The Contribution of Artificial Intelligence in the Domain of

Education

Zongcheng Gu

Shanghai Soong Ching Ling School, Shanghai, China

Keywords: Education, Natural Language Processing, Deep Learning, Intelligent Tutoring System, Reinforcement

Learning, Artificial Intelligence.

Abstract: This paper explores the current roles of Artificial Intelligence (AI) in education, focusing on two key areas:

Natural Language Processing (NLP) and Deep Learning (DL). NLP is enabling advancements in language

assessment, automated essay grading, intelligent tutoring systems (ITSs), and personalized feedback tools.

Through the use of transformer models, NLP is making strides in educational settings, including supporting

non-English-speaking students and offering real-time analytics to enhance teacher-student interactions. Deep

learning, with its multi-layered neural networks, is furthering personalized learning through performance

prediction, classroom behavior monitoring, and reinforcement learning algorithms. These technologies help

create adaptive learning paths that fulfill individual student needs. Nonetheless, this paper also discusses

limitations and challenges regarding AI’s presence in education, such as data bias, the complexity of AI

models, ethical concerns regarding surveillance, and issues of accessibility persist. While AI tools promise

greater collaboration between educators and technology, their growing role raises concerns about over-

reliance, privacy, and fairness in educational outcomes. Looking forward, this paper exhibits future

possibilities of AI, and suggested its integration into education should be evolved continuously, with a focus

on personalized learning, enhanced explainability, and equitable access, while ensuring that human-centered

design principles guide the progression and implementation of AI systems in education.

1 INTRODUCTION

In recent years, the field of education has undergone

nonnegligible reformation, largely driven by the

exploration of sufficient help from artificial

intelligence (AI) technologies regarding teaching

people. AI, with its ability to analyze enormous

amounts of data and provide real-time feedback, has

begun to revolutionize the way students learn and

educators teach. Among the various subfields of AI,

Natural Language Processing (NLP) and Deep

Learning (DL) have emerged as key technologies that

are reshaping educational practices. NLP, which

focuses on enabling machines to understand and

interpret human language, plays a crucial role in

automating tasks such as essay grading, real-time

feedback, and personalized learning. On the other

hand, deep learning, a subset of machine learning that

uses neural networks to analyze and predict complex

patterns, has paved the way for intelligent tutoring

https://orcid.org/0009-0009-0024-6063

systems (ITS), student performance prediction, and

personalized learning paths.

As educational systems increasingly adopt AI-

driven tools, the integration of NLP and DL is

becoming more widespread. NLP facilitates a wide

range of applications, from automating essay grading

to creating interactive educational chatbots. Deep

learning, on the other hand, drives the personalization

of learning by enabling systems to analyze student

performance and adjust learning paths accordingly.

The integration of these technologies not only

enhances the efficiency and effectiveness of

educational tools but also brings a level of

personalization and scalability previously

unimaginable in traditional classroom settings.

For instance, NLP techniques have been applied

to assess student writing, provide personalized

feedback, and even assist teachers grading students

work. Intelligent tutoring systems, powered by deep

learning, are being deployed to offer customized

Gu, Z.

The Contribution of Artiﬁcial Intelligence in the Domain of Education.

DOI: 10.5220/0013677100004670

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Data Science and Engineering (ICDSE 2025), pages 5-10

ISBN: 978-989-758-765-8

learning experiences that adapt to the needs of

individual students. The growing interest in these AI

technologies in education reflects their potential to

improve learning outcomes, enhance student

engagement, and alleviate the burdens on teachers.

However, despite the promise these technologies hold,

there remain significant challenges, including

concerns over data privacy, algorithmic bias, and the

ethical implications of AI’s growing role in education.

This paper aims to provide a comprehensive

review of the applications of AI, particularly NLP and

deep learning, in the field of education. The review

covers the evolution of these technologies, their

current applications, and the challenges associated

with their integration into educational practices.

