, ensuring that student privacy is protected and that bias

is minimized.

Additionally, universities should involve students in the

development and implementation of NLP models to address their

unique needs and preferences. Finally, universities should invest

in training their faculty to use and adapt to the technology, as

well as provide resources and support for students to use the

models effectively. In summary, universities should consider the

opportunities and challenges of using NLP models in higher

education while ensuring that they are used ethically and with a

focus on enhancing student learning rather than replacing human

interaction. Overall, NLP models are a powerful tool for improving

the quality of education by providing students with personalized

learning experiences and automating administrative tasks, while

institutions must tackle the previously mentioned challenges to

safeguard high-quality education for their students.

Author contributions

The author confirms being the sole contributor of this work and

has approved it for publication.

Conflict of interest

The author declares that the research was conducted in the

absence of any commercial or financial relationships that could be

construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the

authors and do not necessarily represent those of their affiliated

organizations, or those of the publisher, the editors and the

reviewers. Any product that may be evaluated in this article, or

claim that may be made by its manufacturer, is not guaranteed or

endorsed by the publisher.

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