Specifically, this paper examines how NLP is used in

language assessment, feedback provision, and

learning support, and how deep learning models are

applied to create personalized learning experiences,

predict student performance, and monitor classroom

behavior.

The review synthesizes recent research,

highlighting the ways in which NLP and deep

learning are transforming education. Key applications

discussed include automated essay grading systems,

intelligent tutoring systems, recommender systems,

educational chatbots, and sentiment analysis tools for

assessing student engagement and performance.

Through a detailed analysis of these systems, this

paper explores both the opportunities and limitations

posed by AI in educational contexts.

Moreover, the paper examines the ethical

implications of AI use in education, particularly

regarding issues of data privacy, fairness, and

transparency. As AI systems become increasingly

integrated into educational environments, the

question of how to balance technological innovation

with ethical responsibility becomes ever more critical.

This review also identifies key areas for future

research, emphasizing the need for further

exploration into improving the accuracy of AI models,

addressing biases in algorithms, and ensuring that AI

tools are designed in ways that support, rather than

replace, human educators.

By providing a thorough examination of AI

technologies in education, this paper aims to

contribute to the ongoing conversation about the role

of AI in educational practices, offering both a critical

perspective on its limitations and a hopeful outlook

on its future potential.

2 AI APPLICATIONS IN

EDUCATION

This section will discuss the utilization of AI in the

current education system from two parts through

analyzing past papers.

2.1 Natural Language Processing

Natural Language Processing (NLP) plays a pivotal

role in the development of AI tools for education. As

one of the most impactful subfields of AI, NLP

enables machines to understand, interpret, and

generate human language. This ability is leveraged

across various educational applications, from

automated essay grading to real-time feedback

through intelligent tutoring systems (ITSs),

enhancing both learning and teaching experiences. In

this section, this review explores the core NLP

techniques and their applications in education,

focusing on key systems that support language

assessment, writing instruction, and interactive

learning environments.

Several recent studies highlight the diverse

applications of NLP in educational settings. One of

the uses of NLP in education is its ability to provide

personalized support for both learners and educators.

One of the recent paper explores how did people used

three transformer-based models, mT5, BanglaT5, and

mBART50, to solve Bengali mathematical word

problems, advancing education for a low-resource

language (Jashim Era et al., 2025). This work

exemplifies how NLP models can be adapted to serve

educational needs in diverse linguistic contexts,

especially for languages with fewer resources. The

use of NLP in such a context allows for automated

problem-solving and the generation of educational

content in a native language, breaking down barriers

for students in non-English speaking regions.

Another critical application of NLP in education

is in creating intelligent tutoring systems and

feedback tools. One paper discusses the potential for

AI to assist teachers by providing real-time analytics

of student performance and offering personalized

feedback based on NLP algorithms (Luckin &

Holmes, 2016). Through real-time monitoring of

student responses, NLP-powered systems can assess

written texts, identify areas of improvement, and

generate targeted suggestions. The role of NLP in

providing instant feedback is invaluable in

educational settings where timely interventions can

significantly improve learning outcomes. Another

example of NLP in intelligent tutoring systems (ITSs)

offers great promise in adapting learning experiences

ICDSE 2025 - The International Conference on Data Science and Engineering

to individual student needs. Beck et al. emphasize that

ITSs are designed to track student performance, using

student models that represent individual learning

paths. These systems adjust teaching strategies based

on the pedagogical module, providing personalized

instruction tailored to the student's progress (Beck et

al., 1996). An example of this in practice is RECIPE,

a writing platform that integrates ChatGPT to help

students improve their writing. The platform,

feedback and align instructional strategies with

students' learning objectives, demonstrating the

powerful synergy between AI and human-centered

educational tools (Kim et al., 2024).

Furthermore, NLP techniques can enhance

classroom communication and instructional strategies

(Carey et al., 2010). NLP can help educators better

understand students' needs, overcome language

barriers, and adjust their teaching methods

accordingly. NLP tools can even analyze student

language patterns to detect learning struggles, making

it easier for teachers to provide more personalized

support. An application of NLP that is described to be

useful in detecting and adjusting is used in automated

essay grading (AEG). Maliha et al. introduce a

Collaborative Deep Learning Model (CDLN) for

AEG, which not only evaluates the grammatical

structure of essays but also assesses the overall

coherence and idea development (Maliha & Pramanik,

2024). This model has demonstrated an impressive

accuracy of 85.50%, surpassing previous state-of-the-

art systems. The ability to evaluate essays on multiple

levels—both structure and content—makes AEG

systems more nuanced and effective, offering

personalized feedback to students (Maliha &

Pramanik, 2024). A paper from Litman provides a

detailed overview of how NLP methods are used to

assess student proficiency in language skills, from

syntactic analysis to semantic evaluation. NLP

models are employed to detect errors in writing and

speech, providing formative feedback to help students

refine their language use. This is particularly useful in

language learning environments, where consistent

assessment and personalized feedback are crucial for

student improvement (Litman, 2016).

In addition to these assessment applications, NLP

is also crucial in chatbot-based learning environments.

Khensous et al. discuss the use of educational

chatbots that assist both students and teachers by

offering personalized learning experiences

(Khensous et al., 2023). Chatbots act as virtual tutors,

providing real-time answers to students' questions

and offering feedback on exercises. They reduce the

teacher’s workload by automating repetitive tasks,

while also supporting students in language learning,

where personalized tutoring is key.

NLP has undoubtedly transformed education by

enabling real-time feedback, enhancing assessments,

and creating more engaging, personalized learning

experiences. The research suggests that NLP-based

systems can automate routine tasks such as grading

and provide personalized feedback, which is crucial

for maintaining engagement in large classes or online

learning platforms (MOOCs). However, the effective

implementation of NLP systems still requires careful

attention to data quality, biases in algorithmic

decisions, and the ethics of AI in education (Holmes

& Miao, 2023). As NLP continues to evolve, future

research must focus on improving accuracy,

addressing the misuse of AI in academic settings (e.g.,

cheating), and ensuring human-centered design

principles that prioritize the teacher-student

relationship over automation (Kim et al., 2024;

Litman, 2016).

2.2 Deep Learning

Deep learning is a subset of machine learning and has

significant applications in education. By utilizing

multi-layered neural networks, deep learning models

can process complex data, learn from it, and make

predictions or classifications with high accuracy. In

the context of education, deep learning has been

applied to student performance prediction, image and

language recognition for monitoring student’s

behavior, and personalized learning paths based on

individual student needs. This section explores these

applications and the role of deep learning in

personalized education.

One promising application of deep learning is

student performance prediction. Khensous et al.

highlights how Recommender Systems (RS) can

leverage deep learning algorithms to predict student

performance based on data from their academic

history and learning behavior (Khensous et al., 2023).

These systems are designed to recommend

appropriate learning materials, identify at-risk

students, and suggest actions to improve academic

performance. Such predictive tools have the potential

to tailor educational interventions to individual

students, ensuring that learning resources are matched

to their needs (Beck et al., 1996) .

Deep learning is also making strides in classroom

behavior monitoring. Recent developments in image

recognition and video analysis use deep learning to

track student engagement, identify patterns of

behavior, and even assess classroom dynamics in real

time. For instance, facial recognition systems can

The Contribution of Artiﬁcial Intelligence in the Domain of Education

gauge students' emotional states, providing valuable

insights into their engagement levels. These

technologies can help teachers adjust their teaching

strategies or identify students who might require

additional support (Luckin & Holmes, 2016) . While

such tools are still in the early stages, they hold

promise for creating real-time, data-driven feedback

systems for teachers, enabling them to provide

personalized interventions based on students' non-

verbal cues. On the other hand, the researchers utilize

deep learning algorithms to distinguish between

human-written and AI-generated content (Najjar et al,

2025). This study highlights the growing role of deep

learning in maintaining academic integrity in

educational settings, a concern that is becoming more

prominent as generative AI tools are used by students

to complete assignments. Deep learning’s ability to

analyze large volumes of data and identify subtle

patterns is instrumental in the detection of AI-

generated content and ensures that academic

institutions maintain rigorous standards.

Reinforcement Learning (RL), another branch of

deep learning, is being increasingly used to design

personalized learning paths for students. RL

algorithms adapt to individual learning styles and

adjust the learning process to maximize educational

outcomes. For example, the adaptive learning

systems powered by RL can guide students through

problem sets at a pace that suits their learning speed,

presenting more challenging tasks as they improve

while offering easier ones when they struggle. This

dynamic adjustment ensures that every student

follows a learning path tailored to their unique needs

(Maliha & Pramanik, 2024). For another instance,

students in computing education are adopting deep

learning-based models such as ChatGPT for problem-

solving (Hou et al., 2024) . This shift from traditional

help-seeking behavior (peer support, class forums,

etc.) to AI-driven help reflects how deep learning

systems, like large language models, are integrated

into student learning as key tools for answering

questions, generating ideas, and providing

explanations. As students become more reliant on

reinforcement learning models for educational

support, these systems are poised to become

foundational resources in learning environments,

especially for more complex subjects like computer

science.

The applications of deep learning in education

point to a future where personalized learning is the

norm. Deep learning models can process vast

amounts of educational data, making it possible to

create learning environments that are highly

responsive to each student’s progress. However, the

challenge lies in data privacy, the ethical implications

of using AI for prediction and surveillance, and the

need for teacher involvement to interpret and act on

the insights provided by AI systems (Kim et al.,

2024) . As these systems become more sophisticated,

educators must balance the benefits of automation

with the need for human judgment and interaction in

the learning process.

3 EXISTING LIMITATIONS AND

FUTURE OUTLOOK

This section will introduce the existing limitations

when utilizing AI for education and discuss the future

potential of AI.

3.1 Existing Limitations

While AI applications in education, particularly those

involving NLP and deep learning, hold great promise,

several challenges remain. The first significant

limitation is the data bias inherent in many AI systems.

For example, deep learning algorithms rely heavily

on large datasets, and if these datasets are not diverse

or representative of all students, the systems may

inadvertently reinforce existing biases, leading to

inaccurate predictions or unfair grading (Khensous et

al., 2023). Jashim Era et al. also further highlight the

importance of training AI models on diverse datasets,

but it also underscores the challenge of ensuring that

AI models are not inadvertently biased against

minority languages or non-English speakers (Jashim

Era et al., 2025).

The second limitation is the complexity of AI

models can sometimes make them opaque, leading to

difficulties in understanding how decisions are made.

This lack of transparency raises concerns about the

accountability of AI in education (Litman, 2016). As

seen by the work of Najjar et al., while AI models

such as XGBoost and Random Forest demonstrate

strong performance in distinguishing between

human-written and AI-generated content, these

models often operate as black boxes, making it

difficult to understand how certain conclusions are

reached (Najjar et al, 2025). This lack of

interpretability is a significant concern for educators

and students who need to understand how AI

assessments or feedback are generated. The ability to

explain AI decisions, particularly in educational

contexts, is crucial for maintaining trust in these

technologies.

ICDSE 2025 - The International Conference on Data Science and Engineering

The third limitation is the ethical concerns

surrounding the use of AI in education. The

increasing use of surveillance technologies, for

example facial recognition and classroom monitoring

tools, raises questions about student privacy and the

potential for misuse of data. AI tools must be

designed with ethical principles in mind, ensuring

that student data is handled responsibly and that AI is

used to support, rather than replace, human educators

(Holmes & Miao, 2023) .

The fourth limitation is that While AI systems like

ChatGPT and other generative models are becoming

essential resources for students, there is a concern that

over-reliance on these tools could undermine critical

thinking and problem-solving skills. While students

increasingly rely on AI tools for assistance, their

engagement with traditional resources like peer

support and class forums is decreasing (Hou et al.,

2024). This shift could lead to students becoming

passive learners, relying too heavily on AI for

answers without developing the necessary skills to

engage critically with material or collaborate

effectively with peers.

The fifth limitation focuses on accessibility and

equity. The widespread use of AI in education also

raises issues of access and equity. Not all students

have the same level of access to AI-powered tools,

which can create disparities in educational outcomes

(Najjar et al, 2025). Students from low-income

backgrounds or regions with limited internet access

may find it difficult to leverage AI resources,

exacerbating the digital divide.

3.2 Future Outlook

The future of AI in education looks incredibly

promising, with advancements in technology

expected to address many of the current limitations.

One of the most exciting developments is the

increasing integration of AI-driven personalized

learning systems. As noted by Holmes et al., AI can

enable educators to cater to the individual needs of

students, providing real-time feedback and

customized learning experiences that adapt to each

student’s strengths and weaknesses (Luckin &

Holmes, 2016). This will likely generate greater

collaboration between AI systems and human

educators, with AI playing a supportive role in

assisting teachers with several tasks, including

grading, providing feedback, and tailoring instruction

to individual students (Beck et al., 1996). In the future,

AI could help create highly personalized educational

journeys, ensuring that every student receives the

support they need to succeed.

In terms of AI tools for content generation and

assessment, deep learning models will continue to

evolve and improve, becoming more accurate in their

assessments and more capable of offering

personalized learning experiences. Hou et al. suggest

that AI tools will become increasingly integrated into

students’ study habits, becoming indispensable for

their academic success (Hou et al., 2024). Over time,

these tools could even replace traditional learning

materials or act as complementary resources to

facilitate independent learning.

Moreover, the focus on explainability and

transparency in AI models is expected to improve. As

Najjar et al. highlights, the integration of Explainable

AI (XAI) is crucial for ensuring that educators and

students can understand and trust AI-driven

assessments (Najjar et al, 2025) .

As AI becomes more embedded in education, it

will also raise new ethical and regulatory challenges.

In particular, educators and policymakers will need to

address issues related to academic integrity, the

potential for AI to perpetuate biases, and ensuring that

AI is used responsibly and equitably. Ethical

frameworks for AI in education must continue to

evolve, with an emphasis on human-centered design

that prioritizes the needs of students and educators

over purely technological advancements (Kim et al.,

2024).

4 CONCLUSIONS

In conclusion, AI applications in education,

particularly those involving NLP and deep learning,

hold immense potential to revolutionize the learning

and teaching experience. These technologies enable

personalized learning, real-time feedback, and

efficient assessment methods, improving the

educational process for both students and educators.

NLP tools have demonstrated success in automating

tasks like grading and providing individualized

support, particularly in language learning and essay

evaluation. Similarly, deep learning models are

improving student performance prediction and

behavior monitoring, offering insights that can guide

instructional adjustments. However, despite these

advancements, challenges such as data bias, model

opacity, ethical concerns, and accessibility remain.

The need for diverse and representative datasets,

transparent AI systems, and responsible data handling

is critical to ensure the fair and effective use of AI in

education. Furthermore, the over-reliance on AI tools

could undermine essential skills like critical thinking

and collaboration. Looking forward, AI is expected to

The Contribution of Artiﬁcial Intelligence in the Domain of Education

continue evolving, offering more personalized and

adaptable learning experiences while addressing

current limitations. Nonetheless, its integration into

education will require careful attention to ethical

considerations, human-centered design, and the

balance between technology and human input to

ensure equitable and responsible use.

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